From 73e994ab20281881c5a166bdd2268f7e819ba4cd Mon Sep 17 00:00:00 2001 From: Alon Burg Date: Sun, 4 Feb 2024 15:17:44 +0200 Subject: [PATCH] Deploy website - based on 2cd57c9531a8adfbe59c0c8783ccffb2dd01067d --- 2024/02/04/multi-pass-inference/index.html | 16 ++++++++-------- 404.html | 14 +++++++------- CNAME | 1 - archive/index.html | 14 +++++++------- assets/js/30fe5c1f.f1931845.js | 1 - assets/js/5f003b45.b1fcd16d.js | 1 - assets/js/5f003b45.ed854424.js | 1 + assets/js/79c75afb.1bf5ca1c.js | 1 - assets/js/79c75afb.2b85edb8.js | 1 + assets/js/90e7b1d0.a46f9018.js | 1 + ...935f2afb.776c12b3.js => 935f2afb.a4b930c0.js} | 2 +- ...9fda4f55.0b967803.js => 9fda4f55.a8782398.js} | 2 +- assets/js/a3f88606.bdbab22a.js | 1 - assets/js/c85f55fc.e57c5951.js | 1 + assets/js/main.56209553.js | 2 ++ ....LICENSE.txt => main.56209553.js.LICENSE.txt} | 0 assets/js/main.9df19a84.js | 2 -- assets/js/runtime~main.205d82e0.js | 1 - assets/js/runtime~main.e6f69ee0.js | 1 + atom.xml | 14 +++++++------- docs/changes/index.html | 14 +++++++------- index.html | 16 ++++++++-------- rss.xml | 10 +++++----- sitemap.xml | 2 +- 24 files changed, 59 insertions(+), 60 deletions(-) delete mode 100644 CNAME delete mode 100644 assets/js/30fe5c1f.f1931845.js delete mode 100644 assets/js/5f003b45.b1fcd16d.js create mode 100644 assets/js/5f003b45.ed854424.js delete mode 100644 assets/js/79c75afb.1bf5ca1c.js create mode 100644 assets/js/79c75afb.2b85edb8.js create mode 100644 assets/js/90e7b1d0.a46f9018.js rename assets/js/{935f2afb.776c12b3.js => 935f2afb.a4b930c0.js} (62%) rename assets/js/{9fda4f55.0b967803.js => 9fda4f55.a8782398.js} (54%) delete mode 100644 assets/js/a3f88606.bdbab22a.js create mode 100644 assets/js/c85f55fc.e57c5951.js create mode 100644 assets/js/main.56209553.js rename assets/js/{main.9df19a84.js.LICENSE.txt => main.56209553.js.LICENSE.txt} (100%) delete mode 100644 assets/js/main.9df19a84.js delete mode 100644 assets/js/runtime~main.205d82e0.js create mode 100644 assets/js/runtime~main.e6f69ee0.js diff --git a/2024/02/04/multi-pass-inference/index.html b/2024/02/04/multi-pass-inference/index.html index 29a802e..7ac44f7 100644 --- a/2024/02/04/multi-pass-inference/index.html +++ b/2024/02/04/multi-pass-inference/index.html @@ -3,16 +3,16 @@ -How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation | Astria articles - - - +How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation | Astria articles + + +
-
Skip to main content

How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation

· 9 min read

Welcome to Astria.ai.

In our first blog post, we’ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We’ll show you how to structure high-quality prompts to generate visuals of professional quality.

What Is Multi-Pass Inference?​

First, let’s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image.

Here's how multi-pass inference enhances control over the background of an image:

1. Iterative Refinement​

In a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome.

2. Choice over base model​

Multi-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models.

3. Increased Precision and Detailing​

With multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model's output is more dependent on the initial prompt and less on a multi-step method.

4. Balancing Foreground and Background​

Multi-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively.

As an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach.

(without multi-pass)

alt_text

(with multi-pass)

alt_text

As you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former.

How Multi-Pass Inference Can Benefit Your Business​

The enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence.

For e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales.

Moreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels.

Lastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market.

How Astria.ai makes Multi-pass inferencing easy​

Multi-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer's expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how.

Let’s first understand how a developer’s expertise is needed and then we’ll show how Astria.ai makes this process easier.

If one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks.

Moreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images.

Astria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints.

Overall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers.

Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing​

Step 1: Training​

First, create a fine-tune of your subject.

alt_text

Select the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type.

We used the following images of a male model obtained from a royalty free collection (Pixabay):

alt_text

Once the tune is ready, we can begin to prompt. Click on your tune.

alt_text

Step 2 Inference​

Let’s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand.

(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.
BREAK photorealistic and highly detailed
BREAK ohwx man wearing hiking clothes and shoes <lora:960310:1.0>
  • The first line contains the base prompt to generate the background and the overall composition.
  • The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition.
  • The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - <lora:960310:1.0> - is added to load the fine-tuned model of our subject.
Negative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck

The negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt.

We can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that’ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img.

For example, let’s take this pose as our input image:

alt_text

Also, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output.

Let’s look at the result of our first prompt:

alt_text

As you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image.

Step 3: Examples​

Prompt 2:

a man at the finish line of a race on an olympic track
BREAK sharp details
BREAK ohwx man wearing running clothes and shoes, jubilant expression on his face&lt;lora:960310:1.0>

Negative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,

alt_text

Prompt 3:

full body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit
BREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65
BREAK ohwx man doing push-ups, intense look on his face <lora:960310:1.0>

Negative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,

alt_text

Prompt 4: -(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic

BREAK
BREAK ohwx man wearing casual sports wear&lt;lora:960310:1.0>
Negative: hat, cartoon, ugly

alt_text

Final Note​

The above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives.

Whether you're looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals.

- - +
Skip to main content

How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation

· 9 min read

Welcome to Astria.ai.

In our first blog post, we’ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We’ll show you how to structure high-quality prompts to generate visuals of professional quality.

What Is Multi-Pass Inference?​

First, let’s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image.

Here's how multi-pass inference enhances control over the background of an image:

1. Iterative Refinement​

In a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome.

2. Choice over base model​

Multi-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models.

3. Increased Precision and Detailing​

With multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model's output is more dependent on the initial prompt and less on a multi-step method.

4. Balancing Foreground and Background​

Multi-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively.

As an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach.

(without multi-pass)

alt_text

(with multi-pass)

alt_text

As you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former.

How Multi-Pass Inference Can Benefit Your Business​

The enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence.

For e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales.

Moreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels.

Lastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market.

How Astria.ai makes Multi-pass inferencing easy​

Multi-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer's expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how.

Let’s first understand how a developer’s expertise is needed and then we’ll show how Astria.ai makes this process easier.

If one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks.

Moreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images.

Astria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints.

Overall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers.

Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing​

Step 1: Training​

First, create a fine-tune of your subject.

alt_text

Select the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type.

We used the following images of a male model obtained from a royalty free collection (Pixabay):

alt_text

Once the tune is ready, we can begin to prompt. Click on your tune.

alt_text

Step 2 Inference​

Let’s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand.

(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.
BREAK photorealistic and highly detailed
BREAK ohwx man wearing hiking clothes and shoes <lora:960310:1.0>
  • The first line contains the base prompt to generate the background and the overall composition.
  • The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition.
  • The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - <lora:960310:1.0> - is added to load the fine-tuned model of our subject.
Negative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck

The negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt.

We can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that’ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img.

For example, let’s take this pose as our input image:

alt_text

Also, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output.

Let’s look at the result of our first prompt:

alt_text

As you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image.

Step 3: Examples​

Prompt 2:

a man at the finish line of a race on an olympic track
BREAK sharp details
BREAK ohwx man wearing running clothes and shoes, jubilant expression on his face&lt;lora:960310:1.0>

Negative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,

alt_text

Prompt 3:

full body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit
BREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65
BREAK ohwx man doing push-ups, intense look on his face <lora:960310:1.0>

Negative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,

alt_text

Prompt 4: +(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic

BREAK
BREAK ohwx man wearing casual sports wear&lt;lora:960310:1.0>
Negative: hat, cartoon, ugly

alt_text

Final Note​

The above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives.

Whether you're looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals.

+ + \ No newline at end of file diff --git a/404.html b/404.html index e2b4e90..7ebd7d7 100644 --- a/404.html +++ b/404.html @@ -3,15 +3,15 @@ -Page Not Found | Astria articles - - - +Page Not Found | Astria articles + + +
-
Skip to main content

Page Not Found

We could not find what you were looking for.

Please contact the owner of the site that linked you to the original URL and let them know their link is broken.

- - +
Skip to main content

Page Not Found

We could not find what you were looking for.

Please contact the owner of the site that linked you to the original URL and let them know their link is broken.

+ + \ No newline at end of file diff --git a/CNAME b/CNAME deleted file mode 100644 index 4ba5d64..0000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -articles.astria.ai \ No newline at end of file diff --git a/archive/index.html b/archive/index.html index caa4f68..0614a27 100644 --- a/archive/index.html +++ b/archive/index.html @@ -3,15 +3,15 @@ -Archive | Astria articles - - - +Archive | Astria articles + + +
-
Skip to main content

Archive

Archive

2024

- - +
Skip to main content

Archive

Archive

2024

+ + \ No newline at end of file diff --git a/assets/js/30fe5c1f.f1931845.js b/assets/js/30fe5c1f.f1931845.js deleted file mode 100644 index 40837e8..0000000 --- a/assets/js/30fe5c1f.f1931845.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[603],{5374:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"/2024/02/04/multi-pass-inference","metadata":{"permalink":"/astria-articles/2024/02/04/multi-pass-inference","source":"@site/blog/2024-02-04-multi-pass-inference.md","title":"How to Use Astria.ai\'s Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation","description":"How to Generate Striking Images with Astria.ai\'s Multi-Pass Inference","date":"2024-02-04T00:00:00.000Z","formattedDate":"February 4, 2024","tags":[],"readingTime":8.61,"hasTruncateMarker":false,"authors":[],"frontMatter":{"title":"How to Use Astria.ai\'s Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation","description":"How to Generate Striking Images with Astria.ai\'s Multi-Pass Inference"}},"content":"Welcome to Astria.ai.\\r\\n\\r\\nIn our first blog post, we\u2019ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We\u2019ll show you how to structure high-quality prompts to generate visuals of professional quality.\\r\\n\\r\\n\\r\\n## What Is Multi-Pass Inference?\\r\\n\\r\\nFirst, let\u2019s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image.\\r\\n\\r\\nHere\'s how multi-pass inference enhances control over the background of an image:\\r\\n\\r\\n\\r\\n### 1. Iterative Refinement\\r\\n\\r\\nIn a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome.\\r\\n\\r\\n\\r\\n### 2. Choice over base model\\r\\n\\r\\nMulti-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models.\\r\\n\\r\\n\\r\\n### 3. Increased Precision and Detailing\\r\\n\\r\\nWith multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model\'s output is more dependent on the initial prompt and less on a multi-step method.\\r\\n\\r\\n\\r\\n### 4. Balancing Foreground and Background\\r\\n\\r\\nMulti-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively.\\r\\n\\r\\nAs an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach.\\r\\n\\r\\n
(without multi-pass)
\\r\\n\\r\\n![alt_text](images/image7.png)\\r\\n\\r\\n
(with multi-pass)
\\r\\n\\r\\n![alt_text](images/image6.png)\\r\\n\\r\\n\\r\\nAs you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former.\\r\\n\\r\\n\\r\\n## How Multi-Pass Inference Can Benefit Your Business\\r\\n\\r\\nThe enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence.\\r\\n\\r\\nFor e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales.\\r\\n\\r\\nMoreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels.\\r\\n\\r\\nLastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market.\\r\\n\\r\\n\\r\\n## How Astria.ai makes Multi-pass inferencing easy\\r\\n\\r\\nMulti-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer\'s expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how.\\r\\n\\r\\nLet\u2019s first understand how a developer\u2019s expertise is needed and then we\u2019ll show how Astria.ai makes this process easier.\\r\\n\\r\\nIf one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks.\\r\\n\\r\\nMoreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images.\\r\\n\\r\\nAstria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints.\\r\\n\\r\\nOverall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers.\\r\\n\\r\\n\\r\\n## Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing\\r\\n\\r\\n### Step 1: Training\\r\\n\\r\\nFirst, create a fine-tune of your subject.\\r\\n\\r\\n![alt_text](images/image10.png)\\r\\n\\r\\n\\r\\nSelect the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type.\\r\\n\\r\\nWe used the following images of a male model obtained from a royalty free collection (Pixabay):\\r\\n\\r\\n\\r\\n![alt_text](images/image4.jpg)\\r\\n\\r\\n\\r\\nOnce the tune is ready, we can begin to prompt. Click on your tune.\\r\\n\\r\\n![alt_text](images\\\\image3.png)\\r\\n\\r\\n\\r\\n### Step 2 Inference\\r\\n\\r\\nLet\u2019s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand.\\r\\n\\r\\n\\r\\n```\\r\\n(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.\\r\\nBREAK photorealistic and highly detailed\\r\\nBREAK ohwx man wearing hiking clothes and shoes \\r\\n```\\r\\n\\r\\n* The first line contains the base prompt to generate the background and the overall composition.\\r\\n* The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition.\\r\\n* The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - <lora:960310:1.0> - is added to load the fine-tuned model of our subject.\\r\\n\\r\\n```\\r\\nNegative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck\\r\\n```\\r\\n\\r\\n\\r\\n\\r\\nThe negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt.\\r\\n\\r\\nWe can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that\u2019ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img.\\r\\n\\r\\nFor example, let\u2019s take this pose as our input image:\\r\\n\\r\\n\\r\\n![alt_text](images/image5.jpg)\\r\\n\\r\\n\\r\\nAlso, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output.\\r\\n\\r\\nLet\u2019s look at the result of our first prompt:\\r\\n\\r\\n\\r\\n![alt_text](images/image8.png)\\r\\n\\r\\n\\r\\nAs you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image.\\r\\n\\r\\n### Step 3: Examples\\r\\n\\r\\nPrompt 2:\\r\\n```\\r\\na man at the finish line of a race on an olympic track\\r\\nBREAK sharp details\\r\\nBREAK ohwx man wearing running clothes and shoes, jubilant expression on his face<lora:960310:1.0>\\r\\n\\r\\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\\r\\n```\\r\\n\\r\\n![alt_text](images/image9.png)\\r\\n\\r\\n\\r\\nPrompt 3:\\r\\n\\r\\n```\\r\\nfull body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit\\r\\nBREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65\\r\\nBREAK ohwx man doing push-ups, intense look on his face \\r\\n\\r\\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\\r\\n```\\r\\n\\r\\n![alt_text](images/image1.png)\\r\\n\\r\\n\\r\\nPrompt 4:\\r\\n(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic\\r\\n\\r\\n```\\r\\nBREAK\\r\\nBREAK ohwx man wearing casual sports wear<lora:960310:1.0>\\r\\nNegative: hat, cartoon, ugly\\r\\n```\\r\\n\\r\\n\\r\\n\\r\\n![alt_text](images/image2.png)\\r\\n\\r\\n\\r\\n\\r\\n## Final Note\\r\\n\\r\\nThe above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives.\\r\\n\\r\\nWhether you\'re looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals."}]}')}}]); \ No newline at end of file diff --git a/assets/js/5f003b45.b1fcd16d.js b/assets/js/5f003b45.b1fcd16d.js deleted file mode 100644 index 72d473a..0000000 --- a/assets/js/5f003b45.b1fcd16d.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[658],{8847:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>l,contentTitle:()=>o,default:()=>u,frontMatter:()=>s,metadata:()=>r,toc:()=>c});var i=a(7462),n=(a(7294),a(3905));const s={title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"},o="How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",r={permalink:"/astria-articles/2024/02/04/multi-pass-inference",source:"@site/blog/2024-02-04-multi-pass-inference.md",title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference",date:"2024-02-04T00:00:00.000Z",formattedDate:"February 4, 2024",tags:[],readingTime:8.61,hasTruncateMarker:!1,authors:[],frontMatter:{title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"}},l={authorsImageUrls:[]},c=[{value:"What Is Multi-Pass Inference?",id:"what-is-multi-pass-inference",level:2},{value:"1. Iterative Refinement",id:"1-iterative-refinement",level:3},{value:"2. Choice over base model",id:"2-choice-over-base-model",level:3},{value:"3. Increased Precision and Detailing",id:"3-increased-precision-and-detailing",level:3},{value:"4. Balancing Foreground and Background",id:"4-balancing-foreground-and-background",level:3},{value:"How Multi-Pass Inference Can Benefit Your Business",id:"how-multi-pass-inference-can-benefit-your-business",level:2},{value:"How Astria.ai makes Multi-pass inferencing easy",id:"how-astriaai-makes-multi-pass-inferencing-easy",level:2},{value:"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing",id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing",level:2},{value:"Step 1: Training",id:"step-1-training",level:3},{value:"Step 2 Inference",id:"step-2-inference",level:3},{value:"Step 3: Examples",id:"step-3-examples",level:3},{value:"Final Note",id:"final-note",level:2}],d={toc:c},p="wrapper";function u(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,i.Z)({},d,s,{components:t,mdxType:"MDXLayout"}),(0,n.kt)("p",null,"Welcome to Astria.ai."),(0,n.kt)("p",null,"In our first blog post, we\u2019ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We\u2019ll show you how to structure high-quality prompts to generate visuals of professional quality."),(0,n.kt)("h2",{id:"what-is-multi-pass-inference"},"What Is Multi-Pass Inference?"),(0,n.kt)("p",null,"First, let\u2019s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image."),(0,n.kt)("p",null,"Here's how multi-pass inference enhances control over the background of an image:"),(0,n.kt)("h3",{id:"1-iterative-refinement"},"1. Iterative Refinement"),(0,n.kt)("p",null,"In a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome."),(0,n.kt)("h3",{id:"2-choice-over-base-model"},"2. Choice over base model"),(0,n.kt)("p",null,"Multi-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models."),(0,n.kt)("h3",{id:"3-increased-precision-and-detailing"},"3. Increased Precision and Detailing"),(0,n.kt)("p",null,"With multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model's output is more dependent on the initial prompt and less on a multi-step method."),(0,n.kt)("h3",{id:"4-balancing-foreground-and-background"},"4. Balancing Foreground and Background"),(0,n.kt)("p",null,"Multi-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively."),(0,n.kt)("p",null,"As an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach."),(0,n.kt)("center",null," (without multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3415).Z,width:"1599",height:"1999"})),(0,n.kt)("center",null," (with multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3184).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"As you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former."),(0,n.kt)("h2",{id:"how-multi-pass-inference-can-benefit-your-business"},"How Multi-Pass Inference Can Benefit Your Business"),(0,n.kt)("p",null,"The enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence."),(0,n.kt)("p",null,"For e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales."),(0,n.kt)("p",null,"Moreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels."),(0,n.kt)("p",null,"Lastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market."),(0,n.kt)("h2",{id:"how-astriaai-makes-multi-pass-inferencing-easy"},"How Astria.ai makes Multi-pass inferencing easy"),(0,n.kt)("p",null,"Multi-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer's expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how."),(0,n.kt)("p",null,"Let\u2019s first understand how a developer\u2019s expertise is needed and then we\u2019ll show how Astria.ai makes this process easier."),(0,n.kt)("p",null,"If one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks."),(0,n.kt)("p",null,"Moreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images."),(0,n.kt)("p",null,"Astria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints."),(0,n.kt)("p",null,"Overall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers."),(0,n.kt)("h2",{id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing"},"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing"),(0,n.kt)("h3",{id:"step-1-training"},"Step 1: Training"),(0,n.kt)("p",null,"First, create a fine-tune of your subject."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(4828).Z,width:"959",height:"845"})),(0,n.kt)("p",null,"Select the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type."),(0,n.kt)("p",null,"We used the following images of a male model obtained from a royalty free collection (Pixabay):"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6876).Z,width:"1600",height:"1600"})),(0,n.kt)("p",null,"Once the tune is ready, we can begin to prompt. Click on your tune."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7034).Z,width:"1454",height:"846"})),(0,n.kt)("h3",{id:"step-2-inference"},"Step 2 Inference"),(0,n.kt)("p",null,"Let\u2019s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand."),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.\nBREAK photorealistic and highly detailed\nBREAK ohwx man wearing hiking clothes and shoes \n")),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},"The first line contains the base prompt to generate the background and the overall composition."),(0,n.kt)("li",{parentName:"ul"},"The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition."),(0,n.kt)("li",{parentName:"ul"},"The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - ","<","lora:960310:1.0> - is added to load the fine-tuned model of our subject.")),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"Negative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck\n")),(0,n.kt)("p",null,"The negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt."),(0,n.kt)("p",null,"We can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that\u2019ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img."),(0,n.kt)("p",null,"For example, let\u2019s take this pose as our input image:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(5052).Z,width:"1280",height:"853"})),(0,n.kt)("p",null,"Also, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output."),(0,n.kt)("p",null,"Let\u2019s look at the result of our first prompt:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6111).Z,width:"1600",height:"1097"})),(0,n.kt)("p",null,"As you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image."),(0,n.kt)("h3",{id:"step-3-examples"},"Step 3: Examples"),(0,n.kt)("p",null,"Prompt 2:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"a man at the finish line of a race on an olympic track\nBREAK sharp details\nBREAK ohwx man wearing running clothes and shoes, jubilant expression on his face<lora:960310:1.0>\n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6959).Z,width:"1536",height:"1920"})),(0,n.kt)("p",null,"Prompt 3:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"full body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit\nBREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65\nBREAK ohwx man doing push-ups, intense look on his face \n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(1601).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"Prompt 4:\n(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"BREAK\nBREAK ohwx man wearing casual sports wear<lora:960310:1.0>\nNegative: hat, cartoon, ugly\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7549).Z,width:"1280",height:"1600"})),(0,n.kt)("h2",{id:"final-note"},"Final Note"),(0,n.kt)("p",null,"The above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives."),(0,n.kt)("p",null,"Whether you're looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals."))}u.isMDXComponent=!0},1601:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image1-ed2246b12f83db790d137c93ec375a7d.png"},4828:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image10-6d93e8d9a2d4a97f2b8965d686d93489.png"},7549:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image2-ecaf6b1d065d3eb6c57c399392ee34e3.png"},7034:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image3-fe71a43fd1042a1c53ef41ccb4493c0e.png"},6876:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image4-3d68ca1fd77775a75949e0fdea948ede.jpg"},5052:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image5-8fb740755774cf33efb7b8cf6deb3c60.jpg"},3184:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image6-75d8f7c291788629a5e79224da37059c.png"},3415:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image7-93a3f8e6eb0be8b0e45d06ff58a44e71.png"},6111:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image8-530b7527b4bd99a570acb0179aafcb36.png"},6959:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image9-4c157fa32c1049e695850306078a6fe1.png"}}]); \ No newline at end of file diff --git a/assets/js/5f003b45.ed854424.js b/assets/js/5f003b45.ed854424.js new file mode 100644 index 0000000..d67b619 --- /dev/null +++ b/assets/js/5f003b45.ed854424.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[658],{8847:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>l,contentTitle:()=>o,default:()=>u,frontMatter:()=>s,metadata:()=>r,toc:()=>c});var i=a(7462),n=(a(7294),a(3905));const s={title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"},o="How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",r={permalink:"/articles/2024/02/04/multi-pass-inference",source:"@site/blog/2024-02-04-multi-pass-inference.md",title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference",date:"2024-02-04T00:00:00.000Z",formattedDate:"February 4, 2024",tags:[],readingTime:8.61,hasTruncateMarker:!1,authors:[],frontMatter:{title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"}},l={authorsImageUrls:[]},c=[{value:"What Is Multi-Pass Inference?",id:"what-is-multi-pass-inference",level:2},{value:"1. Iterative Refinement",id:"1-iterative-refinement",level:3},{value:"2. Choice over base model",id:"2-choice-over-base-model",level:3},{value:"3. Increased Precision and Detailing",id:"3-increased-precision-and-detailing",level:3},{value:"4. Balancing Foreground and Background",id:"4-balancing-foreground-and-background",level:3},{value:"How Multi-Pass Inference Can Benefit Your Business",id:"how-multi-pass-inference-can-benefit-your-business",level:2},{value:"How Astria.ai makes Multi-pass inferencing easy",id:"how-astriaai-makes-multi-pass-inferencing-easy",level:2},{value:"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing",id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing",level:2},{value:"Step 1: Training",id:"step-1-training",level:3},{value:"Step 2 Inference",id:"step-2-inference",level:3},{value:"Step 3: Examples",id:"step-3-examples",level:3},{value:"Final Note",id:"final-note",level:2}],d={toc:c},p="wrapper";function u(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,i.Z)({},d,s,{components:t,mdxType:"MDXLayout"}),(0,n.kt)("p",null,"Welcome to Astria.ai."),(0,n.kt)("p",null,"In our first blog post, we\u2019ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We\u2019ll show you how to structure high-quality prompts to generate visuals of professional quality."),(0,n.kt)("h2",{id:"what-is-multi-pass-inference"},"What Is Multi-Pass Inference?"),(0,n.kt)("p",null,"First, let\u2019s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image."),(0,n.kt)("p",null,"Here's how multi-pass inference enhances control over the background of an image:"),(0,n.kt)("h3",{id:"1-iterative-refinement"},"1. Iterative Refinement"),(0,n.kt)("p",null,"In a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome."),(0,n.kt)("h3",{id:"2-choice-over-base-model"},"2. Choice over base model"),(0,n.kt)("p",null,"Multi-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models."),(0,n.kt)("h3",{id:"3-increased-precision-and-detailing"},"3. Increased Precision and Detailing"),(0,n.kt)("p",null,"With multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model's output is more dependent on the initial prompt and less on a multi-step method."),(0,n.kt)("h3",{id:"4-balancing-foreground-and-background"},"4. Balancing Foreground and Background"),(0,n.kt)("p",null,"Multi-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively."),(0,n.kt)("p",null,"As an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach."),(0,n.kt)("center",null," (without multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3415).Z,width:"1599",height:"1999"})),(0,n.kt)("center",null," (with multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3184).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"As you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former."),(0,n.kt)("h2",{id:"how-multi-pass-inference-can-benefit-your-business"},"How Multi-Pass Inference Can Benefit Your Business"),(0,n.kt)("p",null,"The enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence."),(0,n.kt)("p",null,"For e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales."),(0,n.kt)("p",null,"Moreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels."),(0,n.kt)("p",null,"Lastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market."),(0,n.kt)("h2",{id:"how-astriaai-makes-multi-pass-inferencing-easy"},"How Astria.ai makes Multi-pass inferencing easy"),(0,n.kt)("p",null,"Multi-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer's expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how."),(0,n.kt)("p",null,"Let\u2019s first understand how a developer\u2019s expertise is needed and then we\u2019ll show how Astria.ai makes this process easier."),(0,n.kt)("p",null,"If one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks."),(0,n.kt)("p",null,"Moreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images."),(0,n.kt)("p",null,"Astria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints."),(0,n.kt)("p",null,"Overall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers."),(0,n.kt)("h2",{id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing"},"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing"),(0,n.kt)("h3",{id:"step-1-training"},"Step 1: Training"),(0,n.kt)("p",null,"First, create a fine-tune of your subject."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(4828).Z,width:"959",height:"845"})),(0,n.kt)("p",null,"Select the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type."),(0,n.kt)("p",null,"We used the following images of a male model obtained from a royalty free collection (Pixabay):"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6876).Z,width:"1600",height:"1600"})),(0,n.kt)("p",null,"Once the tune is ready, we can begin to prompt. Click on your tune."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7034).Z,width:"1454",height:"846"})),(0,n.kt)("h3",{id:"step-2-inference"},"Step 2 Inference"),(0,n.kt)("p",null,"Let\u2019s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand."),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.\nBREAK photorealistic and highly detailed\nBREAK ohwx man wearing hiking clothes and shoes \n")),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},"The first line contains the base prompt to generate the background and the overall composition."),(0,n.kt)("li",{parentName:"ul"},"The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition."),(0,n.kt)("li",{parentName:"ul"},"The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - ","<","lora:960310:1.0> - is added to load the fine-tuned model of our subject.")),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"Negative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck\n")),(0,n.kt)("p",null,"The negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt."),(0,n.kt)("p",null,"We can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that\u2019ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img."),(0,n.kt)("p",null,"For example, let\u2019s take this pose as our input image:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(5052).Z,width:"1280",height:"853"})),(0,n.kt)("p",null,"Also, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output."),(0,n.kt)("p",null,"Let\u2019s look at the result of our first prompt:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6111).Z,width:"1600",height:"1097"})),(0,n.kt)("p",null,"As you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image."),(0,n.kt)("h3",{id:"step-3-examples"},"Step 3: Examples"),(0,n.kt)("p",null,"Prompt 2:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"a man at the finish line of a race on an olympic track\nBREAK sharp details\nBREAK ohwx man wearing running clothes and shoes, jubilant expression on his face<lora:960310:1.0>\n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6959).Z,width:"1536",height:"1920"})),(0,n.kt)("p",null,"Prompt 3:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"full body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit\nBREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65\nBREAK ohwx man doing push-ups, intense look on his face \n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(1601).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"Prompt 4:\n(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"BREAK\nBREAK ohwx man wearing casual sports wear<lora:960310:1.0>\nNegative: hat, cartoon, ugly\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7549).Z,width:"1280",height:"1600"})),(0,n.kt)("h2",{id:"final-note"},"Final Note"),(0,n.kt)("p",null,"The above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives."),(0,n.kt)("p",null,"Whether you're looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals."))}u.isMDXComponent=!0},1601:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image1-ed2246b12f83db790d137c93ec375a7d.png"},4828:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image10-6d93e8d9a2d4a97f2b8965d686d93489.png"},7549:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image2-ecaf6b1d065d3eb6c57c399392ee34e3.png"},7034:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image3-fe71a43fd1042a1c53ef41ccb4493c0e.png"},6876:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image4-3d68ca1fd77775a75949e0fdea948ede.jpg"},5052:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image5-8fb740755774cf33efb7b8cf6deb3c60.jpg"},3184:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image6-75d8f7c291788629a5e79224da37059c.png"},3415:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image7-93a3f8e6eb0be8b0e45d06ff58a44e71.png"},6111:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image8-530b7527b4bd99a570acb0179aafcb36.png"},6959:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image9-4c157fa32c1049e695850306078a6fe1.png"}}]); \ No newline at end of file diff --git a/assets/js/79c75afb.1bf5ca1c.js b/assets/js/79c75afb.1bf5ca1c.js deleted file mode 100644 index e5c781c..0000000 --- a/assets/js/79c75afb.1bf5ca1c.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[589],{4042:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>l,contentTitle:()=>o,default:()=>u,frontMatter:()=>s,metadata:()=>r,toc:()=>c});var i=a(7462),n=(a(7294),a(3905));const s={title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"},o="How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",r={permalink:"/astria-articles/2024/02/04/multi-pass-inference",source:"@site/blog/2024-02-04-multi-pass-inference.md",title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference",date:"2024-02-04T00:00:00.000Z",formattedDate:"February 4, 2024",tags:[],readingTime:8.61,hasTruncateMarker:!1,authors:[],frontMatter:{title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"}},l={authorsImageUrls:[]},c=[{value:"What Is Multi-Pass Inference?",id:"what-is-multi-pass-inference",level:2},{value:"1. Iterative Refinement",id:"1-iterative-refinement",level:3},{value:"2. Choice over base model",id:"2-choice-over-base-model",level:3},{value:"3. Increased Precision and Detailing",id:"3-increased-precision-and-detailing",level:3},{value:"4. Balancing Foreground and Background",id:"4-balancing-foreground-and-background",level:3},{value:"How Multi-Pass Inference Can Benefit Your Business",id:"how-multi-pass-inference-can-benefit-your-business",level:2},{value:"How Astria.ai makes Multi-pass inferencing easy",id:"how-astriaai-makes-multi-pass-inferencing-easy",level:2},{value:"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing",id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing",level:2},{value:"Step 1: Training",id:"step-1-training",level:3},{value:"Step 2 Inference",id:"step-2-inference",level:3},{value:"Step 3: Examples",id:"step-3-examples",level:3},{value:"Final Note",id:"final-note",level:2}],d={toc:c},p="wrapper";function u(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,i.Z)({},d,s,{components:t,mdxType:"MDXLayout"}),(0,n.kt)("p",null,"Welcome to Astria.ai."),(0,n.kt)("p",null,"In our first blog post, we\u2019ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We\u2019ll show you how to structure high-quality prompts to generate visuals of professional quality."),(0,n.kt)("h2",{id:"what-is-multi-pass-inference"},"What Is Multi-Pass Inference?"),(0,n.kt)("p",null,"First, let\u2019s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image."),(0,n.kt)("p",null,"Here's how multi-pass inference enhances control over the background of an image:"),(0,n.kt)("h3",{id:"1-iterative-refinement"},"1. Iterative Refinement"),(0,n.kt)("p",null,"In a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome."),(0,n.kt)("h3",{id:"2-choice-over-base-model"},"2. Choice over base model"),(0,n.kt)("p",null,"Multi-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models."),(0,n.kt)("h3",{id:"3-increased-precision-and-detailing"},"3. Increased Precision and Detailing"),(0,n.kt)("p",null,"With multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model's output is more dependent on the initial prompt and less on a multi-step method."),(0,n.kt)("h3",{id:"4-balancing-foreground-and-background"},"4. Balancing Foreground and Background"),(0,n.kt)("p",null,"Multi-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively."),(0,n.kt)("p",null,"As an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach."),(0,n.kt)("center",null," (without multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3415).Z,width:"1599",height:"1999"})),(0,n.kt)("center",null," (with multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3184).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"As you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former."),(0,n.kt)("h2",{id:"how-multi-pass-inference-can-benefit-your-business"},"How Multi-Pass Inference Can Benefit Your Business"),(0,n.kt)("p",null,"The enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence."),(0,n.kt)("p",null,"For e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales."),(0,n.kt)("p",null,"Moreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels."),(0,n.kt)("p",null,"Lastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market."),(0,n.kt)("h2",{id:"how-astriaai-makes-multi-pass-inferencing-easy"},"How Astria.ai makes Multi-pass inferencing easy"),(0,n.kt)("p",null,"Multi-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer's expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how."),(0,n.kt)("p",null,"Let\u2019s first understand how a developer\u2019s expertise is needed and then we\u2019ll show how Astria.ai makes this process easier."),(0,n.kt)("p",null,"If one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks."),(0,n.kt)("p",null,"Moreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images."),(0,n.kt)("p",null,"Astria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints."),(0,n.kt)("p",null,"Overall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers."),(0,n.kt)("h2",{id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing"},"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing"),(0,n.kt)("h3",{id:"step-1-training"},"Step 1: Training"),(0,n.kt)("p",null,"First, create a fine-tune of your subject."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(4828).Z,width:"959",height:"845"})),(0,n.kt)("p",null,"Select the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type."),(0,n.kt)("p",null,"We used the following images of a male model obtained from a royalty free collection (Pixabay):"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6876).Z,width:"1600",height:"1600"})),(0,n.kt)("p",null,"Once the tune is ready, we can begin to prompt. Click on your tune."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7034).Z,width:"1454",height:"846"})),(0,n.kt)("h3",{id:"step-2-inference"},"Step 2 Inference"),(0,n.kt)("p",null,"Let\u2019s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand."),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.\nBREAK photorealistic and highly detailed\nBREAK ohwx man wearing hiking clothes and shoes \n")),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},"The first line contains the base prompt to generate the background and the overall composition."),(0,n.kt)("li",{parentName:"ul"},"The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition."),(0,n.kt)("li",{parentName:"ul"},"The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - ","<","lora:960310:1.0> - is added to load the fine-tuned model of our subject.")),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"Negative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck\n")),(0,n.kt)("p",null,"The negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt."),(0,n.kt)("p",null,"We can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that\u2019ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img."),(0,n.kt)("p",null,"For example, let\u2019s take this pose as our input image:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(5052).Z,width:"1280",height:"853"})),(0,n.kt)("p",null,"Also, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output."),(0,n.kt)("p",null,"Let\u2019s look at the result of our first prompt:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6111).Z,width:"1600",height:"1097"})),(0,n.kt)("p",null,"As you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image."),(0,n.kt)("h3",{id:"step-3-examples"},"Step 3: Examples"),(0,n.kt)("p",null,"Prompt 2:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"a man at the finish line of a race on an olympic track\nBREAK sharp details\nBREAK ohwx man wearing running clothes and shoes, jubilant expression on his face<lora:960310:1.0>\n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6959).Z,width:"1536",height:"1920"})),(0,n.kt)("p",null,"Prompt 3:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"full body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit\nBREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65\nBREAK ohwx man doing push-ups, intense look on his face \n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(1601).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"Prompt 4:\n(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"BREAK\nBREAK ohwx man wearing casual sports wear<lora:960310:1.0>\nNegative: hat, cartoon, ugly\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7549).Z,width:"1280",height:"1600"})),(0,n.kt)("h2",{id:"final-note"},"Final Note"),(0,n.kt)("p",null,"The above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives."),(0,n.kt)("p",null,"Whether you're looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals."))}u.isMDXComponent=!0},1601:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image1-ed2246b12f83db790d137c93ec375a7d.png"},4828:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image10-6d93e8d9a2d4a97f2b8965d686d93489.png"},7549:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image2-ecaf6b1d065d3eb6c57c399392ee34e3.png"},7034:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image3-fe71a43fd1042a1c53ef41ccb4493c0e.png"},6876:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image4-3d68ca1fd77775a75949e0fdea948ede.jpg"},5052:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image5-8fb740755774cf33efb7b8cf6deb3c60.jpg"},3184:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image6-75d8f7c291788629a5e79224da37059c.png"},3415:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image7-93a3f8e6eb0be8b0e45d06ff58a44e71.png"},6111:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image8-530b7527b4bd99a570acb0179aafcb36.png"},6959:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image9-4c157fa32c1049e695850306078a6fe1.png"}}]); \ No newline at end of file diff --git a/assets/js/79c75afb.2b85edb8.js b/assets/js/79c75afb.2b85edb8.js new file mode 100644 index 0000000..6a86401 --- /dev/null +++ b/assets/js/79c75afb.2b85edb8.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[589],{4042:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>l,contentTitle:()=>o,default:()=>u,frontMatter:()=>s,metadata:()=>r,toc:()=>c});var i=a(7462),n=(a(7294),a(3905));const s={title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"},o="How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",r={permalink:"/articles/2024/02/04/multi-pass-inference",source:"@site/blog/2024-02-04-multi-pass-inference.md",title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference",date:"2024-02-04T00:00:00.000Z",formattedDate:"February 4, 2024",tags:[],readingTime:8.61,hasTruncateMarker:!1,authors:[],frontMatter:{title:"How to Use Astria.ai's Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation",description:"How to Generate Striking Images with Astria.ai's Multi-Pass Inference"}},l={authorsImageUrls:[]},c=[{value:"What Is Multi-Pass Inference?",id:"what-is-multi-pass-inference",level:2},{value:"1. Iterative Refinement",id:"1-iterative-refinement",level:3},{value:"2. Choice over base model",id:"2-choice-over-base-model",level:3},{value:"3. Increased Precision and Detailing",id:"3-increased-precision-and-detailing",level:3},{value:"4. Balancing Foreground and Background",id:"4-balancing-foreground-and-background",level:3},{value:"How Multi-Pass Inference Can Benefit Your Business",id:"how-multi-pass-inference-can-benefit-your-business",level:2},{value:"How Astria.ai makes Multi-pass inferencing easy",id:"how-astriaai-makes-multi-pass-inferencing-easy",level:2},{value:"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing",id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing",level:2},{value:"Step 1: Training",id:"step-1-training",level:3},{value:"Step 2 Inference",id:"step-2-inference",level:3},{value:"Step 3: Examples",id:"step-3-examples",level:3},{value:"Final Note",id:"final-note",level:2}],d={toc:c},p="wrapper";function u(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,i.Z)({},d,s,{components:t,mdxType:"MDXLayout"}),(0,n.kt)("p",null,"Welcome to Astria.ai."),(0,n.kt)("p",null,"In our first blog post, we\u2019ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We\u2019ll show you how to structure high-quality prompts to generate visuals of professional quality."),(0,n.kt)("h2",{id:"what-is-multi-pass-inference"},"What Is Multi-Pass Inference?"),(0,n.kt)("p",null,"First, let\u2019s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image."),(0,n.kt)("p",null,"Here's how multi-pass inference enhances control over the background of an image:"),(0,n.kt)("h3",{id:"1-iterative-refinement"},"1. Iterative Refinement"),(0,n.kt)("p",null,"In a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome."),(0,n.kt)("h3",{id:"2-choice-over-base-model"},"2. Choice over base model"),(0,n.kt)("p",null,"Multi-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models."),(0,n.kt)("h3",{id:"3-increased-precision-and-detailing"},"3. Increased Precision and Detailing"),(0,n.kt)("p",null,"With multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model's output is more dependent on the initial prompt and less on a multi-step method."),(0,n.kt)("h3",{id:"4-balancing-foreground-and-background"},"4. Balancing Foreground and Background"),(0,n.kt)("p",null,"Multi-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively."),(0,n.kt)("p",null,"As an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach."),(0,n.kt)("center",null," (without multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3415).Z,width:"1599",height:"1999"})),(0,n.kt)("center",null," (with multi-pass) "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(3184).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"As you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former."),(0,n.kt)("h2",{id:"how-multi-pass-inference-can-benefit-your-business"},"How Multi-Pass Inference Can Benefit Your Business"),(0,n.kt)("p",null,"The enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence."),(0,n.kt)("p",null,"For e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales."),(0,n.kt)("p",null,"Moreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels."),(0,n.kt)("p",null,"Lastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market."),(0,n.kt)("h2",{id:"how-astriaai-makes-multi-pass-inferencing-easy"},"How Astria.ai makes Multi-pass inferencing easy"),(0,n.kt)("p",null,"Multi-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer's expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how."),(0,n.kt)("p",null,"Let\u2019s first understand how a developer\u2019s expertise is needed and then we\u2019ll show how Astria.ai makes this process easier."),(0,n.kt)("p",null,"If one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks."),(0,n.kt)("p",null,"Moreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images."),(0,n.kt)("p",null,"Astria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints."),(0,n.kt)("p",null,"Overall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers."),(0,n.kt)("h2",{id:"step-by-step-guide-to-creating-images-for-a-sportswear-brand-using-multi-pass-inferencing"},"Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing"),(0,n.kt)("h3",{id:"step-1-training"},"Step 1: Training"),(0,n.kt)("p",null,"First, create a fine-tune of your subject."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(4828).Z,width:"959",height:"845"})),(0,n.kt)("p",null,"Select the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type."),(0,n.kt)("p",null,"We used the following images of a male model obtained from a royalty free collection (Pixabay):"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6876).Z,width:"1600",height:"1600"})),(0,n.kt)("p",null,"Once the tune is ready, we can begin to prompt. Click on your tune."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7034).Z,width:"1454",height:"846"})),(0,n.kt)("h3",{id:"step-2-inference"},"Step 2 Inference"),(0,n.kt)("p",null,"Let\u2019s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand."),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.\nBREAK photorealistic and highly detailed\nBREAK ohwx man wearing hiking clothes and shoes \n")),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},"The first line contains the base prompt to generate the background and the overall composition."),(0,n.kt)("li",{parentName:"ul"},"The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition."),(0,n.kt)("li",{parentName:"ul"},"The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - ","<","lora:960310:1.0> - is added to load the fine-tuned model of our subject.")),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"Negative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck\n")),(0,n.kt)("p",null,"The negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt."),(0,n.kt)("p",null,"We can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that\u2019ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img."),(0,n.kt)("p",null,"For example, let\u2019s take this pose as our input image:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(5052).Z,width:"1280",height:"853"})),(0,n.kt)("p",null,"Also, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output."),(0,n.kt)("p",null,"Let\u2019s look at the result of our first prompt:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6111).Z,width:"1600",height:"1097"})),(0,n.kt)("p",null,"As you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image."),(0,n.kt)("h3",{id:"step-3-examples"},"Step 3: Examples"),(0,n.kt)("p",null,"Prompt 2:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"a man at the finish line of a race on an olympic track\nBREAK sharp details\nBREAK ohwx man wearing running clothes and shoes, jubilant expression on his face<lora:960310:1.0>\n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(6959).Z,width:"1536",height:"1920"})),(0,n.kt)("p",null,"Prompt 3:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"full body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit\nBREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65\nBREAK ohwx man doing push-ups, intense look on his face \n\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(1601).Z,width:"1280",height:"1600"})),(0,n.kt)("p",null,"Prompt 4:\n(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"BREAK\nBREAK ohwx man wearing casual sports wear<lora:960310:1.0>\nNegative: hat, cartoon, ugly\n")),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"alt_text",src:a(7549).Z,width:"1280",height:"1600"})),(0,n.kt)("h2",{id:"final-note"},"Final Note"),(0,n.kt)("p",null,"The above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives."),(0,n.kt)("p",null,"Whether you're looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals."))}u.isMDXComponent=!0},1601:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image1-ed2246b12f83db790d137c93ec375a7d.png"},4828:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image10-6d93e8d9a2d4a97f2b8965d686d93489.png"},7549:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image2-ecaf6b1d065d3eb6c57c399392ee34e3.png"},7034:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image3-fe71a43fd1042a1c53ef41ccb4493c0e.png"},6876:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image4-3d68ca1fd77775a75949e0fdea948ede.jpg"},5052:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image5-8fb740755774cf33efb7b8cf6deb3c60.jpg"},3184:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image6-75d8f7c291788629a5e79224da37059c.png"},3415:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image7-93a3f8e6eb0be8b0e45d06ff58a44e71.png"},6111:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image8-530b7527b4bd99a570acb0179aafcb36.png"},6959:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/image9-4c157fa32c1049e695850306078a6fe1.png"}}]); \ No newline at end of file diff --git a/assets/js/90e7b1d0.a46f9018.js b/assets/js/90e7b1d0.a46f9018.js new file mode 100644 index 0000000..2595772 --- /dev/null +++ b/assets/js/90e7b1d0.a46f9018.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[321],{9264:s=>{s.exports=JSON.parse('{"permalink":"/articles/","page":1,"postsPerPage":10,"totalPages":1,"totalCount":1,"blogDescription":"Blog","blogTitle":"Blog"}')}}]); \ No newline at end of file diff --git a/assets/js/935f2afb.776c12b3.js b/assets/js/935f2afb.a4b930c0.js similarity index 62% rename from assets/js/935f2afb.776c12b3.js rename to assets/js/935f2afb.a4b930c0.js index 41e70ff..ceea634 100644 --- a/assets/js/935f2afb.776c12b3.js +++ b/assets/js/935f2afb.a4b930c0.js @@ -1 +1 @@ -"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[53],{1109:e=>{e.exports=JSON.parse('{"pluginId":"default","version":"current","label":"Next","banner":null,"badge":false,"noIndex":false,"className":"docs-version-current","isLast":true,"docsSidebars":{"tutorialSidebar":[{"type":"link","label":"changes","href":"/astria-articles/docs/changes","docId":"changes"}]},"docs":{"changes":{"id":"changes","title":"changes","description":"","sidebar":"tutorialSidebar"}}}')}}]); \ No newline at end of file +"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[53],{1109:e=>{e.exports=JSON.parse('{"pluginId":"default","version":"current","label":"Next","banner":null,"badge":false,"noIndex":false,"className":"docs-version-current","isLast":true,"docsSidebars":{"tutorialSidebar":[{"type":"link","label":"changes","href":"/articles/docs/changes","docId":"changes"}]},"docs":{"changes":{"id":"changes","title":"changes","description":"","sidebar":"tutorialSidebar"}}}')}}]); \ No newline at end of file diff --git a/assets/js/9fda4f55.0b967803.js b/assets/js/9fda4f55.a8782398.js similarity index 54% rename from assets/js/9fda4f55.0b967803.js rename to assets/js/9fda4f55.a8782398.js index 1896902..1b5037d 100644 --- a/assets/js/9fda4f55.0b967803.js +++ b/assets/js/9fda4f55.a8782398.js @@ -1 +1 @@ -"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[868],{2713:(e,t,s)=>{s.r(t),s.d(t,{assets:()=>i,contentTitle:()=>o,default:()=>l,frontMatter:()=>r,metadata:()=>c,toc:()=>d});var a=s(7462),n=(s(7294),s(3905));const r={},o=void 0,c={unversionedId:"changes",id:"changes",title:"changes",description:"",source:"@site/docs/0-changes.md",sourceDirName:".",slug:"/changes",permalink:"/astria-articles/docs/changes",draft:!1,tags:[],version:"current",sidebarPosition:0,frontMatter:{},sidebar:"tutorialSidebar"},i={},d=[],u={toc:d},p="wrapper";function l(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,a.Z)({},u,s,{components:t,mdxType:"MDXLayout"}))}l.isMDXComponent=!0}}]); \ No newline at end of file +"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[868],{2713:(e,t,s)=>{s.r(t),s.d(t,{assets:()=>i,contentTitle:()=>o,default:()=>l,frontMatter:()=>r,metadata:()=>c,toc:()=>d});var a=s(7462),n=(s(7294),s(3905));const r={},o=void 0,c={unversionedId:"changes",id:"changes",title:"changes",description:"",source:"@site/docs/0-changes.md",sourceDirName:".",slug:"/changes",permalink:"/articles/docs/changes",draft:!1,tags:[],version:"current",sidebarPosition:0,frontMatter:{},sidebar:"tutorialSidebar"},i={},d=[],u={toc:d},p="wrapper";function l(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,a.Z)({},u,s,{components:t,mdxType:"MDXLayout"}))}l.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/a3f88606.bdbab22a.js b/assets/js/a3f88606.bdbab22a.js deleted file mode 100644 index f41fc9b..0000000 --- a/assets/js/a3f88606.bdbab22a.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[388],{5206:s=>{s.exports=JSON.parse('{"permalink":"/astria-articles/","page":1,"postsPerPage":10,"totalPages":1,"totalCount":1,"blogDescription":"Blog","blogTitle":"Blog"}')}}]); \ No newline at end of file diff --git a/assets/js/c85f55fc.e57c5951.js b/assets/js/c85f55fc.e57c5951.js new file mode 100644 index 0000000..30fca28 --- /dev/null +++ b/assets/js/c85f55fc.e57c5951.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[339],{9888:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"/2024/02/04/multi-pass-inference","metadata":{"permalink":"/articles/2024/02/04/multi-pass-inference","source":"@site/blog/2024-02-04-multi-pass-inference.md","title":"How to Use Astria.ai\'s Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation","description":"How to Generate Striking Images with Astria.ai\'s Multi-Pass Inference","date":"2024-02-04T00:00:00.000Z","formattedDate":"February 4, 2024","tags":[],"readingTime":8.61,"hasTruncateMarker":false,"authors":[],"frontMatter":{"title":"How to Use Astria.ai\'s Multi-Pass Inference for Highly Controlled Stable Diffusion Image Generation","description":"How to Generate Striking Images with Astria.ai\'s Multi-Pass Inference"}},"content":"Welcome to Astria.ai.\\r\\n\\r\\nIn our first blog post, we\u2019ll take a deep dive into how you can generate very detailed images using a multi-pass inference method. We\u2019ll show you how to structure high-quality prompts to generate visuals of professional quality.\\r\\n\\r\\n\\r\\n## What Is Multi-Pass Inference?\\r\\n\\r\\nFirst, let\u2019s discuss what multi-pass inference is. Multi-pass inference is essentially a technique where you can generate the background of the composition independently from the foreground. On Astria.ai this control is achieved through multiple breaks in the prompting technique. The base image (i.e. the background elements) is generated separately via the first part of the prompt. Then using the next breaks in the prompt the subject is in-painted onto the base image.\\r\\n\\r\\nHere\'s how multi-pass inference enhances control over the background of an image:\\r\\n\\r\\n\\r\\n### 1. Iterative Refinement\\r\\n\\r\\nIn a multi-pass inference, you have the opportunity to adjust and refine the background in a separate pass. This iterative process allows you to steer the image generation towards your desired outcome.\\r\\n\\r\\n\\r\\n### 2. Choice over base model\\r\\n\\r\\nMulti-pass inference allows for choice over the base model thereby giving the users the option to use a variety of pre-trained models like Realistic Vision, Absolute Reality, and other Stable Diffusion models.\\r\\n\\r\\n\\r\\n### 3. Increased Precision and Detailing\\r\\n\\r\\nWith multiple inference steps, you have more chances to introduce specific details or adjustments to the background. This can include changing its color scheme, adding or removing elements, or altering its overall style. Such precision is often not achievable in a single pass, where the model\'s output is more dependent on the initial prompt and less on a multi-step method.\\r\\n\\r\\n\\r\\n### 4. Balancing Foreground and Background\\r\\n\\r\\nMulti-pass inference allows for a more balanced composition between the foreground and the background so that you can modify the background in a way that it complements the foreground elements more effectively.\\r\\n\\r\\nAs an example take a look at these two images of a man wearing sportswear and posing inside a gym. The first one was generated in a single prompt, while for the second one we used a multi-pass approach.\\r\\n\\r\\n
(without multi-pass)
\\r\\n\\r\\n![alt_text](images/image7.png)\\r\\n\\r\\n
(with multi-pass)
\\r\\n\\r\\n![alt_text](images/image6.png)\\r\\n\\r\\n\\r\\nAs you can see in the second image the background has more character to it. The elements of the gym are more prominent as compared to the former.\\r\\n\\r\\n\\r\\n## How Multi-Pass Inference Can Benefit Your Business\\r\\n\\r\\nThe enhanced control over image backgrounds provided by multi-pass inference offers significant benefits for businesses in various domains. By precisely tailoring image backgrounds, companies can maintain a consistent visual brand identity, crucial for marketing, advertising, and establishing a strong social media presence.\\r\\n\\r\\nFor e-commerce and retail sectors, the background of product images plays a critical role in shaping customer perception. Tailoring these backgrounds to complement the products not only enhances their appeal but also provides clearer context, which can lead to increased sales.\\r\\n\\r\\nMoreover, multi-pass inference enables rapid and cost-effective creation of high-quality, bespoke images. This reduces the reliance on expensive photoshoots and graphic design work, presenting a more economical approach to content creation. Businesses can easily modify image backgrounds to suit various platforms and formats, such as social media, websites, and print media, ensuring optimal visual presentation across all channels.\\r\\n\\r\\nLastly, in a digital landscape overflowing with visual content, unique and tailored images with custom backgrounds provide businesses with a competitive edge. Such visuals are more likely to capture audience interest in a crowded market, standing out from standard, generic content. Therefore, the ability to control image backgrounds through multi-pass inference is not just a technical advantage but a strategic tool for branding, marketing, product presentation, and creating visually compelling content that differentiates a business in the market.\\r\\n\\r\\n\\r\\n## How Astria.ai makes Multi-pass inferencing easy\\r\\n\\r\\nMulti-pass inferencing, particularly in the context of advanced generative models like Stable Diffusion, often requires a developer\'s expertise due to several technical complexities. At Astria.ai, we provide a user-friendly apis that can significantly simplify this process for users who do not possess extensive technical know-how.\\r\\n\\r\\nLet\u2019s first understand how a developer\u2019s expertise is needed and then we\u2019ll show how Astria.ai makes this process easier.\\r\\n\\r\\nIf one were to fine-tune and implement Stable Diffusion for multi-pass inferencing one would need a fair understanding of how these machine learning models work so that they can adjust parameters for different passes. This would require a fair amount of coding skills especially for customizing the inference process, integrating different components (like schedulers, encoders, decoders), and handling data preprocessing and postprocessing. Developers must be proficient in relevant programming languages and frameworks.\\r\\n\\r\\nMoreover each pass in multi-pass inferencing may require adjustments to optimize the output. Developers need to troubleshoot issues, fine-tune parameters, and experiment with different configurations to achieve the desired results, which demands both technical skills and problem-solving abilities. Lastly, generative models can be resource-intensive. Developers need to manage and optimize the use of computational resources like GPUs, especially when working with large models or high-resolution images.\\r\\n\\r\\nAstria.ai simplifies the above procedures by providing simple APIs that abstract the complexities of the underlying model. The platform also comes with pre-configured settings and templates showcased in the gallery that users can select from, reducing the time to do prompt engineering, and helping understand the breadth of options available. This includes predefined prompts, styles, and optimization settings. Apart from this Astria also handles the computational resource management in the background, allowing users to focus on the creative aspects of image generation without worrying about technical constraints.\\r\\n\\r\\nOverall, while multi-pass inferencing with AI models requires considerable technical expertise due to its complexity, a platform like Astria.ai democratizes this capability by providing easy-to-use api and automated workflows, making advanced image generation accessible to developers.\\r\\n\\r\\n\\r\\n## Step-by-Step Guide to Creating Images for a Sportswear Brand Using Multi-Pass Inferencing\\r\\n\\r\\n### Step 1: Training\\r\\n\\r\\nFirst, create a fine-tune of your subject.\\r\\n\\r\\n![alt_text](images/image10.png)\\r\\n\\r\\n\\r\\nSelect the model type as LORA. This is a fast and efficient way to train the model, as it only trains an adapter layer on top of the base model, instead of training all the weights which is typically the case if we select the Checkpoint Model type.\\r\\n\\r\\nWe used the following images of a male model obtained from a royalty free collection (Pixabay):\\r\\n\\r\\n\\r\\n![alt_text](images/image4.jpg)\\r\\n\\r\\n\\r\\nOnce the tune is ready, we can begin to prompt. Click on your tune.\\r\\n\\r\\n![alt_text](images\\\\image3.png)\\r\\n\\r\\n\\r\\n### Step 2 Inference\\r\\n\\r\\nLet\u2019s first look at the structure of our prompt. Suppose you have to create images to market a sportswear brand.\\r\\n\\r\\n\\r\\n```\\r\\n(medium shot) of a male model wearing hiking clothes and shoes, standing in a dense forest, behind him is a small waterfall.\\r\\nBREAK photorealistic and highly detailed\\r\\nBREAK ohwx man wearing hiking clothes and shoes \\r\\n```\\r\\n\\r\\n* The first line contains the base prompt to generate the background and the overall composition.\\r\\n* The second line is a common prompt that is added both to the base prompt and the person prompt, in order to avoid repetition.\\r\\n* The third line is the person prompt, to detail how our subject is composed in the foreground. The statement - <lora:960310:1.0> - is added to load the fine-tuned model of our subject.\\r\\n\\r\\n```\\r\\nNegative Prompt: (brand logos on t-shirt), (worst quality, greyscale), watermark, username, signature, text, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, bad feet, extra fingers, mutated hands, poorly drawn hands, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck\\r\\n```\\r\\n\\r\\n\\r\\n\\r\\nThe negative prompt is a list of prompts we want to avoid in our generated image. Anything placed in parentheses applies extra weights to that prompt.\\r\\n\\r\\nWe can add an input image if we want our generated image to follow an input template. On the ControlNet Hint dropdown menu, we can select pose, if we want to copy the pose of the subject from the input image. Select the Text2img toggle to be true, that\u2019ll preserve the pose of the image. (recommended). If you want the semantics i.e. the looks and feels of the original image as well, then go for Img2img.\\r\\n\\r\\nFor example, let\u2019s take this pose as our input image:\\r\\n\\r\\n\\r\\n![alt_text](images/image5.jpg)\\r\\n\\r\\n\\r\\nAlso, keep the Inpaint Faces and Face Swap toggle on. Inpaint Faces iterates one more time over the faces of the subject to ensure that there is no distortion in the outcome, while the Face Swap option ensures that the face of our model is taken from the training images and swapped in the generated image to enhance resemblance in the final output.\\r\\n\\r\\nLet\u2019s look at the result of our first prompt:\\r\\n\\r\\n\\r\\n![alt_text](images/image8.png)\\r\\n\\r\\n\\r\\nAs you can see, the ControlNet has ensured that the output pose is similar to the pose of the input image.\\r\\n\\r\\n### Step 3: Examples\\r\\n\\r\\nPrompt 2:\\r\\n```\\r\\na man at the finish line of a race on an olympic track\\r\\nBREAK sharp details\\r\\nBREAK ohwx man wearing running clothes and shoes, jubilant expression on his face<lora:960310:1.0>\\r\\n\\r\\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\\r\\n```\\r\\n\\r\\n![alt_text](images/image9.png)\\r\\n\\r\\n\\r\\nPrompt 3:\\r\\n\\r\\n```\\r\\nfull body workout in a vibrant gym, action, perspective, speed, movement, ripped, push ups fit\\r\\nBREAK sharp details, realistic image, Porta 160 color, ARRI ALEXA 65\\r\\nBREAK ohwx man doing push-ups, intense look on his face \\r\\n\\r\\nNegative: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, long neck, disfigured, fused lips,\\r\\n```\\r\\n\\r\\n![alt_text](images/image1.png)\\r\\n\\r\\n\\r\\nPrompt 4:\\r\\n(wide shot) of a man walking at night on the streets of New York, warm lighting, photorealistic\\r\\n\\r\\n```\\r\\nBREAK\\r\\nBREAK ohwx man wearing casual sports wear<lora:960310:1.0>\\r\\nNegative: hat, cartoon, ugly\\r\\n```\\r\\n\\r\\n\\r\\n\\r\\n![alt_text](images/image2.png)\\r\\n\\r\\n\\r\\n\\r\\n## Final Note\\r\\n\\r\\nThe above steps can be used to generate product photography or e-commerce images. With multi-pass inference, you can gain a huge amount of control over your image backgrounds vis a vis the foreground. This technique allows you to iteratively refine and tailor the background details, ensuring that they align with your vision and objectives.\\r\\n\\r\\nWhether you\'re looking to create images for branding, marketing, storytelling, or artistic expression, multi-pass inference by Astria.ai provides the flexibility and precision to shape the background just as you need it. You can now harness this tool to bring depth, context, and nuance to your visual content, making your image speak in harmony with your creative goals."}]}')}}]); \ No newline at end of file diff --git a/assets/js/main.56209553.js b/assets/js/main.56209553.js new file mode 100644 index 0000000..555808a --- /dev/null +++ b/assets/js/main.56209553.js @@ -0,0 +1,2 @@ +/*! For license information please see main.56209553.js.LICENSE.txt */ +(self.webpackChunkastria_docs_2=self.webpackChunkastria_docs_2||[]).push([[179],{723:(e,t,n)=>{"use strict";n.d(t,{Z:()=>p});var r=n(7294),a=n(7462),o=n(8356),i=n.n(o),l=n(6887);const s={17896441:[()=>Promise.all([n.e(532),n.e(592),n.e(918)]).then(n.bind(n,8945)),"@theme/DocItem",8945],"1be78505":[()=>Promise.all([n.e(532),n.e(514)]).then(n.bind(n,9963)),"@theme/DocPage",9963],"3fd67606":[()=>n.e(940).then(n.t.bind(n,3769,19)),"/Users/burg/git/astria-articles/.docusaurus/docusaurus-plugin-content-docs/default/plugin-route-context-module-100.json",3769],"5348e8a7":[()=>n.e(823).then(n.t.bind(n,4469,19)),"/Users/burg/git/astria-articles/.docusaurus/docusaurus-plugin-content-blog/default/plugin-route-context-module-100.json",4469],"5f003b45":[()=>Promise.all([n.e(592),n.e(658)]).then(n.bind(n,8847)),"@site/blog/2024-02-04-multi-pass-inference.md?truncated=true",8847],"79c75afb":[()=>Promise.all([n.e(592),n.e(589)]).then(n.bind(n,4042)),"@site/blog/2024-02-04-multi-pass-inference.md",4042],"814f3328":[()=>n.e(535).then(n.t.bind(n,5641,19)),"~blog/default/blog-post-list-prop-default.json",5641],"90e7b1d0":[()=>n.e(321).then(n.t.bind(n,9264,19)),"~blog/default/articles-18f.json",9264],"935f2afb":[()=>n.e(53).then(n.t.bind(n,1109,19)),"~docs/default/version-current-metadata-prop-751.json",1109],"9e4087bc":[()=>n.e(608).then(n.bind(n,3169)),"@theme/BlogArchivePage",3169],"9fda4f55":[()=>Promise.all([n.e(592),n.e(868)]).then(n.bind(n,2713)),"@site/docs/0-changes.md",2713],a6aa9e1f:[()=>Promise.all([n.e(532),n.e(592),n.e(218),n.e(89)]).then(n.bind(n,3269)),"@theme/BlogListPage",3269],c85f55fc:[()=>n.e(339).then(n.t.bind(n,9888,19)),"~blog/default/articles-archive-305.json",9888],ccc49370:[()=>Promise.all([n.e(532),n.e(592),n.e(218),n.e(103)]).then(n.bind(n,5203)),"@theme/BlogPostPage",5203]};function u(e){let{error:t,retry:n,pastDelay:a}=e;return t?r.createElement("div",{style:{textAlign:"center",color:"#fff",backgroundColor:"#fa383e",borderColor:"#fa383e",borderStyle:"solid",borderRadius:"0.25rem",borderWidth:"1px",boxSizing:"border-box",display:"block",padding:"1rem",flex:"0 0 50%",marginLeft:"25%",marginRight:"25%",marginTop:"5rem",maxWidth:"50%",width:"100%"}},r.createElement("p",null,String(t)),r.createElement("div",null,r.createElement("button",{type:"button",onClick:n},"Retry"))):a?r.createElement("div",{style:{display:"flex",justifyContent:"center",alignItems:"center",height:"100vh"}},r.createElement("svg",{id:"loader",style:{width:128,height:110,position:"absolute",top:"calc(100vh - 64%)"},viewBox:"0 0 45 45",xmlns:"http://www.w3.org/2000/svg",stroke:"#61dafb"},r.createElement("g",{fill:"none",fillRule:"evenodd",transform:"translate(1 1)",strokeWidth:"2"},r.createElement("circle",{cx:"22",cy:"22",r:"6",strokeOpacity:"0"},r.createElement("animate",{attributeName:"r",begin:"1.5s",dur:"3s",values:"6;22",calcMode:"linear",repeatCount:"indefinite"}),r.createElement("animate",{attributeName:"stroke-opacity",begin:"1.5s",dur:"3s",values:"1;0",calcMode:"linear",repeatCount:"indefinite"}),r.createElement("animate",{attributeName:"stroke-width",begin:"1.5s",dur:"3s",values:"2;0",calcMode:"linear",repeatCount:"indefinite"})),r.createElement("circle",{cx:"22",cy:"22",r:"6",strokeOpacity:"0"},r.createElement("animate",{attributeName:"r",begin:"3s",dur:"3s",values:"6;22",calcMode:"linear",repeatCount:"indefinite"}),r.createElement("animate",{attributeName:"stroke-opacity",begin:"3s",dur:"3s",values:"1;0",calcMode:"linear",repeatCount:"indefinite"}),r.createElement("animate",{attributeName:"stroke-width",begin:"3s",dur:"3s",values:"2;0",calcMode:"linear",repeatCount:"indefinite"})),r.createElement("circle",{cx:"22",cy:"22",r:"8"},r.createElement("animate",{attributeName:"r",begin:"0s",dur:"1.5s",values:"6;1;2;3;4;5;6",calcMode:"linear",repeatCount:"indefinite"}))))):null}var c=n(9670),d=n(226);function f(e,t){if("*"===e)return i()({loading:u,loader:()=>n.e(972).then(n.bind(n,4972)),modules:["@theme/NotFound"],webpack:()=>[4972],render(e,t){const n=e.default;return r.createElement(d.z,{value:{plugin:{name:"native",id:"default"}}},r.createElement(n,t))}});const o=l[`${e}-${t}`],f={},p=[],m=[],h=(0,c.Z)(o);return Object.entries(h).forEach((e=>{let[t,n]=e;const r=s[n];r&&(f[t]=r[0],p.push(r[1]),m.push(r[2]))})),i().Map({loading:u,loader:f,modules:p,webpack:()=>m,render(t,n){const i=JSON.parse(JSON.stringify(o));Object.entries(t).forEach((t=>{let[n,r]=t;const a=r.default;if(!a)throw new Error(`The page component at ${e} doesn't have a default export. This makes it impossible to render anything. Consider default-exporting a React component.`);"object"!=typeof a&&"function"!=typeof a||Object.keys(r).filter((e=>"default"!==e)).forEach((e=>{a[e]=r[e]}));let o=i;const l=n.split(".");l.slice(0,-1).forEach((e=>{o=o[e]})),o[l[l.length-1]]=a}));const l=i.__comp;delete i.__comp;const s=i.__context;return delete i.__context,r.createElement(d.z,{value:s},r.createElement(l,(0,a.Z)({},i,n)))}})}const p=[{path:"/articles/2024/02/04/multi-pass-inference",component:f("/articles/2024/02/04/multi-pass-inference","43f"),exact:!0},{path:"/articles/archive",component:f("/articles/archive","277"),exact:!0},{path:"/articles/docs",component:f("/articles/docs","cf0"),routes:[{path:"/articles/docs/changes",component:f("/articles/docs/changes","0a7"),exact:!0,sidebar:"tutorialSidebar"}]},{path:"/articles/",component:f("/articles/","0d2"),exact:!0},{path:"*",component:f("*")}]},8934:(e,t,n)=>{"use strict";n.d(t,{_:()=>a,t:()=>o});var r=n(7294);const a=r.createContext(!1);function o(e){let{children:t}=e;const[n,o]=(0,r.useState)(!1);return(0,r.useEffect)((()=>{o(!0)}),[]),r.createElement(a.Provider,{value:n},t)}},7221:(e,t,n)=>{"use strict";var r=n(7294),a=n(3935),o=n(3727),i=n(405),l=n(412);const s=[n(2497),n(3310),n(8320),n(2295)];var u=n(723),c=n(6550),d=n(8790);function f(e){let{children:t}=e;return r.createElement(r.Fragment,null,t)}var p=n(7462),m=n(5742),h=n(2263),g=n(4996),v=n(6668),b=n(1944),y=n(4711),w=n(9727),k=n(3320),E=n(8780),S=n(197);function x(){const{i18n:{defaultLocale:e,localeConfigs:t}}=(0,h.Z)(),n=(0,y.l)();return r.createElement(m.Z,null,Object.entries(t).map((e=>{let[t,{htmlLang:a}]=e;return r.createElement("link",{key:t,rel:"alternate",href:n.createUrl({locale:t,fullyQualified:!0}),hrefLang:a})})),r.createElement("link",{rel:"alternate",href:n.createUrl({locale:e,fullyQualified:!0}),hrefLang:"x-default"}))}function C(e){let{permalink:t}=e;const{siteConfig:{url:n}}=(0,h.Z)(),a=function(){const{siteConfig:{url:e,baseUrl:t,trailingSlash:n}}=(0,h.Z)(),{pathname:r}=(0,c.TH)();return e+(0,E.applyTrailingSlash)((0,g.Z)(r),{trailingSlash:n,baseUrl:t})}(),o=t?`${n}${t}`:a;return r.createElement(m.Z,null,r.createElement("meta",{property:"og:url",content:o}),r.createElement("link",{rel:"canonical",href:o}))}function T(){const{i18n:{currentLocale:e}}=(0,h.Z)(),{metadata:t,image:n}=(0,v.L)();return r.createElement(r.Fragment,null,r.createElement(m.Z,null,r.createElement("meta",{name:"twitter:card",content:"summary_large_image"}),r.createElement("body",{className:w.h})),n&&r.createElement(b.d,{image:n}),r.createElement(C,null),r.createElement(x,null),r.createElement(S.Z,{tag:k.HX,locale:e}),r.createElement(m.Z,null,t.map(((e,t)=>r.createElement("meta",(0,p.Z)({key:t},e))))))}const _=new Map;function A(e){if(_.has(e.pathname))return{...e,pathname:_.get(e.pathname)};if((0,d.f)(u.Z,e.pathname).some((e=>{let{route:t}=e;return!0===t.exact})))return _.set(e.pathname,e.pathname),e;const t=e.pathname.trim().replace(/(?:\/index)?\.html$/,"")||"/";return _.set(e.pathname,t),{...e,pathname:t}}var L=n(8934),R=n(8940);function N(e){for(var t=arguments.length,n=new Array(t>1?t-1:0),r=1;r{const r=t.default?.[e]??t[e];return r?.(...n)}));return()=>a.forEach((e=>e?.()))}const P=function(e){let{children:t,location:n,previousLocation:a}=e;return(0,r.useLayoutEffect)((()=>{a!==n&&(!function(e){let{location:t,previousLocation:n}=e;if(!n)return;const r=t.pathname===n.pathname,a=t.hash===n.hash,o=t.search===n.search;if(r&&a&&!o)return;const{hash:i}=t;if(i){const e=decodeURIComponent(i.substring(1)),t=document.getElementById(e);t?.scrollIntoView()}else window.scrollTo(0,0)}({location:n,previousLocation:a}),N("onRouteDidUpdate",{previousLocation:a,location:n}))}),[a,n]),t};function O(e){const t=Array.from(new Set([e,decodeURI(e)])).map((e=>(0,d.f)(u.Z,e))).flat();return Promise.all(t.map((e=>e.route.component.preload?.())))}class I extends r.Component{previousLocation;routeUpdateCleanupCb;constructor(e){super(e),this.previousLocation=null,this.routeUpdateCleanupCb=l.Z.canUseDOM?N("onRouteUpdate",{previousLocation:null,location:this.props.location}):()=>{},this.state={nextRouteHasLoaded:!0}}shouldComponentUpdate(e,t){if(e.location===this.props.location)return t.nextRouteHasLoaded;const n=e.location;return this.previousLocation=this.props.location,this.setState({nextRouteHasLoaded:!1}),this.routeUpdateCleanupCb=N("onRouteUpdate",{previousLocation:this.previousLocation,location:n}),O(n.pathname).then((()=>{this.routeUpdateCleanupCb(),this.setState({nextRouteHasLoaded:!0})})).catch((e=>{console.warn(e),window.location.reload()})),!1}render(){const{children:e,location:t}=this.props;return r.createElement(P,{previousLocation:this.previousLocation,location:t},r.createElement(c.AW,{location:t,render:()=>e}))}}const D=I,M="__docusaurus-base-url-issue-banner-container",F="__docusaurus-base-url-issue-banner",B="__docusaurus-base-url-issue-banner-suggestion-container",j="__DOCUSAURUS_INSERT_BASEURL_BANNER";function z(e){return`\nwindow['${j}'] = true;\n\ndocument.addEventListener('DOMContentLoaded', maybeInsertBanner);\n\nfunction maybeInsertBanner() {\n var shouldInsert = window['${j}'];\n shouldInsert && insertBanner();\n}\n\nfunction insertBanner() {\n var bannerContainer = document.getElementById('${M}');\n if (!bannerContainer) {\n return;\n }\n var bannerHtml = ${JSON.stringify(function(e){return`\n
\n

Your Docusaurus site did not load properly.

\n

A very common reason is a wrong site baseUrl configuration.

\n

Current configured baseUrl = ${e} ${"/"===e?" (default value)":""}

\n

We suggest trying baseUrl =

\n
\n`}(e)).replace(/{window[j]=!1}),[]),r.createElement(r.Fragment,null,!l.Z.canUseDOM&&r.createElement(m.Z,null,r.createElement("script",null,z(e))),r.createElement("div",{id:M}))}function $(){const{siteConfig:{baseUrl:e,baseUrlIssueBanner:t}}=(0,h.Z)(),{pathname:n}=(0,c.TH)();return t&&n===e?r.createElement(U,null):null}function q(){const{siteConfig:{favicon:e,title:t,noIndex:n},i18n:{currentLocale:a,localeConfigs:o}}=(0,h.Z)(),i=(0,g.Z)(e),{htmlLang:l,direction:s}=o[a];return r.createElement(m.Z,null,r.createElement("html",{lang:l,dir:s}),r.createElement("title",null,t),r.createElement("meta",{property:"og:title",content:t}),r.createElement("meta",{name:"viewport",content:"width=device-width, initial-scale=1.0"}),n&&r.createElement("meta",{name:"robots",content:"noindex, nofollow"}),e&&r.createElement("link",{rel:"icon",href:i}))}var H=n(4763),G=n(2389);function Z(){const e=(0,G.Z)();return r.createElement(m.Z,null,r.createElement("html",{"data-has-hydrated":e}))}function V(){const e=(0,d.H)(u.Z),t=(0,c.TH)();return r.createElement(H.Z,null,r.createElement(R.M,null,r.createElement(L.t,null,r.createElement(f,null,r.createElement(q,null),r.createElement(T,null),r.createElement($,null),r.createElement(D,{location:A(t)},e)),r.createElement(Z,null))))}var W=n(6887);const Y=function(e){try{return document.createElement("link").relList.supports(e)}catch{return!1}}("prefetch")?function(e){return new Promise(((t,n)=>{if("undefined"==typeof document)return void n();const r=document.createElement("link");r.setAttribute("rel","prefetch"),r.setAttribute("href",e),r.onload=()=>t(),r.onerror=()=>n();const a=document.getElementsByTagName("head")[0]??document.getElementsByName("script")[0]?.parentNode;a?.appendChild(r)}))}:function(e){return new Promise(((t,n)=>{const r=new XMLHttpRequest;r.open("GET",e,!0),r.withCredentials=!0,r.onload=()=>{200===r.status?t():n()},r.send(null)}))};var K=n(9670);const Q=new Set,X=new Set,J=()=>navigator.connection?.effectiveType.includes("2g")||navigator.connection?.saveData,ee={prefetch(e){if(!(e=>!J()&&!X.has(e)&&!Q.has(e))(e))return!1;Q.add(e);const t=(0,d.f)(u.Z,e).flatMap((e=>{return t=e.route.path,Object.entries(W).filter((e=>{let[n]=e;return n.replace(/-[^-]+$/,"")===t})).flatMap((e=>{let[,t]=e;return Object.values((0,K.Z)(t))}));var t}));return Promise.all(t.map((e=>{const t=n.gca(e);return t&&!t.includes("undefined")?Y(t).catch((()=>{})):Promise.resolve()})))},preload:e=>!!(e=>!J()&&!X.has(e))(e)&&(X.add(e),O(e))},te=Object.freeze(ee);if(l.Z.canUseDOM){window.docusaurus=te;const e=a.hydrate;O(window.location.pathname).then((()=>{e(r.createElement(i.B6,null,r.createElement(o.VK,null,r.createElement(V,null))),document.getElementById("__docusaurus"))}))}},8940:(e,t,n)=>{"use strict";n.d(t,{_:()=>c,M:()=>d});var r=n(7294),a=n(6809);const o=JSON.parse('{"docusaurus-plugin-content-docs":{"default":{"path":"/articles/docs","versions":[{"name":"current","label":"Next","isLast":true,"path":"/articles/docs","mainDocId":"changes","docs":[{"id":"changes","path":"/articles/docs/changes","sidebar":"tutorialSidebar"}],"draftIds":[],"sidebars":{"tutorialSidebar":{"link":{"path":"/articles/docs/changes","label":"changes"}}}}],"breadcrumbs":true}}}'),i=JSON.parse('{"defaultLocale":"en","locales":["en"],"path":"i18n","currentLocale":"en","localeConfigs":{"en":{"label":"English","direction":"ltr","htmlLang":"en","calendar":"gregory","path":"en"}}}');var l=n(7529);const s=JSON.parse('{"docusaurusVersion":"2.4.3","siteVersion":"0.0.0","pluginVersions":{"docusaurus-plugin-content-docs":{"type":"package","name":"@docusaurus/plugin-content-docs","version":"2.4.3"},"docusaurus-plugin-content-blog":{"type":"package","name":"@docusaurus/plugin-content-blog","version":"2.4.3"},"docusaurus-plugin-content-pages":{"type":"package","name":"@docusaurus/plugin-content-pages","version":"2.4.3"},"docusaurus-plugin-sitemap":{"type":"package","name":"@docusaurus/plugin-sitemap","version":"2.4.3"},"docusaurus-theme-classic":{"type":"package","name":"@docusaurus/theme-classic","version":"2.4.3"}}}'),u={siteConfig:a.default,siteMetadata:s,globalData:o,i18n:i,codeTranslations:l},c=r.createContext(u);function d(e){let{children:t}=e;return r.createElement(c.Provider,{value:u},t)}},4763:(e,t,n)=>{"use strict";n.d(t,{Z:()=>f});var r=n(7294),a=n(412),o=n(5742),i=n(8780),l=n(7961);function s(e){let{error:t,tryAgain:n}=e;return r.createElement("div",{style:{display:"flex",flexDirection:"column",justifyContent:"center",alignItems:"flex-start",minHeight:"100vh",width:"100%",maxWidth:"80ch",fontSize:"20px",margin:"0 auto",padding:"1rem"}},r.createElement("h1",{style:{fontSize:"3rem"}},"This page crashed"),r.createElement("button",{type:"button",onClick:n,style:{margin:"1rem 0",fontSize:"2rem",cursor:"pointer",borderRadius:20,padding:"1rem"}},"Try again"),r.createElement(u,{error:t}))}function u(e){let{error:t}=e;const n=(0,i.getErrorCausalChain)(t).map((e=>e.message)).join("\n\nCause:\n");return r.createElement("p",{style:{whiteSpace:"pre-wrap"}},n)}function c(e){let{error:t,tryAgain:n}=e;return r.createElement(f,{fallback:()=>r.createElement(s,{error:t,tryAgain:n})},r.createElement(o.Z,null,r.createElement("title",null,"Page Error")),r.createElement(l.Z,null,r.createElement(s,{error:t,tryAgain:n})))}const d=e=>r.createElement(c,e);class f extends r.Component{constructor(e){super(e),this.state={error:null}}componentDidCatch(e){a.Z.canUseDOM&&this.setState({error:e})}render(){const{children:e}=this.props,{error:t}=this.state;if(t){const e={error:t,tryAgain:()=>this.setState({error:null})};return(this.props.fallback??d)(e)}return e??null}}},412:(e,t,n)=>{"use strict";n.d(t,{Z:()=>a});const r="undefined"!=typeof window&&"document"in window&&"createElement"in window.document,a={canUseDOM:r,canUseEventListeners:r&&("addEventListener"in window||"attachEvent"in window),canUseIntersectionObserver:r&&"IntersectionObserver"in window,canUseViewport:r&&"screen"in window}},5742:(e,t,n)=>{"use strict";n.d(t,{Z:()=>o});var r=n(7294),a=n(405);function o(e){return r.createElement(a.ql,e)}},9960:(e,t,n)=>{"use strict";n.d(t,{Z:()=>p});var r=n(7462),a=n(7294),o=n(3727),i=n(8780),l=n(2263),s=n(3919),u=n(412);const c=a.createContext({collectLink:()=>{}});var d=n(4996);function f(e,t){let{isNavLink:n,to:f,href:p,activeClassName:m,isActive:h,"data-noBrokenLinkCheck":g,autoAddBaseUrl:v=!0,...b}=e;const{siteConfig:{trailingSlash:y,baseUrl:w}}=(0,l.Z)(),{withBaseUrl:k}=(0,d.C)(),E=(0,a.useContext)(c),S=(0,a.useRef)(null);(0,a.useImperativeHandle)(t,(()=>S.current));const x=f||p;const C=(0,s.Z)(x),T=x?.replace("pathname://","");let _=void 0!==T?(A=T,v&&(e=>e.startsWith("/"))(A)?k(A):A):void 0;var A;_&&C&&(_=(0,i.applyTrailingSlash)(_,{trailingSlash:y,baseUrl:w}));const L=(0,a.useRef)(!1),R=n?o.OL:o.rU,N=u.Z.canUseIntersectionObserver,P=(0,a.useRef)(),O=()=>{L.current||null==_||(window.docusaurus.preload(_),L.current=!0)};(0,a.useEffect)((()=>(!N&&C&&null!=_&&window.docusaurus.prefetch(_),()=>{N&&P.current&&P.current.disconnect()})),[P,_,N,C]);const I=_?.startsWith("#")??!1,D=!_||!C||I;return D||g||E.collectLink(_),D?a.createElement("a",(0,r.Z)({ref:S,href:_},x&&!C&&{target:"_blank",rel:"noopener noreferrer"},b)):a.createElement(R,(0,r.Z)({},b,{onMouseEnter:O,onTouchStart:O,innerRef:e=>{S.current=e,N&&e&&C&&(P.current=new window.IntersectionObserver((t=>{t.forEach((t=>{e===t.target&&(t.isIntersecting||t.intersectionRatio>0)&&(P.current.unobserve(e),P.current.disconnect(),null!=_&&window.docusaurus.prefetch(_))}))})),P.current.observe(e))},to:_},n&&{isActive:h,activeClassName:m}))}const p=a.forwardRef(f)},1875:(e,t,n)=>{"use strict";n.d(t,{Z:()=>r});const r=()=>null},5999:(e,t,n)=>{"use strict";n.d(t,{Z:()=>s,I:()=>l});var r=n(7294);function a(e,t){const n=e.split(/(\{\w+\})/).map(((e,n)=>{if(n%2==1){const n=t?.[e.slice(1,-1)];if(void 0!==n)return n}return e}));return n.some((e=>(0,r.isValidElement)(e)))?n.map(((e,t)=>(0,r.isValidElement)(e)?r.cloneElement(e,{key:t}):e)).filter((e=>""!==e)):n.join("")}var o=n(7529);function i(e){let{id:t,message:n}=e;if(void 0===t&&void 0===n)throw new Error("Docusaurus translation declarations must have at least a translation id or a default translation message");return o[t??n]??n??t}function l(e,t){let{message:n,id:r}=e;return a(i({message:n,id:r}),t)}function s(e){let{children:t,id:n,values:o}=e;if(t&&"string"!=typeof t)throw console.warn("Illegal children",t),new Error("The Docusaurus component only accept simple string values");const l=i({message:t,id:n});return r.createElement(r.Fragment,null,a(l,o))}},9935:(e,t,n)=>{"use strict";n.d(t,{m:()=>r});const r="default"},3919:(e,t,n)=>{"use strict";function r(e){return/^(?:\w*:|\/\/)/.test(e)}function a(e){return void 0!==e&&!r(e)}n.d(t,{Z:()=>a,b:()=>r})},4996:(e,t,n)=>{"use strict";n.d(t,{C:()=>i,Z:()=>l});var r=n(7294),a=n(2263),o=n(3919);function i(){const{siteConfig:{baseUrl:e,url:t}}=(0,a.Z)(),n=(0,r.useCallback)(((n,r)=>function(e,t,n,r){let{forcePrependBaseUrl:a=!1,absolute:i=!1}=void 0===r?{}:r;if(!n||n.startsWith("#")||(0,o.b)(n))return n;if(a)return t+n.replace(/^\//,"");if(n===t.replace(/\/$/,""))return t;const l=n.startsWith(t)?n:t+n.replace(/^\//,"");return i?e+l:l}(t,e,n,r)),[t,e]);return{withBaseUrl:n}}function l(e,t){void 0===t&&(t={});const{withBaseUrl:n}=i();return n(e,t)}},2263:(e,t,n)=>{"use strict";n.d(t,{Z:()=>o});var r=n(7294),a=n(8940);function o(){return(0,r.useContext)(a._)}},2389:(e,t,n)=>{"use strict";n.d(t,{Z:()=>o});var r=n(7294),a=n(8934);function o(){return(0,r.useContext)(a._)}},9670:(e,t,n)=>{"use strict";n.d(t,{Z:()=>a});const r=e=>"object"==typeof e&&!!e&&Object.keys(e).length>0;function a(e){const t={};return function e(n,a){Object.entries(n).forEach((n=>{let[o,i]=n;const l=a?`${a}.${o}`:o;r(i)?e(i,l):t[l]=i}))}(e),t}},226:(e,t,n)=>{"use strict";n.d(t,{_:()=>a,z:()=>o});var r=n(7294);const a=r.createContext(null);function o(e){let{children:t,value:n}=e;const o=r.useContext(a),i=(0,r.useMemo)((()=>function(e){let{parent:t,value:n}=e;if(!t){if(!n)throw new Error("Unexpected: no Docusaurus route context found");if(!("plugin"in n))throw new Error("Unexpected: Docusaurus topmost route context has no `plugin` attribute");return n}const r={...t.data,...n?.data};return{plugin:t.plugin,data:r}}({parent:o,value:n})),[o,n]);return r.createElement(a.Provider,{value:i},t)}},143:(e,t,n)=>{"use strict";n.d(t,{Iw:()=>h,gA:()=>f,_r:()=>c,Jo:()=>g,zh:()=>d,yW:()=>m,gB:()=>p});var r=n(6550),a=n(2263),o=n(9935);function i(e,t){void 0===t&&(t={});const n=function(){const{globalData:e}=(0,a.Z)();return e}()[e];if(!n&&t.failfast)throw new Error(`Docusaurus plugin global data not found for "${e}" plugin.`);return n}const l=e=>e.versions.find((e=>e.isLast));function s(e,t){const n=function(e,t){const n=l(e);return[...e.versions.filter((e=>e!==n)),n].find((e=>!!(0,r.LX)(t,{path:e.path,exact:!1,strict:!1})))}(e,t),a=n?.docs.find((e=>!!(0,r.LX)(t,{path:e.path,exact:!0,strict:!1})));return{activeVersion:n,activeDoc:a,alternateDocVersions:a?function(t){const n={};return e.versions.forEach((e=>{e.docs.forEach((r=>{r.id===t&&(n[e.name]=r)}))})),n}(a.id):{}}}const u={},c=()=>i("docusaurus-plugin-content-docs")??u,d=e=>function(e,t,n){void 0===t&&(t=o.m),void 0===n&&(n={});const r=i(e),a=r?.[t];if(!a&&n.failfast)throw new Error(`Docusaurus plugin global data not found for "${e}" plugin with id "${t}".`);return a}("docusaurus-plugin-content-docs",e,{failfast:!0});function f(e){void 0===e&&(e={});const t=c(),{pathname:n}=(0,r.TH)();return function(e,t,n){void 0===n&&(n={});const a=Object.entries(e).sort(((e,t)=>t[1].path.localeCompare(e[1].path))).find((e=>{let[,n]=e;return!!(0,r.LX)(t,{path:n.path,exact:!1,strict:!1})})),o=a?{pluginId:a[0],pluginData:a[1]}:void 0;if(!o&&n.failfast)throw new Error(`Can't find active docs plugin for "${t}" pathname, while it was expected to be found. Maybe you tried to use a docs feature that can only be used on a docs-related page? Existing docs plugin paths are: ${Object.values(e).map((e=>e.path)).join(", ")}`);return o}(t,n,e)}function p(e){return d(e).versions}function m(e){const t=d(e);return l(t)}function h(e){const t=d(e),{pathname:n}=(0,r.TH)();return s(t,n)}function g(e){const t=d(e),{pathname:n}=(0,r.TH)();return function(e,t){const n=l(e);return{latestDocSuggestion:s(e,t).alternateDocVersions[n.name],latestVersionSuggestion:n}}(t,n)}},8320:(e,t,n)=>{"use strict";n.r(t),n.d(t,{default:()=>o});var r=n(4865),a=n.n(r);a().configure({showSpinner:!1});const o={onRouteUpdate(e){let{location:t,previousLocation:n}=e;if(n&&t.pathname!==n.pathname){const e=window.setTimeout((()=>{a().start()}),200);return()=>window.clearTimeout(e)}},onRouteDidUpdate(){a().done()}}},3310:(e,t,n)=>{"use strict";n.r(t);var r=n(7410),a=n(6809);!function(e){const{themeConfig:{prism:t}}=a.default,{additionalLanguages:r}=t;globalThis.Prism=e,r.forEach((e=>{n(6726)(`./prism-${e}`)})),delete globalThis.Prism}(r.Z)},9471:(e,t,n)=>{"use strict";n.d(t,{Z:()=>o});var r=n(7294);const a={iconExternalLink:"iconExternalLink_nPIU"};function o(e){let{width:t=13.5,height:n=13.5}=e;return r.createElement("svg",{width:t,height:n,"aria-hidden":"true",viewBox:"0 0 24 24",className:a.iconExternalLink},r.createElement("path",{fill:"currentColor",d:"M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"}))}},7961:(e,t,n)=>{"use strict";n.d(t,{Z:()=>dt});var r=n(7294),a=n(6010),o=n(4763),i=n(1944),l=n(7462),s=n(6550),u=n(5999),c=n(5936);const d="__docusaurus_skipToContent_fallback";function f(e){e.setAttribute("tabindex","-1"),e.focus(),e.removeAttribute("tabindex")}function p(){const e=(0,r.useRef)(null),{action:t}=(0,s.k6)(),n=(0,r.useCallback)((e=>{e.preventDefault();const t=document.querySelector("main:first-of-type")??document.getElementById(d);t&&f(t)}),[]);return(0,c.S)((n=>{let{location:r}=n;e.current&&!r.hash&&"PUSH"===t&&f(e.current)})),{containerRef:e,onClick:n}}const m=(0,u.I)({id:"theme.common.skipToMainContent",description:"The skip to content label used for accessibility, allowing to rapidly navigate to main content with keyboard tab/enter navigation",message:"Skip to main content"});function h(e){const t=e.children??m,{containerRef:n,onClick:a}=p();return r.createElement("div",{ref:n,role:"region","aria-label":m},r.createElement("a",(0,l.Z)({},e,{href:`#${d}`,onClick:a}),t))}var g=n(5281),v=n(9727);const b={skipToContent:"skipToContent_fXgn"};function y(){return r.createElement(h,{className:b.skipToContent})}var w=n(6668),k=n(9689);function E(e){let{width:t=21,height:n=21,color:a="currentColor",strokeWidth:o=1.2,className:i,...s}=e;return r.createElement("svg",(0,l.Z)({viewBox:"0 0 15 15",width:t,height:n},s),r.createElement("g",{stroke:a,strokeWidth:o},r.createElement("path",{d:"M.75.75l13.5 13.5M14.25.75L.75 14.25"})))}const S={closeButton:"closeButton_CVFx"};function x(e){return r.createElement("button",(0,l.Z)({type:"button","aria-label":(0,u.I)({id:"theme.AnnouncementBar.closeButtonAriaLabel",message:"Close",description:"The ARIA label for close button of announcement bar"})},e,{className:(0,a.Z)("clean-btn close",S.closeButton,e.className)}),r.createElement(E,{width:14,height:14,strokeWidth:3.1}))}const C={content:"content_knG7"};function T(e){const{announcementBar:t}=(0,w.L)(),{content:n}=t;return r.createElement("div",(0,l.Z)({},e,{className:(0,a.Z)(C.content,e.className),dangerouslySetInnerHTML:{__html:n}}))}const _={announcementBar:"announcementBar_mb4j",announcementBarPlaceholder:"announcementBarPlaceholder_vyr4",announcementBarClose:"announcementBarClose_gvF7",announcementBarContent:"announcementBarContent_xLdY"};function A(){const{announcementBar:e}=(0,w.L)(),{isActive:t,close:n}=(0,k.nT)();if(!t)return null;const{backgroundColor:a,textColor:o,isCloseable:i}=e;return r.createElement("div",{className:_.announcementBar,style:{backgroundColor:a,color:o},role:"banner"},i&&r.createElement("div",{className:_.announcementBarPlaceholder}),r.createElement(T,{className:_.announcementBarContent}),i&&r.createElement(x,{onClick:n,className:_.announcementBarClose}))}var L=n(2961),R=n(2466);var N=n(902),P=n(3102);const O=r.createContext(null);function I(e){let{children:t}=e;const n=function(){const e=(0,L.e)(),t=(0,P.HY)(),[n,a]=(0,r.useState)(!1),o=null!==t.component,i=(0,N.D9)(o);return(0,r.useEffect)((()=>{o&&!i&&a(!0)}),[o,i]),(0,r.useEffect)((()=>{o?e.shown||a(!0):a(!1)}),[e.shown,o]),(0,r.useMemo)((()=>[n,a]),[n])}();return r.createElement(O.Provider,{value:n},t)}function D(e){if(e.component){const t=e.component;return r.createElement(t,e.props)}}function M(){const e=(0,r.useContext)(O);if(!e)throw new N.i6("NavbarSecondaryMenuDisplayProvider");const[t,n]=e,a=(0,r.useCallback)((()=>n(!1)),[n]),o=(0,P.HY)();return(0,r.useMemo)((()=>({shown:t,hide:a,content:D(o)})),[a,o,t])}function F(e){let{header:t,primaryMenu:n,secondaryMenu:o}=e;const{shown:i}=M();return r.createElement("div",{className:"navbar-sidebar"},t,r.createElement("div",{className:(0,a.Z)("navbar-sidebar__items",{"navbar-sidebar__items--show-secondary":i})},r.createElement("div",{className:"navbar-sidebar__item menu"},n),r.createElement("div",{className:"navbar-sidebar__item menu"},o)))}var B=n(2949),j=n(2389);function z(e){return r.createElement("svg",(0,l.Z)({viewBox:"0 0 24 24",width:24,height:24},e),r.createElement("path",{fill:"currentColor",d:"M12,9c1.65,0,3,1.35,3,3s-1.35,3-3,3s-3-1.35-3-3S10.35,9,12,9 M12,7c-2.76,0-5,2.24-5,5s2.24,5,5,5s5-2.24,5-5 S14.76,7,12,7L12,7z M2,13l2,0c0.55,0,1-0.45,1-1s-0.45-1-1-1l-2,0c-0.55,0-1,0.45-1,1S1.45,13,2,13z M20,13l2,0c0.55,0,1-0.45,1-1 s-0.45-1-1-1l-2,0c-0.55,0-1,0.45-1,1S19.45,13,20,13z M11,2v2c0,0.55,0.45,1,1,1s1-0.45,1-1V2c0-0.55-0.45-1-1-1S11,1.45,11,2z M11,20v2c0,0.55,0.45,1,1,1s1-0.45,1-1v-2c0-0.55-0.45-1-1-1C11.45,19,11,19.45,11,20z M5.99,4.58c-0.39-0.39-1.03-0.39-1.41,0 c-0.39,0.39-0.39,1.03,0,1.41l1.06,1.06c0.39,0.39,1.03,0.39,1.41,0s0.39-1.03,0-1.41L5.99,4.58z M18.36,16.95 c-0.39-0.39-1.03-0.39-1.41,0c-0.39,0.39-0.39,1.03,0,1.41l1.06,1.06c0.39,0.39,1.03,0.39,1.41,0c0.39-0.39,0.39-1.03,0-1.41 L18.36,16.95z M19.42,5.99c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06c-0.39,0.39-0.39,1.03,0,1.41 s1.03,0.39,1.41,0L19.42,5.99z M7.05,18.36c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06 c-0.39,0.39-0.39,1.03,0,1.41s1.03,0.39,1.41,0L7.05,18.36z"}))}function U(e){return r.createElement("svg",(0,l.Z)({viewBox:"0 0 24 24",width:24,height:24},e),r.createElement("path",{fill:"currentColor",d:"M9.37,5.51C9.19,6.15,9.1,6.82,9.1,7.5c0,4.08,3.32,7.4,7.4,7.4c0.68,0,1.35-0.09,1.99-0.27C17.45,17.19,14.93,19,12,19 c-3.86,0-7-3.14-7-7C5,9.07,6.81,6.55,9.37,5.51z M12,3c-4.97,0-9,4.03-9,9s4.03,9,9,9s9-4.03,9-9c0-0.46-0.04-0.92-0.1-1.36 c-0.98,1.37-2.58,2.26-4.4,2.26c-2.98,0-5.4-2.42-5.4-5.4c0-1.81,0.89-3.42,2.26-4.4C12.92,3.04,12.46,3,12,3L12,3z"}))}const $={toggle:"toggle_vylO",toggleButton:"toggleButton_gllP",darkToggleIcon:"darkToggleIcon_wfgR",lightToggleIcon:"lightToggleIcon_pyhR",toggleButtonDisabled:"toggleButtonDisabled_aARS"};function q(e){let{className:t,buttonClassName:n,value:o,onChange:i}=e;const l=(0,j.Z)(),s=(0,u.I)({message:"Switch between dark and light mode (currently {mode})",id:"theme.colorToggle.ariaLabel",description:"The ARIA label for the navbar color mode toggle"},{mode:"dark"===o?(0,u.I)({message:"dark mode",id:"theme.colorToggle.ariaLabel.mode.dark",description:"The name for the dark color mode"}):(0,u.I)({message:"light mode",id:"theme.colorToggle.ariaLabel.mode.light",description:"The name for the light color mode"})});return r.createElement("div",{className:(0,a.Z)($.toggle,t)},r.createElement("button",{className:(0,a.Z)("clean-btn",$.toggleButton,!l&&$.toggleButtonDisabled,n),type:"button",onClick:()=>i("dark"===o?"light":"dark"),disabled:!l,title:s,"aria-label":s,"aria-live":"polite"},r.createElement(z,{className:(0,a.Z)($.toggleIcon,$.lightToggleIcon)}),r.createElement(U,{className:(0,a.Z)($.toggleIcon,$.darkToggleIcon)})))}const H=r.memo(q),G={darkNavbarColorModeToggle:"darkNavbarColorModeToggle_X3D1"};function Z(e){let{className:t}=e;const n=(0,w.L)().navbar.style,a=(0,w.L)().colorMode.disableSwitch,{colorMode:o,setColorMode:i}=(0,B.I)();return a?null:r.createElement(H,{className:t,buttonClassName:"dark"===n?G.darkNavbarColorModeToggle:void 0,value:o,onChange:i})}var V=n(1327);function W(){return r.createElement(V.Z,{className:"navbar__brand",imageClassName:"navbar__logo",titleClassName:"navbar__title text--truncate"})}function Y(){const e=(0,L.e)();return r.createElement("button",{type:"button","aria-label":(0,u.I)({id:"theme.docs.sidebar.closeSidebarButtonAriaLabel",message:"Close navigation bar",description:"The ARIA label for close button of mobile sidebar"}),className:"clean-btn navbar-sidebar__close",onClick:()=>e.toggle()},r.createElement(E,{color:"var(--ifm-color-emphasis-600)"}))}function K(){return r.createElement("div",{className:"navbar-sidebar__brand"},r.createElement(W,null),r.createElement(Z,{className:"margin-right--md"}),r.createElement(Y,null))}var Q=n(9960),X=n(4996),J=n(3919);function ee(e,t){return void 0!==e&&void 0!==t&&new RegExp(e,"gi").test(t)}var te=n(9471);function ne(e){let{activeBasePath:t,activeBaseRegex:n,to:a,href:o,label:i,html:s,isDropdownLink:u,prependBaseUrlToHref:c,...d}=e;const f=(0,X.Z)(a),p=(0,X.Z)(t),m=(0,X.Z)(o,{forcePrependBaseUrl:!0}),h=i&&o&&!(0,J.Z)(o),g=s?{dangerouslySetInnerHTML:{__html:s}}:{children:r.createElement(r.Fragment,null,i,h&&r.createElement(te.Z,u&&{width:12,height:12}))};return o?r.createElement(Q.Z,(0,l.Z)({href:c?m:o},d,g)):r.createElement(Q.Z,(0,l.Z)({to:f,isNavLink:!0},(t||n)&&{isActive:(e,t)=>n?ee(n,t.pathname):t.pathname.startsWith(p)},d,g))}function re(e){let{className:t,isDropdownItem:n=!1,...o}=e;const i=r.createElement(ne,(0,l.Z)({className:(0,a.Z)(n?"dropdown__link":"navbar__item navbar__link",t),isDropdownLink:n},o));return n?r.createElement("li",null,i):i}function ae(e){let{className:t,isDropdownItem:n,...o}=e;return r.createElement("li",{className:"menu__list-item"},r.createElement(ne,(0,l.Z)({className:(0,a.Z)("menu__link",t)},o)))}function oe(e){let{mobile:t=!1,position:n,...a}=e;const o=t?ae:re;return r.createElement(o,(0,l.Z)({},a,{activeClassName:a.activeClassName??(t?"menu__link--active":"navbar__link--active")}))}var ie=n(6043),le=n(8596),se=n(2263);function ue(e,t){return e.some((e=>function(e,t){return!!(0,le.Mg)(e.to,t)||!!ee(e.activeBaseRegex,t)||!(!e.activeBasePath||!t.startsWith(e.activeBasePath))}(e,t)))}function ce(e){let{items:t,position:n,className:o,onClick:i,...s}=e;const u=(0,r.useRef)(null),[c,d]=(0,r.useState)(!1);return(0,r.useEffect)((()=>{const e=e=>{u.current&&!u.current.contains(e.target)&&d(!1)};return document.addEventListener("mousedown",e),document.addEventListener("touchstart",e),document.addEventListener("focusin",e),()=>{document.removeEventListener("mousedown",e),document.removeEventListener("touchstart",e),document.removeEventListener("focusin",e)}}),[u]),r.createElement("div",{ref:u,className:(0,a.Z)("navbar__item","dropdown","dropdown--hoverable",{"dropdown--right":"right"===n,"dropdown--show":c})},r.createElement(ne,(0,l.Z)({"aria-haspopup":"true","aria-expanded":c,role:"button",href:s.to?void 0:"#",className:(0,a.Z)("navbar__link",o)},s,{onClick:s.to?void 0:e=>e.preventDefault(),onKeyDown:e=>{"Enter"===e.key&&(e.preventDefault(),d(!c))}}),s.children??s.label),r.createElement("ul",{className:"dropdown__menu"},t.map(((e,t)=>r.createElement(xe,(0,l.Z)({isDropdownItem:!0,activeClassName:"dropdown__link--active"},e,{key:t}))))))}function de(e){let{items:t,className:n,position:o,onClick:i,...u}=e;const c=function(){const{siteConfig:{baseUrl:e}}=(0,se.Z)(),{pathname:t}=(0,s.TH)();return t.replace(e,"/")}(),d=ue(t,c),{collapsed:f,toggleCollapsed:p,setCollapsed:m}=(0,ie.u)({initialState:()=>!d});return(0,r.useEffect)((()=>{d&&m(!d)}),[c,d,m]),r.createElement("li",{className:(0,a.Z)("menu__list-item",{"menu__list-item--collapsed":f})},r.createElement(ne,(0,l.Z)({role:"button",className:(0,a.Z)("menu__link menu__link--sublist menu__link--sublist-caret",n)},u,{onClick:e=>{e.preventDefault(),p()}}),u.children??u.label),r.createElement(ie.z,{lazy:!0,as:"ul",className:"menu__list",collapsed:f},t.map(((e,t)=>r.createElement(xe,(0,l.Z)({mobile:!0,isDropdownItem:!0,onClick:i,activeClassName:"menu__link--active"},e,{key:t}))))))}function fe(e){let{mobile:t=!1,...n}=e;const a=t?de:ce;return r.createElement(a,n)}var pe=n(4711);function me(e){let{width:t=20,height:n=20,...a}=e;return r.createElement("svg",(0,l.Z)({viewBox:"0 0 24 24",width:t,height:n,"aria-hidden":!0},a),r.createElement("path",{fill:"currentColor",d:"M12.87 15.07l-2.54-2.51.03-.03c1.74-1.94 2.98-4.17 3.71-6.53H17V4h-7V2H8v2H1v1.99h11.17C11.5 7.92 10.44 9.75 9 11.35 8.07 10.32 7.3 9.19 6.69 8h-2c.73 1.63 1.73 3.17 2.98 4.56l-5.09 5.02L4 19l5-5 3.11 3.11.76-2.04zM18.5 10h-2L12 22h2l1.12-3h4.75L21 22h2l-4.5-12zm-2.62 7l1.62-4.33L19.12 17h-3.24z"}))}const he="iconLanguage_nlXk";var ge=n(1875);const ve={searchBox:"searchBox_ZlJk"};function be(e){let{children:t,className:n}=e;return r.createElement("div",{className:(0,a.Z)(n,ve.searchBox)},t)}var ye=n(143),we=n(2802);var ke=n(373);const Ee=e=>e.docs.find((t=>t.id===e.mainDocId));const Se={default:oe,localeDropdown:function(e){let{mobile:t,dropdownItemsBefore:n,dropdownItemsAfter:a,...o}=e;const{i18n:{currentLocale:i,locales:c,localeConfigs:d}}=(0,se.Z)(),f=(0,pe.l)(),{search:p,hash:m}=(0,s.TH)(),h=[...n,...c.map((e=>{const n=`${`pathname://${f.createUrl({locale:e,fullyQualified:!1})}`}${p}${m}`;return{label:d[e].label,lang:d[e].htmlLang,to:n,target:"_self",autoAddBaseUrl:!1,className:e===i?t?"menu__link--active":"dropdown__link--active":""}})),...a],g=t?(0,u.I)({message:"Languages",id:"theme.navbar.mobileLanguageDropdown.label",description:"The label for the mobile language switcher dropdown"}):d[i].label;return r.createElement(fe,(0,l.Z)({},o,{mobile:t,label:r.createElement(r.Fragment,null,r.createElement(me,{className:he}),g),items:h}))},search:function(e){let{mobile:t,className:n}=e;return t?null:r.createElement(be,{className:n},r.createElement(ge.Z,null))},dropdown:fe,html:function(e){let{value:t,className:n,mobile:o=!1,isDropdownItem:i=!1}=e;const l=i?"li":"div";return r.createElement(l,{className:(0,a.Z)({navbar__item:!o&&!i,"menu__list-item":o},n),dangerouslySetInnerHTML:{__html:t}})},doc:function(e){let{docId:t,label:n,docsPluginId:a,...o}=e;const{activeDoc:i}=(0,ye.Iw)(a),s=(0,we.vY)(t,a);return null===s?null:r.createElement(oe,(0,l.Z)({exact:!0},o,{isActive:()=>i?.path===s.path||!!i?.sidebar&&i.sidebar===s.sidebar,label:n??s.id,to:s.path}))},docSidebar:function(e){let{sidebarId:t,label:n,docsPluginId:a,...o}=e;const{activeDoc:i}=(0,ye.Iw)(a),s=(0,we.oz)(t,a).link;if(!s)throw new Error(`DocSidebarNavbarItem: Sidebar with ID "${t}" doesn't have anything to be linked to.`);return r.createElement(oe,(0,l.Z)({exact:!0},o,{isActive:()=>i?.sidebar===t,label:n??s.label,to:s.path}))},docsVersion:function(e){let{label:t,to:n,docsPluginId:a,...o}=e;const i=(0,we.lO)(a)[0],s=t??i.label,u=n??(e=>e.docs.find((t=>t.id===e.mainDocId)))(i).path;return r.createElement(oe,(0,l.Z)({},o,{label:s,to:u}))},docsVersionDropdown:function(e){let{mobile:t,docsPluginId:n,dropdownActiveClassDisabled:a,dropdownItemsBefore:o,dropdownItemsAfter:i,...c}=e;const{search:d,hash:f}=(0,s.TH)(),p=(0,ye.Iw)(n),m=(0,ye.gB)(n),{savePreferredVersionName:h}=(0,ke.J)(n),g=[...o,...m.map((e=>{const t=p.alternateDocVersions[e.name]??Ee(e);return{label:e.label,to:`${t.path}${d}${f}`,isActive:()=>e===p.activeVersion,onClick:()=>h(e.name)}})),...i],v=(0,we.lO)(n)[0],b=t&&g.length>1?(0,u.I)({id:"theme.navbar.mobileVersionsDropdown.label",message:"Versions",description:"The label for the navbar versions dropdown on mobile view"}):v.label,y=t&&g.length>1?void 0:Ee(v).path;return g.length<=1?r.createElement(oe,(0,l.Z)({},c,{mobile:t,label:b,to:y,isActive:a?()=>!1:void 0})):r.createElement(fe,(0,l.Z)({},c,{mobile:t,label:b,to:y,items:g,isActive:a?()=>!1:void 0}))}};function xe(e){let{type:t,...n}=e;const a=function(e,t){return e&&"default"!==e?e:"items"in t?"dropdown":"default"}(t,n),o=Se[a];if(!o)throw new Error(`No NavbarItem component found for type "${t}".`);return r.createElement(o,n)}function Ce(){const e=(0,L.e)(),t=(0,w.L)().navbar.items;return r.createElement("ul",{className:"menu__list"},t.map(((t,n)=>r.createElement(xe,(0,l.Z)({mobile:!0},t,{onClick:()=>e.toggle(),key:n})))))}function Te(e){return r.createElement("button",(0,l.Z)({},e,{type:"button",className:"clean-btn navbar-sidebar__back"}),r.createElement(u.Z,{id:"theme.navbar.mobileSidebarSecondaryMenu.backButtonLabel",description:"The label of the back button to return to main menu, inside the mobile navbar sidebar secondary menu (notably used to display the docs sidebar)"},"\u2190 Back to main menu"))}function _e(){const e=0===(0,w.L)().navbar.items.length,t=M();return r.createElement(r.Fragment,null,!e&&r.createElement(Te,{onClick:()=>t.hide()}),t.content)}function Ae(){const e=(0,L.e)();var t;return void 0===(t=e.shown)&&(t=!0),(0,r.useEffect)((()=>(document.body.style.overflow=t?"hidden":"visible",()=>{document.body.style.overflow="visible"})),[t]),e.shouldRender?r.createElement(F,{header:r.createElement(K,null),primaryMenu:r.createElement(Ce,null),secondaryMenu:r.createElement(_e,null)}):null}const Le={navbarHideable:"navbarHideable_m1mJ",navbarHidden:"navbarHidden_jGov"};function Re(e){return r.createElement("div",(0,l.Z)({role:"presentation"},e,{className:(0,a.Z)("navbar-sidebar__backdrop",e.className)}))}function Ne(e){let{children:t}=e;const{navbar:{hideOnScroll:n,style:o}}=(0,w.L)(),i=(0,L.e)(),{navbarRef:l,isNavbarVisible:s}=function(e){const[t,n]=(0,r.useState)(e),a=(0,r.useRef)(!1),o=(0,r.useRef)(0),i=(0,r.useCallback)((e=>{null!==e&&(o.current=e.getBoundingClientRect().height)}),[]);return(0,R.RF)(((t,r)=>{let{scrollY:i}=t;if(!e)return;if(i=l?n(!1):i+u{if(!e)return;const r=t.location.hash;if(r?document.getElementById(r.substring(1)):void 0)return a.current=!0,void n(!1);n(!0)})),{navbarRef:i,isNavbarVisible:t}}(n);return r.createElement("nav",{ref:l,"aria-label":(0,u.I)({id:"theme.NavBar.navAriaLabel",message:"Main",description:"The ARIA label for the main navigation"}),className:(0,a.Z)("navbar","navbar--fixed-top",n&&[Le.navbarHideable,!s&&Le.navbarHidden],{"navbar--dark":"dark"===o,"navbar--primary":"primary"===o,"navbar-sidebar--show":i.shown})},t,r.createElement(Re,{onClick:i.toggle}),r.createElement(Ae,null))}var Pe=n(8780);const Oe={errorBoundaryError:"errorBoundaryError_a6uf"};function Ie(e){return r.createElement("button",(0,l.Z)({type:"button"},e),r.createElement(u.Z,{id:"theme.ErrorPageContent.tryAgain",description:"The label of the button to try again rendering when the React error boundary captures an error"},"Try again"))}function De(e){let{error:t}=e;const n=(0,Pe.getErrorCausalChain)(t).map((e=>e.message)).join("\n\nCause:\n");return r.createElement("p",{className:Oe.errorBoundaryError},n)}class Me extends r.Component{componentDidCatch(e,t){throw this.props.onError(e,t)}render(){return this.props.children}}const Fe="right";function Be(e){let{width:t=30,height:n=30,className:a,...o}=e;return r.createElement("svg",(0,l.Z)({className:a,width:t,height:n,viewBox:"0 0 30 30","aria-hidden":"true"},o),r.createElement("path",{stroke:"currentColor",strokeLinecap:"round",strokeMiterlimit:"10",strokeWidth:"2",d:"M4 7h22M4 15h22M4 23h22"}))}function je(){const{toggle:e,shown:t}=(0,L.e)();return r.createElement("button",{onClick:e,"aria-label":(0,u.I)({id:"theme.docs.sidebar.toggleSidebarButtonAriaLabel",message:"Toggle navigation bar",description:"The ARIA label for hamburger menu button of mobile navigation"}),"aria-expanded":t,className:"navbar__toggle clean-btn",type:"button"},r.createElement(Be,null))}const ze={colorModeToggle:"colorModeToggle_DEke"};function Ue(e){let{items:t}=e;return r.createElement(r.Fragment,null,t.map(((e,t)=>r.createElement(Me,{key:t,onError:t=>new Error(`A theme navbar item failed to render.\nPlease double-check the following navbar item (themeConfig.navbar.items) of your Docusaurus config:\n${JSON.stringify(e,null,2)}`,{cause:t})},r.createElement(xe,e)))))}function $e(e){let{left:t,right:n}=e;return r.createElement("div",{className:"navbar__inner"},r.createElement("div",{className:"navbar__items"},t),r.createElement("div",{className:"navbar__items navbar__items--right"},n))}function qe(){const e=(0,L.e)(),t=(0,w.L)().navbar.items,[n,a]=function(e){function t(e){return"left"===(e.position??Fe)}return[e.filter(t),e.filter((e=>!t(e)))]}(t),o=t.find((e=>"search"===e.type));return r.createElement($e,{left:r.createElement(r.Fragment,null,!e.disabled&&r.createElement(je,null),r.createElement(W,null),r.createElement(Ue,{items:n})),right:r.createElement(r.Fragment,null,r.createElement(Ue,{items:a}),r.createElement(Z,{className:ze.colorModeToggle}),!o&&r.createElement(be,null,r.createElement(ge.Z,null)))})}function He(){return r.createElement(Ne,null,r.createElement(qe,null))}function Ge(e){let{item:t}=e;const{to:n,href:a,label:o,prependBaseUrlToHref:i,...s}=t,u=(0,X.Z)(n),c=(0,X.Z)(a,{forcePrependBaseUrl:!0});return r.createElement(Q.Z,(0,l.Z)({className:"footer__link-item"},a?{href:i?c:a}:{to:u},s),o,a&&!(0,J.Z)(a)&&r.createElement(te.Z,null))}function Ze(e){let{item:t}=e;return t.html?r.createElement("li",{className:"footer__item",dangerouslySetInnerHTML:{__html:t.html}}):r.createElement("li",{key:t.href??t.to,className:"footer__item"},r.createElement(Ge,{item:t}))}function Ve(e){let{column:t}=e;return r.createElement("div",{className:"col footer__col"},r.createElement("div",{className:"footer__title"},t.title),r.createElement("ul",{className:"footer__items clean-list"},t.items.map(((e,t)=>r.createElement(Ze,{key:t,item:e})))))}function We(e){let{columns:t}=e;return r.createElement("div",{className:"row footer__links"},t.map(((e,t)=>r.createElement(Ve,{key:t,column:e}))))}function Ye(){return r.createElement("span",{className:"footer__link-separator"},"\xb7")}function Ke(e){let{item:t}=e;return t.html?r.createElement("span",{className:"footer__link-item",dangerouslySetInnerHTML:{__html:t.html}}):r.createElement(Ge,{item:t})}function Qe(e){let{links:t}=e;return r.createElement("div",{className:"footer__links text--center"},r.createElement("div",{className:"footer__links"},t.map(((e,n)=>r.createElement(r.Fragment,{key:n},r.createElement(Ke,{item:e}),t.length!==n+1&&r.createElement(Ye,null))))))}function Xe(e){let{links:t}=e;return function(e){return"title"in e[0]}(t)?r.createElement(We,{columns:t}):r.createElement(Qe,{links:t})}var Je=n(941);const et={footerLogoLink:"footerLogoLink_BH7S"};function tt(e){let{logo:t}=e;const{withBaseUrl:n}=(0,X.C)(),o={light:n(t.src),dark:n(t.srcDark??t.src)};return r.createElement(Je.Z,{className:(0,a.Z)("footer__logo",t.className),alt:t.alt,sources:o,width:t.width,height:t.height,style:t.style})}function nt(e){let{logo:t}=e;return t.href?r.createElement(Q.Z,{href:t.href,className:et.footerLogoLink,target:t.target},r.createElement(tt,{logo:t})):r.createElement(tt,{logo:t})}function rt(e){let{copyright:t}=e;return r.createElement("div",{className:"footer__copyright",dangerouslySetInnerHTML:{__html:t}})}function at(e){let{style:t,links:n,logo:o,copyright:i}=e;return r.createElement("footer",{className:(0,a.Z)("footer",{"footer--dark":"dark"===t})},r.createElement("div",{className:"container container-fluid"},n,(o||i)&&r.createElement("div",{className:"footer__bottom text--center"},o&&r.createElement("div",{className:"margin-bottom--sm"},o),i)))}function ot(){const{footer:e}=(0,w.L)();if(!e)return null;const{copyright:t,links:n,logo:a,style:o}=e;return r.createElement(at,{style:o,links:n&&n.length>0&&r.createElement(Xe,{links:n}),logo:a&&r.createElement(nt,{logo:a}),copyright:t&&r.createElement(rt,{copyright:t})})}const it=r.memo(ot),lt=(0,N.Qc)([B.S,k.pl,R.OC,ke.L5,i.VC,function(e){let{children:t}=e;return r.createElement(P.n2,null,r.createElement(L.M,null,r.createElement(I,null,t)))}]);function st(e){let{children:t}=e;return r.createElement(lt,null,t)}function ut(e){let{error:t,tryAgain:n}=e;return r.createElement("main",{className:"container margin-vert--xl"},r.createElement("div",{className:"row"},r.createElement("div",{className:"col col--6 col--offset-3"},r.createElement("h1",{className:"hero__title"},r.createElement(u.Z,{id:"theme.ErrorPageContent.title",description:"The title of the fallback page when the page crashed"},"This page crashed.")),r.createElement("div",{className:"margin-vert--lg"},r.createElement(Ie,{onClick:n,className:"button button--primary shadow--lw"})),r.createElement("hr",null),r.createElement("div",{className:"margin-vert--md"},r.createElement(De,{error:t})))))}const ct={mainWrapper:"mainWrapper_z2l0"};function dt(e){const{children:t,noFooter:n,wrapperClassName:l,title:s,description:u}=e;return(0,v.t)(),r.createElement(st,null,r.createElement(i.d,{title:s,description:u}),r.createElement(y,null),r.createElement(A,null),r.createElement(He,null),r.createElement("div",{id:d,className:(0,a.Z)(g.k.wrapper.main,ct.mainWrapper,l)},r.createElement(o.Z,{fallback:e=>r.createElement(ut,e)},t)),!n&&r.createElement(it,null))}},1327:(e,t,n)=>{"use strict";n.d(t,{Z:()=>d});var r=n(7462),a=n(7294),o=n(9960),i=n(4996),l=n(2263),s=n(6668),u=n(941);function c(e){let{logo:t,alt:n,imageClassName:r}=e;const o={light:(0,i.Z)(t.src),dark:(0,i.Z)(t.srcDark||t.src)},l=a.createElement(u.Z,{className:t.className,sources:o,height:t.height,width:t.width,alt:n,style:t.style});return r?a.createElement("div",{className:r},l):l}function d(e){const{siteConfig:{title:t}}=(0,l.Z)(),{navbar:{title:n,logo:u}}=(0,s.L)(),{imageClassName:d,titleClassName:f,...p}=e,m=(0,i.Z)(u?.href||"/"),h=n?"":t,g=u?.alt??h;return a.createElement(o.Z,(0,r.Z)({to:m},p,u?.target&&{target:u.target}),u&&a.createElement(c,{logo:u,alt:g,imageClassName:d}),null!=n&&a.createElement("b",{className:f},n))}},197:(e,t,n)=>{"use strict";n.d(t,{Z:()=>o});var r=n(7294),a=n(5742);function o(e){let{locale:t,version:n,tag:o}=e;const i=t;return r.createElement(a.Z,null,t&&r.createElement("meta",{name:"docusaurus_locale",content:t}),n&&r.createElement("meta",{name:"docusaurus_version",content:n}),o&&r.createElement("meta",{name:"docusaurus_tag",content:o}),i&&r.createElement("meta",{name:"docsearch:language",content:i}),n&&r.createElement("meta",{name:"docsearch:version",content:n}),o&&r.createElement("meta",{name:"docsearch:docusaurus_tag",content:o}))}},941:(e,t,n)=>{"use strict";n.d(t,{Z:()=>u});var r=n(7462),a=n(7294),o=n(6010),i=n(2389),l=n(2949);const s={themedImage:"themedImage_ToTc","themedImage--light":"themedImage--light_HNdA","themedImage--dark":"themedImage--dark_i4oU"};function u(e){const t=(0,i.Z)(),{colorMode:n}=(0,l.I)(),{sources:u,className:c,alt:d,...f}=e,p=t?"dark"===n?["dark"]:["light"]:["light","dark"];return a.createElement(a.Fragment,null,p.map((e=>a.createElement("img",(0,r.Z)({key:e,src:u[e],alt:d,className:(0,o.Z)(s.themedImage,s[`themedImage--${e}`],c)},f)))))}},6043:(e,t,n)=>{"use strict";n.d(t,{u:()=>s,z:()=>g});var r=n(7462),a=n(7294),o=n(412),i=n(1442);const l="ease-in-out";function s(e){let{initialState:t}=e;const[n,r]=(0,a.useState)(t??!1),o=(0,a.useCallback)((()=>{r((e=>!e))}),[]);return{collapsed:n,setCollapsed:r,toggleCollapsed:o}}const u={display:"none",overflow:"hidden",height:"0px"},c={display:"block",overflow:"visible",height:"auto"};function d(e,t){const n=t?u:c;e.style.display=n.display,e.style.overflow=n.overflow,e.style.height=n.height}function f(e){let{collapsibleRef:t,collapsed:n,animation:r}=e;const o=(0,a.useRef)(!1);(0,a.useEffect)((()=>{const e=t.current;function a(){const t=e.scrollHeight,n=r?.duration??function(e){if((0,i.n)())return 1;const t=e/36;return Math.round(10*(4+15*t**.25+t/5))}(t);return{transition:`height ${n}ms ${r?.easing??l}`,height:`${t}px`}}function s(){const t=a();e.style.transition=t.transition,e.style.height=t.height}if(!o.current)return d(e,n),void(o.current=!0);return e.style.willChange="height",function(){const t=requestAnimationFrame((()=>{n?(s(),requestAnimationFrame((()=>{e.style.height=u.height,e.style.overflow=u.overflow}))):(e.style.display="block",requestAnimationFrame((()=>{s()})))}));return()=>cancelAnimationFrame(t)}()}),[t,n,r])}function p(e){if(!o.Z.canUseDOM)return e?u:c}function m(e){let{as:t="div",collapsed:n,children:r,animation:o,onCollapseTransitionEnd:i,className:l,disableSSRStyle:s}=e;const u=(0,a.useRef)(null);return f({collapsibleRef:u,collapsed:n,animation:o}),a.createElement(t,{ref:u,style:s?void 0:p(n),onTransitionEnd:e=>{"height"===e.propertyName&&(d(u.current,n),i?.(n))},className:l},r)}function h(e){let{collapsed:t,...n}=e;const[o,i]=(0,a.useState)(!t),[l,s]=(0,a.useState)(t);return(0,a.useLayoutEffect)((()=>{t||i(!0)}),[t]),(0,a.useLayoutEffect)((()=>{o&&s(t)}),[o,t]),o?a.createElement(m,(0,r.Z)({},n,{collapsed:l})):null}function g(e){let{lazy:t,...n}=e;const r=t?h:m;return a.createElement(r,n)}},9689:(e,t,n)=>{"use strict";n.d(t,{nT:()=>m,pl:()=>p});var r=n(7294),a=n(2389),o=n(12),i=n(902),l=n(6668);const s=(0,o.WA)("docusaurus.announcement.dismiss"),u=(0,o.WA)("docusaurus.announcement.id"),c=()=>"true"===s.get(),d=e=>s.set(String(e)),f=r.createContext(null);function p(e){let{children:t}=e;const n=function(){const{announcementBar:e}=(0,l.L)(),t=(0,a.Z)(),[n,o]=(0,r.useState)((()=>!!t&&c()));(0,r.useEffect)((()=>{o(c())}),[]);const i=(0,r.useCallback)((()=>{d(!0),o(!0)}),[]);return(0,r.useEffect)((()=>{if(!e)return;const{id:t}=e;let n=u.get();"annoucement-bar"===n&&(n="announcement-bar");const r=t!==n;u.set(t),r&&d(!1),!r&&c()||o(!1)}),[e]),(0,r.useMemo)((()=>({isActive:!!e&&!n,close:i})),[e,n,i])}();return r.createElement(f.Provider,{value:n},t)}function m(){const e=(0,r.useContext)(f);if(!e)throw new i.i6("AnnouncementBarProvider");return e}},2949:(e,t,n)=>{"use strict";n.d(t,{I:()=>g,S:()=>h});var r=n(7294),a=n(412),o=n(902),i=n(12),l=n(6668);const s=r.createContext(void 0),u="theme",c=(0,i.WA)(u),d={light:"light",dark:"dark"},f=e=>e===d.dark?d.dark:d.light,p=e=>a.Z.canUseDOM?f(document.documentElement.getAttribute("data-theme")):f(e),m=e=>{c.set(f(e))};function h(e){let{children:t}=e;const n=function(){const{colorMode:{defaultMode:e,disableSwitch:t,respectPrefersColorScheme:n}}=(0,l.L)(),[a,o]=(0,r.useState)(p(e));(0,r.useEffect)((()=>{t&&c.del()}),[t]);const i=(0,r.useCallback)((function(t,r){void 0===r&&(r={});const{persist:a=!0}=r;t?(o(t),a&&m(t)):(o(n?window.matchMedia("(prefers-color-scheme: dark)").matches?d.dark:d.light:e),c.del())}),[n,e]);(0,r.useEffect)((()=>{document.documentElement.setAttribute("data-theme",f(a))}),[a]),(0,r.useEffect)((()=>{if(t)return;const e=e=>{if(e.key!==u)return;const t=c.get();null!==t&&i(f(t))};return window.addEventListener("storage",e),()=>window.removeEventListener("storage",e)}),[t,i]);const s=(0,r.useRef)(!1);return(0,r.useEffect)((()=>{if(t&&!n)return;const e=window.matchMedia("(prefers-color-scheme: dark)"),r=()=>{window.matchMedia("print").matches||s.current?s.current=window.matchMedia("print").matches:i(null)};return e.addListener(r),()=>e.removeListener(r)}),[i,t,n]),(0,r.useMemo)((()=>({colorMode:a,setColorMode:i,get isDarkTheme(){return a===d.dark},setLightTheme(){i(d.light)},setDarkTheme(){i(d.dark)}})),[a,i])}();return r.createElement(s.Provider,{value:n},t)}function g(){const e=(0,r.useContext)(s);if(null==e)throw new o.i6("ColorModeProvider","Please see https://docusaurus.io/docs/api/themes/configuration#use-color-mode.");return e}},373:(e,t,n)=>{"use strict";n.d(t,{J:()=>b,L5:()=>g});var r=n(7294),a=n(143),o=n(9935),i=n(6668),l=n(2802),s=n(902),u=n(12);const c=e=>`docs-preferred-version-${e}`,d={save:(e,t,n)=>{(0,u.WA)(c(e),{persistence:t}).set(n)},read:(e,t)=>(0,u.WA)(c(e),{persistence:t}).get(),clear:(e,t)=>{(0,u.WA)(c(e),{persistence:t}).del()}},f=e=>Object.fromEntries(e.map((e=>[e,{preferredVersionName:null}])));const p=r.createContext(null);function m(){const e=(0,a._r)(),t=(0,i.L)().docs.versionPersistence,n=(0,r.useMemo)((()=>Object.keys(e)),[e]),[o,l]=(0,r.useState)((()=>f(n)));(0,r.useEffect)((()=>{l(function(e){let{pluginIds:t,versionPersistence:n,allDocsData:r}=e;function a(e){const t=d.read(e,n);return r[e].versions.some((e=>e.name===t))?{preferredVersionName:t}:(d.clear(e,n),{preferredVersionName:null})}return Object.fromEntries(t.map((e=>[e,a(e)])))}({allDocsData:e,versionPersistence:t,pluginIds:n}))}),[e,t,n]);return[o,(0,r.useMemo)((()=>({savePreferredVersion:function(e,n){d.save(e,t,n),l((t=>({...t,[e]:{preferredVersionName:n}})))}})),[t])]}function h(e){let{children:t}=e;const n=m();return r.createElement(p.Provider,{value:n},t)}function g(e){let{children:t}=e;return l.cE?r.createElement(h,null,t):r.createElement(r.Fragment,null,t)}function v(){const e=(0,r.useContext)(p);if(!e)throw new s.i6("DocsPreferredVersionContextProvider");return e}function b(e){void 0===e&&(e=o.m);const t=(0,a.zh)(e),[n,i]=v(),{preferredVersionName:l}=n[e];return{preferredVersion:t.versions.find((e=>e.name===l))??null,savePreferredVersionName:(0,r.useCallback)((t=>{i.savePreferredVersion(e,t)}),[i,e])}}},1116:(e,t,n)=>{"use strict";n.d(t,{V:()=>s,b:()=>l});var r=n(7294),a=n(902);const o=Symbol("EmptyContext"),i=r.createContext(o);function l(e){let{children:t,name:n,items:a}=e;const o=(0,r.useMemo)((()=>n&&a?{name:n,items:a}:null),[n,a]);return r.createElement(i.Provider,{value:o},t)}function s(){const e=(0,r.useContext)(i);if(e===o)throw new a.i6("DocsSidebarProvider");return e}},2961:(e,t,n)=>{"use strict";n.d(t,{M:()=>f,e:()=>p});var r=n(7294),a=n(3102),o=n(7524),i=n(6550),l=(n(1688),n(902));function s(e){!function(e){const t=(0,i.k6)(),n=(0,l.zX)(e);(0,r.useEffect)((()=>t.block(((e,t)=>n(e,t)))),[t,n])}(((t,n)=>{if("POP"===n)return e(t,n)}))}var u=n(6668);const c=r.createContext(void 0);function d(){const e=function(){const e=(0,a.HY)(),{items:t}=(0,u.L)().navbar;return 0===t.length&&!e.component}(),t=(0,o.i)(),n=!e&&"mobile"===t,[i,l]=(0,r.useState)(!1);s((()=>{if(i)return l(!1),!1}));const c=(0,r.useCallback)((()=>{l((e=>!e))}),[]);return(0,r.useEffect)((()=>{"desktop"===t&&l(!1)}),[t]),(0,r.useMemo)((()=>({disabled:e,shouldRender:n,toggle:c,shown:i})),[e,n,c,i])}function f(e){let{children:t}=e;const n=d();return r.createElement(c.Provider,{value:n},t)}function p(){const e=r.useContext(c);if(void 0===e)throw new l.i6("NavbarMobileSidebarProvider");return e}},3102:(e,t,n)=>{"use strict";n.d(t,{HY:()=>l,Zo:()=>s,n2:()=>i});var r=n(7294),a=n(902);const o=r.createContext(null);function i(e){let{children:t}=e;const n=(0,r.useState)({component:null,props:null});return r.createElement(o.Provider,{value:n},t)}function l(){const e=(0,r.useContext)(o);if(!e)throw new a.i6("NavbarSecondaryMenuContentProvider");return e[0]}function s(e){let{component:t,props:n}=e;const i=(0,r.useContext)(o);if(!i)throw new a.i6("NavbarSecondaryMenuContentProvider");const[,l]=i,s=(0,a.Ql)(n);return(0,r.useEffect)((()=>{l({component:t,props:s})}),[l,t,s]),(0,r.useEffect)((()=>()=>l({component:null,props:null})),[l]),null}},9727:(e,t,n)=>{"use strict";n.d(t,{h:()=>a,t:()=>o});var r=n(7294);const a="navigation-with-keyboard";function o(){(0,r.useEffect)((()=>{function e(e){"keydown"===e.type&&"Tab"===e.key&&document.body.classList.add(a),"mousedown"===e.type&&document.body.classList.remove(a)}return document.addEventListener("keydown",e),document.addEventListener("mousedown",e),()=>{document.body.classList.remove(a),document.removeEventListener("keydown",e),document.removeEventListener("mousedown",e)}}),[])}},7524:(e,t,n)=>{"use strict";n.d(t,{i:()=>u});var r=n(7294),a=n(412);const o={desktop:"desktop",mobile:"mobile",ssr:"ssr"},i=996;function l(){return a.Z.canUseDOM?window.innerWidth>i?o.desktop:o.mobile:o.ssr}const s=!1;function u(){const[e,t]=(0,r.useState)((()=>s?"ssr":l()));return(0,r.useEffect)((()=>{function e(){t(l())}const n=s?window.setTimeout(e,1e3):void 0;return window.addEventListener("resize",e),()=>{window.removeEventListener("resize",e),clearTimeout(n)}}),[]),e}},5281:(e,t,n)=>{"use strict";n.d(t,{k:()=>r});const r={page:{blogListPage:"blog-list-page",blogPostPage:"blog-post-page",blogTagsListPage:"blog-tags-list-page",blogTagPostListPage:"blog-tags-post-list-page",docsDocPage:"docs-doc-page",docsTagsListPage:"docs-tags-list-page",docsTagDocListPage:"docs-tags-doc-list-page",mdxPage:"mdx-page"},wrapper:{main:"main-wrapper",blogPages:"blog-wrapper",docsPages:"docs-wrapper",mdxPages:"mdx-wrapper"},common:{editThisPage:"theme-edit-this-page",lastUpdated:"theme-last-updated",backToTopButton:"theme-back-to-top-button",codeBlock:"theme-code-block",admonition:"theme-admonition",admonitionType:e=>`theme-admonition-${e}`},layout:{},docs:{docVersionBanner:"theme-doc-version-banner",docVersionBadge:"theme-doc-version-badge",docBreadcrumbs:"theme-doc-breadcrumbs",docMarkdown:"theme-doc-markdown",docTocMobile:"theme-doc-toc-mobile",docTocDesktop:"theme-doc-toc-desktop",docFooter:"theme-doc-footer",docFooterTagsRow:"theme-doc-footer-tags-row",docFooterEditMetaRow:"theme-doc-footer-edit-meta-row",docSidebarContainer:"theme-doc-sidebar-container",docSidebarMenu:"theme-doc-sidebar-menu",docSidebarItemCategory:"theme-doc-sidebar-item-category",docSidebarItemLink:"theme-doc-sidebar-item-link",docSidebarItemCategoryLevel:e=>`theme-doc-sidebar-item-category-level-${e}`,docSidebarItemLinkLevel:e=>`theme-doc-sidebar-item-link-level-${e}`},blog:{}}},1442:(e,t,n)=>{"use strict";function r(){return window.matchMedia("(prefers-reduced-motion: reduce)").matches}n.d(t,{n:()=>r})},2802:(e,t,n)=>{"use strict";n.d(t,{Wl:()=>f,_F:()=>h,cE:()=>d,hI:()=>k,lO:()=>b,vY:()=>w,oz:()=>y,s1:()=>v});var r=n(7294),a=n(6550),o=n(8790),i=n(143),l=n(373),s=n(1116);function u(e){return Array.from(new Set(e))}var c=n(8596);const d=!!i._r;function f(e){if(e.href)return e.href;for(const t of e.items){if("link"===t.type)return t.href;if("category"===t.type){const e=f(t);if(e)return e}}}const p=(e,t)=>void 0!==e&&(0,c.Mg)(e,t),m=(e,t)=>e.some((e=>h(e,t)));function h(e,t){return"link"===e.type?p(e.href,t):"category"===e.type&&(p(e.href,t)||m(e.items,t))}function g(e){let{sidebarItems:t,pathname:n,onlyCategories:r=!1}=e;const a=[];return function e(t){for(const o of t)if("category"===o.type&&((0,c.Mg)(o.href,n)||e(o.items))||"link"===o.type&&(0,c.Mg)(o.href,n)){return r&&"category"!==o.type||a.unshift(o),!0}return!1}(t),a}function v(){const e=(0,s.V)(),{pathname:t}=(0,a.TH)(),n=(0,i.gA)()?.pluginData.breadcrumbs;return!1!==n&&e?g({sidebarItems:e.items,pathname:t}):null}function b(e){const{activeVersion:t}=(0,i.Iw)(e),{preferredVersion:n}=(0,l.J)(e),a=(0,i.yW)(e);return(0,r.useMemo)((()=>u([t,n,a].filter(Boolean))),[t,n,a])}function y(e,t){const n=b(t);return(0,r.useMemo)((()=>{const t=n.flatMap((e=>e.sidebars?Object.entries(e.sidebars):[])),r=t.find((t=>t[0]===e));if(!r)throw new Error(`Can't find any sidebar with id "${e}" in version${n.length>1?"s":""} ${n.map((e=>e.name)).join(", ")}".\nAvailable sidebar ids are:\n- ${t.map((e=>e[0])).join("\n- ")}`);return r[1]}),[e,n])}function w(e,t){const n=b(t);return(0,r.useMemo)((()=>{const t=n.flatMap((e=>e.docs)),r=t.find((t=>t.id===e));if(!r){if(n.flatMap((e=>e.draftIds)).includes(e))return null;throw new Error(`Couldn't find any doc with id "${e}" in version${n.length>1?"s":""} "${n.map((e=>e.name)).join(", ")}".\nAvailable doc ids are:\n- ${u(t.map((e=>e.id))).join("\n- ")}`)}return r}),[e,n])}function k(e){let{route:t,versionMetadata:n}=e;const r=(0,a.TH)(),i=t.routes,l=i.find((e=>(0,a.LX)(r.pathname,e)));if(!l)return null;const s=l.sidebar,u=s?n.docsSidebars[s]:void 0;return{docElement:(0,o.H)(i),sidebarName:s,sidebarItems:u}}},1944:(e,t,n)=>{"use strict";n.d(t,{FG:()=>f,d:()=>c,VC:()=>p});var r=n(7294),a=n(6010),o=n(5742),i=n(226);function l(){const e=r.useContext(i._);if(!e)throw new Error("Unexpected: no Docusaurus route context found");return e}var s=n(4996),u=n(2263);function c(e){let{title:t,description:n,keywords:a,image:i,children:l}=e;const c=function(e){const{siteConfig:t}=(0,u.Z)(),{title:n,titleDelimiter:r}=t;return e?.trim().length?`${e.trim()} ${r} ${n}`:n}(t),{withBaseUrl:d}=(0,s.C)(),f=i?d(i,{absolute:!0}):void 0;return r.createElement(o.Z,null,t&&r.createElement("title",null,c),t&&r.createElement("meta",{property:"og:title",content:c}),n&&r.createElement("meta",{name:"description",content:n}),n&&r.createElement("meta",{property:"og:description",content:n}),a&&r.createElement("meta",{name:"keywords",content:Array.isArray(a)?a.join(","):a}),f&&r.createElement("meta",{property:"og:image",content:f}),f&&r.createElement("meta",{name:"twitter:image",content:f}),l)}const d=r.createContext(void 0);function f(e){let{className:t,children:n}=e;const i=r.useContext(d),l=(0,a.Z)(i,t);return r.createElement(d.Provider,{value:l},r.createElement(o.Z,null,r.createElement("html",{className:l})),n)}function p(e){let{children:t}=e;const n=l(),o=`plugin-${n.plugin.name.replace(/docusaurus-(?:plugin|theme)-(?:content-)?/gi,"")}`;const i=`plugin-id-${n.plugin.id}`;return r.createElement(f,{className:(0,a.Z)(o,i)},t)}},902:(e,t,n)=>{"use strict";n.d(t,{D9:()=>i,Qc:()=>u,Ql:()=>s,i6:()=>l,zX:()=>o});var r=n(7294);const a=n(412).Z.canUseDOM?r.useLayoutEffect:r.useEffect;function o(e){const t=(0,r.useRef)(e);return a((()=>{t.current=e}),[e]),(0,r.useCallback)((function(){return t.current(...arguments)}),[])}function i(e){const t=(0,r.useRef)();return a((()=>{t.current=e})),t.current}class l extends Error{constructor(e,t){super(),this.name="ReactContextError",this.message=`Hook ${this.stack?.split("\n")[1]?.match(/at (?:\w+\.)?(?\w+)/)?.groups.name??""} is called outside the <${e}>. ${t??""}`}}function s(e){const t=Object.entries(e);return t.sort(((e,t)=>e[0].localeCompare(t[0]))),(0,r.useMemo)((()=>e),t.flat())}function u(e){return t=>{let{children:n}=t;return r.createElement(r.Fragment,null,e.reduceRight(((e,t)=>r.createElement(t,null,e)),n))}}},8596:(e,t,n)=>{"use strict";n.d(t,{Mg:()=>i,Ns:()=>l});var r=n(7294),a=n(723),o=n(2263);function i(e,t){const n=e=>(!e||e.endsWith("/")?e:`${e}/`)?.toLowerCase();return n(e)===n(t)}function l(){const{baseUrl:e}=(0,o.Z)().siteConfig;return(0,r.useMemo)((()=>function(e){let{baseUrl:t,routes:n}=e;function r(e){return e.path===t&&!0===e.exact}function a(e){return e.path===t&&!e.exact}return function e(t){if(0===t.length)return;return t.find(r)||e(t.filter(a).flatMap((e=>e.routes??[])))}(n)}({routes:a.Z,baseUrl:e})),[e])}},2466:(e,t,n)=>{"use strict";n.d(t,{Ct:()=>f,OC:()=>s,RF:()=>d});var r=n(7294),a=n(412),o=n(2389),i=n(902);const l=r.createContext(void 0);function s(e){let{children:t}=e;const n=function(){const e=(0,r.useRef)(!0);return(0,r.useMemo)((()=>({scrollEventsEnabledRef:e,enableScrollEvents:()=>{e.current=!0},disableScrollEvents:()=>{e.current=!1}})),[])}();return r.createElement(l.Provider,{value:n},t)}function u(){const e=(0,r.useContext)(l);if(null==e)throw new i.i6("ScrollControllerProvider");return e}const c=()=>a.Z.canUseDOM?{scrollX:window.pageXOffset,scrollY:window.pageYOffset}:null;function d(e,t){void 0===t&&(t=[]);const{scrollEventsEnabledRef:n}=u(),a=(0,r.useRef)(c()),o=(0,i.zX)(e);(0,r.useEffect)((()=>{const e=()=>{if(!n.current)return;const e=c();o(e,a.current),a.current=e},t={passive:!0};return e(),window.addEventListener("scroll",e,t),()=>window.removeEventListener("scroll",e,t)}),[o,n,...t])}function f(){const e=(0,r.useRef)(null),t=(0,o.Z)()&&"smooth"===getComputedStyle(document.documentElement).scrollBehavior;return{startScroll:n=>{e.current=t?function(e){return window.scrollTo({top:e,behavior:"smooth"}),()=>{}}(n):function(e){let t=null;const n=document.documentElement.scrollTop>e;return function r(){const a=document.documentElement.scrollTop;(n&&a>e||!n&&at&&cancelAnimationFrame(t)}(n)},cancelScroll:()=>e.current?.()}}},3320:(e,t,n)=>{"use strict";n.d(t,{HX:()=>r,os:()=>a});n(2263);const r="default";function a(e,t){return`docs-${e}-${t}`}},12:(e,t,n)=>{"use strict";n.d(t,{WA:()=>s});n(7294),n(1688);const r="localStorage";function a(e){let{key:t,oldValue:n,newValue:r,storage:a}=e;if(n===r)return;const o=document.createEvent("StorageEvent");o.initStorageEvent("storage",!1,!1,t,n,r,window.location.href,a),window.dispatchEvent(o)}function o(e){if(void 0===e&&(e=r),"undefined"==typeof window)throw new Error("Browser storage is not available on Node.js/Docusaurus SSR process.");if("none"===e)return null;try{return window[e]}catch(n){return t=n,i||(console.warn("Docusaurus browser storage is not available.\nPossible reasons: running Docusaurus in an iframe, in an incognito browser session, or using too strict browser privacy settings.",t),i=!0),null}var t}let i=!1;const l={get:()=>null,set:()=>{},del:()=>{},listen:()=>()=>{}};function s(e,t){if("undefined"==typeof window)return function(e){function t(){throw new Error(`Illegal storage API usage for storage key "${e}".\nDocusaurus storage APIs are not supposed to be called on the server-rendering process.\nPlease only call storage APIs in effects and event handlers.`)}return{get:t,set:t,del:t,listen:t}}(e);const n=o(t?.persistence);return null===n?l:{get:()=>{try{return n.getItem(e)}catch(t){return console.error(`Docusaurus storage error, can't get key=${e}`,t),null}},set:t=>{try{const r=n.getItem(e);n.setItem(e,t),a({key:e,oldValue:r,newValue:t,storage:n})}catch(r){console.error(`Docusaurus storage error, can't set ${e}=${t}`,r)}},del:()=>{try{const t=n.getItem(e);n.removeItem(e),a({key:e,oldValue:t,newValue:null,storage:n})}catch(t){console.error(`Docusaurus storage error, can't delete key=${e}`,t)}},listen:t=>{try{const r=r=>{r.storageArea===n&&r.key===e&&t(r)};return window.addEventListener("storage",r),()=>window.removeEventListener("storage",r)}catch(r){return console.error(`Docusaurus storage error, can't listen for changes of key=${e}`,r),()=>{}}}}}},4711:(e,t,n)=>{"use strict";n.d(t,{l:()=>i});var r=n(2263),a=n(6550),o=n(8780);function i(){const{siteConfig:{baseUrl:e,url:t,trailingSlash:n},i18n:{defaultLocale:i,currentLocale:l}}=(0,r.Z)(),{pathname:s}=(0,a.TH)(),u=(0,o.applyTrailingSlash)(s,{trailingSlash:n,baseUrl:e}),c=l===i?e:e.replace(`/${l}/`,"/"),d=u.replace(e,"");return{createUrl:function(e){let{locale:n,fullyQualified:r}=e;return`${r?t:""}${function(e){return e===i?`${c}`:`${c}${e}/`}(n)}${d}`}}}},5936:(e,t,n)=>{"use strict";n.d(t,{S:()=>i});var r=n(7294),a=n(6550),o=n(902);function i(e){const t=(0,a.TH)(),n=(0,o.D9)(t),i=(0,o.zX)(e);(0,r.useEffect)((()=>{n&&t!==n&&i({location:t,previousLocation:n})}),[i,t,n])}},6668:(e,t,n)=>{"use strict";n.d(t,{L:()=>a});var r=n(2263);function a(){return(0,r.Z)().siteConfig.themeConfig}},8802:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.default=function(e,t){const{trailingSlash:n,baseUrl:r}=t;if(e.startsWith("#"))return e;if(void 0===n)return e;const[a]=e.split(/[#?]/),o="/"===a||a===r?a:(i=a,n?function(e){return e.endsWith("/")?e:`${e}/`}(i):function(e){return e.endsWith("/")?e.slice(0,-1):e}(i));var i;return e.replace(a,o)}},4143:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.getErrorCausalChain=void 0,t.getErrorCausalChain=function e(t){return t.cause?[t,...e(t.cause)]:[t]}},8780:function(e,t,n){"use strict";var r=this&&this.__importDefault||function(e){return e&&e.__esModule?e:{default:e}};Object.defineProperty(t,"__esModule",{value:!0}),t.getErrorCausalChain=t.applyTrailingSlash=t.blogPostContainerID=void 0,t.blogPostContainerID="__blog-post-container";var a=n(8802);Object.defineProperty(t,"applyTrailingSlash",{enumerable:!0,get:function(){return r(a).default}});var o=n(4143);Object.defineProperty(t,"getErrorCausalChain",{enumerable:!0,get:function(){return o.getErrorCausalChain}})},6010:(e,t,n)=>{"use strict";function r(e){var t,n,a="";if("string"==typeof e||"number"==typeof e)a+=e;else if("object"==typeof e)if(Array.isArray(e))for(t=0;ta});const a=function(){for(var e,t,n=0,a="";n{"use strict";n.d(t,{lX:()=>w,q_:()=>T,ob:()=>p,PP:()=>A,Ep:()=>f});var r=n(7462);function a(e){return"/"===e.charAt(0)}function o(e,t){for(var n=t,r=n+1,a=e.length;r=0;f--){var p=i[f];"."===p?o(i,f):".."===p?(o(i,f),d++):d&&(o(i,f),d--)}if(!u)for(;d--;d)i.unshift("..");!u||""===i[0]||i[0]&&a(i[0])||i.unshift("");var m=i.join("/");return n&&"/"!==m.substr(-1)&&(m+="/"),m};var l=n(8776);function s(e){return"/"===e.charAt(0)?e:"/"+e}function u(e){return"/"===e.charAt(0)?e.substr(1):e}function c(e,t){return function(e,t){return 0===e.toLowerCase().indexOf(t.toLowerCase())&&-1!=="/?#".indexOf(e.charAt(t.length))}(e,t)?e.substr(t.length):e}function d(e){return"/"===e.charAt(e.length-1)?e.slice(0,-1):e}function f(e){var t=e.pathname,n=e.search,r=e.hash,a=t||"/";return n&&"?"!==n&&(a+="?"===n.charAt(0)?n:"?"+n),r&&"#"!==r&&(a+="#"===r.charAt(0)?r:"#"+r),a}function p(e,t,n,a){var o;"string"==typeof e?(o=function(e){var t=e||"/",n="",r="",a=t.indexOf("#");-1!==a&&(r=t.substr(a),t=t.substr(0,a));var o=t.indexOf("?");return-1!==o&&(n=t.substr(o),t=t.substr(0,o)),{pathname:t,search:"?"===n?"":n,hash:"#"===r?"":r}}(e),o.state=t):(void 0===(o=(0,r.Z)({},e)).pathname&&(o.pathname=""),o.search?"?"!==o.search.charAt(0)&&(o.search="?"+o.search):o.search="",o.hash?"#"!==o.hash.charAt(0)&&(o.hash="#"+o.hash):o.hash="",void 0!==t&&void 0===o.state&&(o.state=t));try{o.pathname=decodeURI(o.pathname)}catch(l){throw l instanceof URIError?new URIError('Pathname "'+o.pathname+'" could not be decoded. This is likely caused by an invalid percent-encoding.'):l}return n&&(o.key=n),a?o.pathname?"/"!==o.pathname.charAt(0)&&(o.pathname=i(o.pathname,a.pathname)):o.pathname=a.pathname:o.pathname||(o.pathname="/"),o}function m(){var e=null;var t=[];return{setPrompt:function(t){return e=t,function(){e===t&&(e=null)}},confirmTransitionTo:function(t,n,r,a){if(null!=e){var o="function"==typeof e?e(t,n):e;"string"==typeof o?"function"==typeof r?r(o,a):a(!0):a(!1!==o)}else a(!0)},appendListener:function(e){var n=!0;function r(){n&&e.apply(void 0,arguments)}return t.push(r),function(){n=!1,t=t.filter((function(e){return e!==r}))}},notifyListeners:function(){for(var e=arguments.length,n=new Array(e),r=0;rt?n.splice(t,n.length-t,a):n.push(a),d({action:r,location:a,index:t,entries:n})}}))},replace:function(e,t){var r="REPLACE",a=p(e,t,h(),w.location);c.confirmTransitionTo(a,r,n,(function(e){e&&(w.entries[w.index]=a,d({action:r,location:a}))}))},go:y,goBack:function(){y(-1)},goForward:function(){y(1)},canGo:function(e){var t=w.index+e;return t>=0&&t{"use strict";var r=n(9864),a={childContextTypes:!0,contextType:!0,contextTypes:!0,defaultProps:!0,displayName:!0,getDefaultProps:!0,getDerivedStateFromError:!0,getDerivedStateFromProps:!0,mixins:!0,propTypes:!0,type:!0},o={name:!0,length:!0,prototype:!0,caller:!0,callee:!0,arguments:!0,arity:!0},i={$$typeof:!0,compare:!0,defaultProps:!0,displayName:!0,propTypes:!0,type:!0},l={};function s(e){return r.isMemo(e)?i:l[e.$$typeof]||a}l[r.ForwardRef]={$$typeof:!0,render:!0,defaultProps:!0,displayName:!0,propTypes:!0},l[r.Memo]=i;var u=Object.defineProperty,c=Object.getOwnPropertyNames,d=Object.getOwnPropertySymbols,f=Object.getOwnPropertyDescriptor,p=Object.getPrototypeOf,m=Object.prototype;e.exports=function e(t,n,r){if("string"!=typeof n){if(m){var a=p(n);a&&a!==m&&e(t,a,r)}var i=c(n);d&&(i=i.concat(d(n)));for(var l=s(t),h=s(n),g=0;g{"use strict";e.exports=function(e,t,n,r,a,o,i,l){if(!e){var s;if(void 0===t)s=new Error("Minified exception occurred; use the non-minified dev environment for the full error message and additional helpful warnings.");else{var u=[n,r,a,o,i,l],c=0;(s=new Error(t.replace(/%s/g,(function(){return u[c++]})))).name="Invariant Violation"}throw s.framesToPop=1,s}}},5826:e=>{e.exports=Array.isArray||function(e){return"[object Array]"==Object.prototype.toString.call(e)}},2497:(e,t,n)=>{"use strict";n.r(t)},2295:(e,t,n)=>{"use strict";n.r(t)},4865:function(e,t,n){var r,a;r=function(){var e,t,n={version:"0.2.0"},r=n.settings={minimum:.08,easing:"ease",positionUsing:"",speed:200,trickle:!0,trickleRate:.02,trickleSpeed:800,showSpinner:!0,barSelector:'[role="bar"]',spinnerSelector:'[role="spinner"]',parent:"body",template:'
'};function a(e,t,n){return en?n:e}function o(e){return 100*(-1+e)}function i(e,t,n){var a;return(a="translate3d"===r.positionUsing?{transform:"translate3d("+o(e)+"%,0,0)"}:"translate"===r.positionUsing?{transform:"translate("+o(e)+"%,0)"}:{"margin-left":o(e)+"%"}).transition="all "+t+"ms "+n,a}n.configure=function(e){var t,n;for(t in e)void 0!==(n=e[t])&&e.hasOwnProperty(t)&&(r[t]=n);return this},n.status=null,n.set=function(e){var t=n.isStarted();e=a(e,r.minimum,1),n.status=1===e?null:e;var o=n.render(!t),u=o.querySelector(r.barSelector),c=r.speed,d=r.easing;return o.offsetWidth,l((function(t){""===r.positionUsing&&(r.positionUsing=n.getPositioningCSS()),s(u,i(e,c,d)),1===e?(s(o,{transition:"none",opacity:1}),o.offsetWidth,setTimeout((function(){s(o,{transition:"all "+c+"ms linear",opacity:0}),setTimeout((function(){n.remove(),t()}),c)}),c)):setTimeout(t,c)})),this},n.isStarted=function(){return"number"==typeof n.status},n.start=function(){n.status||n.set(0);var e=function(){setTimeout((function(){n.status&&(n.trickle(),e())}),r.trickleSpeed)};return r.trickle&&e(),this},n.done=function(e){return e||n.status?n.inc(.3+.5*Math.random()).set(1):this},n.inc=function(e){var t=n.status;return t?("number"!=typeof e&&(e=(1-t)*a(Math.random()*t,.1,.95)),t=a(t+e,0,.994),n.set(t)):n.start()},n.trickle=function(){return n.inc(Math.random()*r.trickleRate)},e=0,t=0,n.promise=function(r){return r&&"resolved"!==r.state()?(0===t&&n.start(),e++,t++,r.always((function(){0==--t?(e=0,n.done()):n.set((e-t)/e)})),this):this},n.render=function(e){if(n.isRendered())return document.getElementById("nprogress");c(document.documentElement,"nprogress-busy");var t=document.createElement("div");t.id="nprogress",t.innerHTML=r.template;var a,i=t.querySelector(r.barSelector),l=e?"-100":o(n.status||0),u=document.querySelector(r.parent);return s(i,{transition:"all 0 linear",transform:"translate3d("+l+"%,0,0)"}),r.showSpinner||(a=t.querySelector(r.spinnerSelector))&&p(a),u!=document.body&&c(u,"nprogress-custom-parent"),u.appendChild(t),t},n.remove=function(){d(document.documentElement,"nprogress-busy"),d(document.querySelector(r.parent),"nprogress-custom-parent");var e=document.getElementById("nprogress");e&&p(e)},n.isRendered=function(){return!!document.getElementById("nprogress")},n.getPositioningCSS=function(){var e=document.body.style,t="WebkitTransform"in e?"Webkit":"MozTransform"in e?"Moz":"msTransform"in e?"ms":"OTransform"in e?"O":"";return t+"Perspective"in e?"translate3d":t+"Transform"in e?"translate":"margin"};var l=function(){var e=[];function t(){var n=e.shift();n&&n(t)}return function(n){e.push(n),1==e.length&&t()}}(),s=function(){var e=["Webkit","O","Moz","ms"],t={};function n(e){return e.replace(/^-ms-/,"ms-").replace(/-([\da-z])/gi,(function(e,t){return t.toUpperCase()}))}function r(t){var n=document.body.style;if(t in n)return t;for(var r,a=e.length,o=t.charAt(0).toUpperCase()+t.slice(1);a--;)if((r=e[a]+o)in n)return r;return t}function a(e){return e=n(e),t[e]||(t[e]=r(e))}function o(e,t,n){t=a(t),e.style[t]=n}return function(e,t){var n,r,a=arguments;if(2==a.length)for(n in t)void 0!==(r=t[n])&&t.hasOwnProperty(n)&&o(e,n,r);else o(e,a[1],a[2])}}();function u(e,t){return("string"==typeof e?e:f(e)).indexOf(" "+t+" ")>=0}function c(e,t){var n=f(e),r=n+t;u(n,t)||(e.className=r.substring(1))}function d(e,t){var n,r=f(e);u(e,t)&&(n=r.replace(" "+t+" "," "),e.className=n.substring(1,n.length-1))}function f(e){return(" "+(e.className||"")+" ").replace(/\s+/gi," ")}function p(e){e&&e.parentNode&&e.parentNode.removeChild(e)}return n},void 0===(a="function"==typeof r?r.call(t,n,t,e):r)||(e.exports=a)},7418:e=>{"use strict";var t=Object.getOwnPropertySymbols,n=Object.prototype.hasOwnProperty,r=Object.prototype.propertyIsEnumerable;e.exports=function(){try{if(!Object.assign)return!1;var e=new String("abc");if(e[5]="de","5"===Object.getOwnPropertyNames(e)[0])return!1;for(var t={},n=0;n<10;n++)t["_"+String.fromCharCode(n)]=n;if("0123456789"!==Object.getOwnPropertyNames(t).map((function(e){return t[e]})).join(""))return!1;var r={};return"abcdefghijklmnopqrst".split("").forEach((function(e){r[e]=e})),"abcdefghijklmnopqrst"===Object.keys(Object.assign({},r)).join("")}catch(a){return!1}}()?Object.assign:function(e,a){for(var o,i,l=function(e){if(null==e)throw new TypeError("Object.assign cannot be called with null or undefined");return Object(e)}(e),s=1;s{"use strict";n.d(t,{Z:()=>o});var r=function(){var e=/(?:^|\s)lang(?:uage)?-([\w-]+)(?=\s|$)/i,t=0,n={},r={util:{encode:function e(t){return t instanceof a?new a(t.type,e(t.content),t.alias):Array.isArray(t)?t.map(e):t.replace(/&/g,"&").replace(/=d.reach);S+=E.value.length,E=E.next){var x=E.value;if(t.length>e.length)return;if(!(x instanceof a)){var C,T=1;if(b){if(!(C=o(k,S,e,v))||C.index>=e.length)break;var _=C.index,A=C.index+C[0].length,L=S;for(L+=E.value.length;_>=L;)L+=(E=E.next).value.length;if(S=L-=E.value.length,E.value instanceof a)continue;for(var R=E;R!==t.tail&&(Ld.reach&&(d.reach=I);var D=E.prev;if(P&&(D=s(t,D,P),S+=P.length),u(t,D,T),E=s(t,D,new a(f,g?r.tokenize(N,g):N,y,N)),O&&s(t,E,O),T>1){var M={cause:f+","+m,reach:I};i(e,t,n,E.prev,S,M),d&&M.reach>d.reach&&(d.reach=M.reach)}}}}}}function l(){var e={value:null,prev:null,next:null},t={value:null,prev:e,next:null};e.next=t,this.head=e,this.tail=t,this.length=0}function s(e,t,n){var r=t.next,a={value:n,prev:t,next:r};return t.next=a,r.prev=a,e.length++,a}function u(e,t,n){for(var r=t.next,a=0;a"+o.content+""},r}(),a=r;r.default=r,a.languages.markup={comment:{pattern://,greedy:!0},prolog:{pattern:/<\?[\s\S]+?\?>/,greedy:!0},doctype:{pattern:/"'[\]]|"[^"]*"|'[^']*')+(?:\[(?:[^<"'\]]|"[^"]*"|'[^']*'|<(?!!--)|)*\]\s*)?>/i,greedy:!0,inside:{"internal-subset":{pattern:/(^[^\[]*\[)[\s\S]+(?=\]>$)/,lookbehind:!0,greedy:!0,inside:null},string:{pattern:/"[^"]*"|'[^']*'/,greedy:!0},punctuation:/^$|[[\]]/,"doctype-tag":/^DOCTYPE/i,name:/[^\s<>'"]+/}},cdata:{pattern://i,greedy:!0},tag:{pattern:/<\/?(?!\d)[^\s>\/=$<%]+(?:\s(?:\s*[^\s>\/=]+(?:\s*=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+(?=[\s>]))|(?=[\s/>])))+)?\s*\/?>/,greedy:!0,inside:{tag:{pattern:/^<\/?[^\s>\/]+/,inside:{punctuation:/^<\/?/,namespace:/^[^\s>\/:]+:/}},"special-attr":[],"attr-value":{pattern:/=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+)/,inside:{punctuation:[{pattern:/^=/,alias:"attr-equals"},/"|'/]}},punctuation:/\/?>/,"attr-name":{pattern:/[^\s>\/]+/,inside:{namespace:/^[^\s>\/:]+:/}}}},entity:[{pattern:/&[\da-z]{1,8};/i,alias:"named-entity"},/&#x?[\da-f]{1,8};/i]},a.languages.markup.tag.inside["attr-value"].inside.entity=a.languages.markup.entity,a.languages.markup.doctype.inside["internal-subset"].inside=a.languages.markup,a.hooks.add("wrap",(function(e){"entity"===e.type&&(e.attributes.title=e.content.replace(/&/,"&"))})),Object.defineProperty(a.languages.markup.tag,"addInlined",{value:function(e,t){var n={};n["language-"+t]={pattern:/(^$)/i,lookbehind:!0,inside:a.languages[t]},n.cdata=/^$/i;var r={"included-cdata":{pattern://i,inside:n}};r["language-"+t]={pattern:/[\s\S]+/,inside:a.languages[t]};var o={};o[e]={pattern:RegExp(/(<__[^>]*>)(?:))*\]\]>|(?!)/.source.replace(/__/g,(function(){return e})),"i"),lookbehind:!0,greedy:!0,inside:r},a.languages.insertBefore("markup","cdata",o)}}),Object.defineProperty(a.languages.markup.tag,"addAttribute",{value:function(e,t){a.languages.markup.tag.inside["special-attr"].push({pattern:RegExp(/(^|["'\s])/.source+"(?:"+e+")"+/\s*=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+(?=[\s>]))/.source,"i"),lookbehind:!0,inside:{"attr-name":/^[^\s=]+/,"attr-value":{pattern:/=[\s\S]+/,inside:{value:{pattern:/(^=\s*(["']|(?!["'])))\S[\s\S]*(?=\2$)/,lookbehind:!0,alias:[t,"language-"+t],inside:a.languages[t]},punctuation:[{pattern:/^=/,alias:"attr-equals"},/"|'/]}}}})}}),a.languages.html=a.languages.markup,a.languages.mathml=a.languages.markup,a.languages.svg=a.languages.markup,a.languages.xml=a.languages.extend("markup",{}),a.languages.ssml=a.languages.xml,a.languages.atom=a.languages.xml,a.languages.rss=a.languages.xml,function(e){var t="\\b(?:BASH|BASHOPTS|BASH_ALIASES|BASH_ARGC|BASH_ARGV|BASH_CMDS|BASH_COMPLETION_COMPAT_DIR|BASH_LINENO|BASH_REMATCH|BASH_SOURCE|BASH_VERSINFO|BASH_VERSION|COLORTERM|COLUMNS|COMP_WORDBREAKS|DBUS_SESSION_BUS_ADDRESS|DEFAULTS_PATH|DESKTOP_SESSION|DIRSTACK|DISPLAY|EUID|GDMSESSION|GDM_LANG|GNOME_KEYRING_CONTROL|GNOME_KEYRING_PID|GPG_AGENT_INFO|GROUPS|HISTCONTROL|HISTFILE|HISTFILESIZE|HISTSIZE|HOME|HOSTNAME|HOSTTYPE|IFS|INSTANCE|JOB|LANG|LANGUAGE|LC_ADDRESS|LC_ALL|LC_IDENTIFICATION|LC_MEASUREMENT|LC_MONETARY|LC_NAME|LC_NUMERIC|LC_PAPER|LC_TELEPHONE|LC_TIME|LESSCLOSE|LESSOPEN|LINES|LOGNAME|LS_COLORS|MACHTYPE|MAILCHECK|MANDATORY_PATH|NO_AT_BRIDGE|OLDPWD|OPTERR|OPTIND|ORBIT_SOCKETDIR|OSTYPE|PAPERSIZE|PATH|PIPESTATUS|PPID|PS1|PS2|PS3|PS4|PWD|RANDOM|REPLY|SECONDS|SELINUX_INIT|SESSION|SESSIONTYPE|SESSION_MANAGER|SHELL|SHELLOPTS|SHLVL|SSH_AUTH_SOCK|TERM|UID|UPSTART_EVENTS|UPSTART_INSTANCE|UPSTART_JOB|UPSTART_SESSION|USER|WINDOWID|XAUTHORITY|XDG_CONFIG_DIRS|XDG_CURRENT_DESKTOP|XDG_DATA_DIRS|XDG_GREETER_DATA_DIR|XDG_MENU_PREFIX|XDG_RUNTIME_DIR|XDG_SEAT|XDG_SEAT_PATH|XDG_SESSION_DESKTOP|XDG_SESSION_ID|XDG_SESSION_PATH|XDG_SESSION_TYPE|XDG_VTNR|XMODIFIERS)\\b",n={pattern:/(^(["']?)\w+\2)[ \t]+\S.*/,lookbehind:!0,alias:"punctuation",inside:null},r={bash:n,environment:{pattern:RegExp("\\$"+t),alias:"constant"},variable:[{pattern:/\$?\(\([\s\S]+?\)\)/,greedy:!0,inside:{variable:[{pattern:/(^\$\(\([\s\S]+)\)\)/,lookbehind:!0},/^\$\(\(/],number:/\b0x[\dA-Fa-f]+\b|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:[Ee]-?\d+)?/,operator:/--|\+\+|\*\*=?|<<=?|>>=?|&&|\|\||[=!+\-*/%<>^&|]=?|[?~:]/,punctuation:/\(\(?|\)\)?|,|;/}},{pattern:/\$\((?:\([^)]+\)|[^()])+\)|`[^`]+`/,greedy:!0,inside:{variable:/^\$\(|^`|\)$|`$/}},{pattern:/\$\{[^}]+\}/,greedy:!0,inside:{operator:/:[-=?+]?|[!\/]|##?|%%?|\^\^?|,,?/,punctuation:/[\[\]]/,environment:{pattern:RegExp("(\\{)"+t),lookbehind:!0,alias:"constant"}}},/\$(?:\w+|[#?*!@$])/],entity:/\\(?:[abceEfnrtv\\"]|O?[0-7]{1,3}|U[0-9a-fA-F]{8}|u[0-9a-fA-F]{4}|x[0-9a-fA-F]{1,2})/};e.languages.bash={shebang:{pattern:/^#!\s*\/.*/,alias:"important"},comment:{pattern:/(^|[^"{\\$])#.*/,lookbehind:!0},"function-name":[{pattern:/(\bfunction\s+)[\w-]+(?=(?:\s*\(?:\s*\))?\s*\{)/,lookbehind:!0,alias:"function"},{pattern:/\b[\w-]+(?=\s*\(\s*\)\s*\{)/,alias:"function"}],"for-or-select":{pattern:/(\b(?:for|select)\s+)\w+(?=\s+in\s)/,alias:"variable",lookbehind:!0},"assign-left":{pattern:/(^|[\s;|&]|[<>]\()\w+(?=\+?=)/,inside:{environment:{pattern:RegExp("(^|[\\s;|&]|[<>]\\()"+t),lookbehind:!0,alias:"constant"}},alias:"variable",lookbehind:!0},string:[{pattern:/((?:^|[^<])<<-?\s*)(\w+)\s[\s\S]*?(?:\r?\n|\r)\2/,lookbehind:!0,greedy:!0,inside:r},{pattern:/((?:^|[^<])<<-?\s*)(["'])(\w+)\2\s[\s\S]*?(?:\r?\n|\r)\3/,lookbehind:!0,greedy:!0,inside:{bash:n}},{pattern:/(^|[^\\](?:\\\\)*)"(?:\\[\s\S]|\$\([^)]+\)|\$(?!\()|`[^`]+`|[^"\\`$])*"/,lookbehind:!0,greedy:!0,inside:r},{pattern:/(^|[^$\\])'[^']*'/,lookbehind:!0,greedy:!0},{pattern:/\$'(?:[^'\\]|\\[\s\S])*'/,greedy:!0,inside:{entity:r.entity}}],environment:{pattern:RegExp("\\$?"+t),alias:"constant"},variable:r.variable,function:{pattern:/(^|[\s;|&]|[<>]\()(?:add|apropos|apt|apt-cache|apt-get|aptitude|aspell|automysqlbackup|awk|basename|bash|bc|bconsole|bg|bzip2|cal|cat|cfdisk|chgrp|chkconfig|chmod|chown|chroot|cksum|clear|cmp|column|comm|composer|cp|cron|crontab|csplit|curl|cut|date|dc|dd|ddrescue|debootstrap|df|diff|diff3|dig|dir|dircolors|dirname|dirs|dmesg|docker|docker-compose|du|egrep|eject|env|ethtool|expand|expect|expr|fdformat|fdisk|fg|fgrep|file|find|fmt|fold|format|free|fsck|ftp|fuser|gawk|git|gparted|grep|groupadd|groupdel|groupmod|groups|grub-mkconfig|gzip|halt|head|hg|history|host|hostname|htop|iconv|id|ifconfig|ifdown|ifup|import|install|ip|jobs|join|kill|killall|less|link|ln|locate|logname|logrotate|look|lpc|lpr|lprint|lprintd|lprintq|lprm|ls|lsof|lynx|make|man|mc|mdadm|mkconfig|mkdir|mke2fs|mkfifo|mkfs|mkisofs|mknod|mkswap|mmv|more|most|mount|mtools|mtr|mutt|mv|nano|nc|netstat|nice|nl|node|nohup|notify-send|npm|nslookup|op|open|parted|passwd|paste|pathchk|ping|pkill|pnpm|podman|podman-compose|popd|pr|printcap|printenv|ps|pushd|pv|quota|quotacheck|quotactl|ram|rar|rcp|reboot|remsync|rename|renice|rev|rm|rmdir|rpm|rsync|scp|screen|sdiff|sed|sendmail|seq|service|sftp|sh|shellcheck|shuf|shutdown|sleep|slocate|sort|split|ssh|stat|strace|su|sudo|sum|suspend|swapon|sync|tac|tail|tar|tee|time|timeout|top|touch|tr|traceroute|tsort|tty|umount|uname|unexpand|uniq|units|unrar|unshar|unzip|update-grub|uptime|useradd|userdel|usermod|users|uudecode|uuencode|v|vcpkg|vdir|vi|vim|virsh|vmstat|wait|watch|wc|wget|whereis|which|who|whoami|write|xargs|xdg-open|yarn|yes|zenity|zip|zsh|zypper)(?=$|[)\s;|&])/,lookbehind:!0},keyword:{pattern:/(^|[\s;|&]|[<>]\()(?:case|do|done|elif|else|esac|fi|for|function|if|in|select|then|until|while)(?=$|[)\s;|&])/,lookbehind:!0},builtin:{pattern:/(^|[\s;|&]|[<>]\()(?:\.|:|alias|bind|break|builtin|caller|cd|command|continue|declare|echo|enable|eval|exec|exit|export|getopts|hash|help|let|local|logout|mapfile|printf|pwd|read|readarray|readonly|return|set|shift|shopt|source|test|times|trap|type|typeset|ulimit|umask|unalias|unset)(?=$|[)\s;|&])/,lookbehind:!0,alias:"class-name"},boolean:{pattern:/(^|[\s;|&]|[<>]\()(?:false|true)(?=$|[)\s;|&])/,lookbehind:!0},"file-descriptor":{pattern:/\B&\d\b/,alias:"important"},operator:{pattern:/\d?<>|>\||\+=|=[=~]?|!=?|<<[<-]?|[&\d]?>>|\d[<>]&?|[<>][&=]?|&[>&]?|\|[&|]?/,inside:{"file-descriptor":{pattern:/^\d/,alias:"important"}}},punctuation:/\$?\(\(?|\)\)?|\.\.|[{}[\];\\]/,number:{pattern:/(^|\s)(?:[1-9]\d*|0)(?:[.,]\d+)?\b/,lookbehind:!0}},n.inside=e.languages.bash;for(var a=["comment","function-name","for-or-select","assign-left","string","environment","function","keyword","builtin","boolean","file-descriptor","operator","punctuation","number"],o=r.variable[1].inside,i=0;i]=?|[!=]=?=?|--?|\+\+?|&&?|\|\|?|[?*/~^%]/,punctuation:/[{}[\];(),.:]/},a.languages.c=a.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),a.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),a.languages.insertBefore("c","string",{macro:{pattern:/(^[\t ]*)#\s*[a-z](?:[^\r\n\\/]|\/(?!\*)|\/\*(?:[^*]|\*(?!\/))*\*\/|\\(?:\r\n|[\s\S]))*/im,lookbehind:!0,greedy:!0,alias:"property",inside:{string:[{pattern:/^(#\s*include\s*)<[^>]+>/,lookbehind:!0},a.languages.c.string],char:a.languages.c.char,comment:a.languages.c.comment,"macro-name":[{pattern:/(^#\s*define\s+)\w+\b(?!\()/i,lookbehind:!0},{pattern:/(^#\s*define\s+)\w+\b(?=\()/i,lookbehind:!0,alias:"function"}],directive:{pattern:/^(#\s*)[a-z]+/,lookbehind:!0,alias:"keyword"},"directive-hash":/^#/,punctuation:/##|\\(?=[\r\n])/,expression:{pattern:/\S[\s\S]*/,inside:a.languages.c}}}}),a.languages.insertBefore("c","function",{constant:/\b(?:EOF|NULL|SEEK_CUR|SEEK_END|SEEK_SET|__DATE__|__FILE__|__LINE__|__TIMESTAMP__|__TIME__|__func__|stderr|stdin|stdout)\b/}),delete a.languages.c.boolean,function(e){var t=/\b(?:alignas|alignof|asm|auto|bool|break|case|catch|char|char16_t|char32_t|char8_t|class|co_await|co_return|co_yield|compl|concept|const|const_cast|consteval|constexpr|constinit|continue|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|final|float|for|friend|goto|if|import|inline|int|int16_t|int32_t|int64_t|int8_t|long|module|mutable|namespace|new|noexcept|nullptr|operator|override|private|protected|public|register|reinterpret_cast|requires|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|uint16_t|uint32_t|uint64_t|uint8_t|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/,n=/\b(?!)\w+(?:\s*\.\s*\w+)*\b/.source.replace(//g,(function(){return t.source}));e.languages.cpp=e.languages.extend("c",{"class-name":[{pattern:RegExp(/(\b(?:class|concept|enum|struct|typename)\s+)(?!)\w+/.source.replace(//g,(function(){return t.source}))),lookbehind:!0},/\b[A-Z]\w*(?=\s*::\s*\w+\s*\()/,/\b[A-Z_]\w*(?=\s*::\s*~\w+\s*\()/i,/\b\w+(?=\s*<(?:[^<>]|<(?:[^<>]|<[^<>]*>)*>)*>\s*::\s*\w+\s*\()/],keyword:t,number:{pattern:/(?:\b0b[01']+|\b0x(?:[\da-f']+(?:\.[\da-f']*)?|\.[\da-f']+)(?:p[+-]?[\d']+)?|(?:\b[\d']+(?:\.[\d']*)?|\B\.[\d']+)(?:e[+-]?[\d']+)?)[ful]{0,4}/i,greedy:!0},operator:/>>=?|<<=?|->|--|\+\+|&&|\|\||[?:~]|<=>|[-+*/%&|^!=<>]=?|\b(?:and|and_eq|bitand|bitor|not|not_eq|or|or_eq|xor|xor_eq)\b/,boolean:/\b(?:false|true)\b/}),e.languages.insertBefore("cpp","string",{module:{pattern:RegExp(/(\b(?:import|module)\s+)/.source+"(?:"+/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"|<[^<>\r\n]*>/.source+"|"+/(?:\s*:\s*)?|:\s*/.source.replace(//g,(function(){return n}))+")"),lookbehind:!0,greedy:!0,inside:{string:/^[<"][\s\S]+/,operator:/:/,punctuation:/\./}},"raw-string":{pattern:/R"([^()\\ ]{0,16})\([\s\S]*?\)\1"/,alias:"string",greedy:!0}}),e.languages.insertBefore("cpp","keyword",{"generic-function":{pattern:/\b(?!operator\b)[a-z_]\w*\s*<(?:[^<>]|<[^<>]*>)*>(?=\s*\()/i,inside:{function:/^\w+/,generic:{pattern:/<[\s\S]+/,alias:"class-name",inside:e.languages.cpp}}}}),e.languages.insertBefore("cpp","operator",{"double-colon":{pattern:/::/,alias:"punctuation"}}),e.languages.insertBefore("cpp","class-name",{"base-clause":{pattern:/(\b(?:class|struct)\s+\w+\s*:\s*)[^;{}"'\s]+(?:\s+[^;{}"'\s]+)*(?=\s*[;{])/,lookbehind:!0,greedy:!0,inside:e.languages.extend("cpp",{})}}),e.languages.insertBefore("inside","double-colon",{"class-name":/\b[a-z_]\w*\b(?!\s*::)/i},e.languages.cpp["base-clause"])}(a),function(e){var t=/(?:"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"|'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n])*')/;e.languages.css={comment:/\/\*[\s\S]*?\*\//,atrule:{pattern:/@[\w-](?:[^;{\s]|\s+(?![\s{]))*(?:;|(?=\s*\{))/,inside:{rule:/^@[\w-]+/,"selector-function-argument":{pattern:/(\bselector\s*\(\s*(?![\s)]))(?:[^()\s]|\s+(?![\s)])|\((?:[^()]|\([^()]*\))*\))+(?=\s*\))/,lookbehind:!0,alias:"selector"},keyword:{pattern:/(^|[^\w-])(?:and|not|only|or)(?![\w-])/,lookbehind:!0}}},url:{pattern:RegExp("\\burl\\((?:"+t.source+"|"+/(?:[^\\\r\n()"']|\\[\s\S])*/.source+")\\)","i"),greedy:!0,inside:{function:/^url/i,punctuation:/^\(|\)$/,string:{pattern:RegExp("^"+t.source+"$"),alias:"url"}}},selector:{pattern:RegExp("(^|[{}\\s])[^{}\\s](?:[^{};\"'\\s]|\\s+(?![\\s{])|"+t.source+")*(?=\\s*\\{)"),lookbehind:!0},string:{pattern:t,greedy:!0},property:{pattern:/(^|[^-\w\xA0-\uFFFF])(?!\s)[-_a-z\xA0-\uFFFF](?:(?!\s)[-\w\xA0-\uFFFF])*(?=\s*:)/i,lookbehind:!0},important:/!important\b/i,function:{pattern:/(^|[^-a-z0-9])[-a-z0-9]+(?=\()/i,lookbehind:!0},punctuation:/[(){};:,]/},e.languages.css.atrule.inside.rest=e.languages.css;var n=e.languages.markup;n&&(n.tag.addInlined("style","css"),n.tag.addAttribute("style","css"))}(a),function(e){var t,n=/("|')(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/;e.languages.css.selector={pattern:e.languages.css.selector.pattern,lookbehind:!0,inside:t={"pseudo-element":/:(?:after|before|first-letter|first-line|selection)|::[-\w]+/,"pseudo-class":/:[-\w]+/,class:/\.[-\w]+/,id:/#[-\w]+/,attribute:{pattern:RegExp("\\[(?:[^[\\]\"']|"+n.source+")*\\]"),greedy:!0,inside:{punctuation:/^\[|\]$/,"case-sensitivity":{pattern:/(\s)[si]$/i,lookbehind:!0,alias:"keyword"},namespace:{pattern:/^(\s*)(?:(?!\s)[-*\w\xA0-\uFFFF])*\|(?!=)/,lookbehind:!0,inside:{punctuation:/\|$/}},"attr-name":{pattern:/^(\s*)(?:(?!\s)[-\w\xA0-\uFFFF])+/,lookbehind:!0},"attr-value":[n,{pattern:/(=\s*)(?:(?!\s)[-\w\xA0-\uFFFF])+(?=\s*$)/,lookbehind:!0}],operator:/[|~*^$]?=/}},"n-th":[{pattern:/(\(\s*)[+-]?\d*[\dn](?:\s*[+-]\s*\d+)?(?=\s*\))/,lookbehind:!0,inside:{number:/[\dn]+/,operator:/[+-]/}},{pattern:/(\(\s*)(?:even|odd)(?=\s*\))/i,lookbehind:!0}],combinator:/>|\+|~|\|\|/,punctuation:/[(),]/}},e.languages.css.atrule.inside["selector-function-argument"].inside=t,e.languages.insertBefore("css","property",{variable:{pattern:/(^|[^-\w\xA0-\uFFFF])--(?!\s)[-_a-z\xA0-\uFFFF](?:(?!\s)[-\w\xA0-\uFFFF])*/i,lookbehind:!0}});var r={pattern:/(\b\d+)(?:%|[a-z]+(?![\w-]))/,lookbehind:!0},a={pattern:/(^|[^\w.-])-?(?:\d+(?:\.\d+)?|\.\d+)/,lookbehind:!0};e.languages.insertBefore("css","function",{operator:{pattern:/(\s)[+\-*\/](?=\s)/,lookbehind:!0},hexcode:{pattern:/\B#[\da-f]{3,8}\b/i,alias:"color"},color:[{pattern:/(^|[^\w-])(?:AliceBlue|AntiqueWhite|Aqua|Aquamarine|Azure|Beige|Bisque|Black|BlanchedAlmond|Blue|BlueViolet|Brown|BurlyWood|CadetBlue|Chartreuse|Chocolate|Coral|CornflowerBlue|Cornsilk|Crimson|Cyan|DarkBlue|DarkCyan|DarkGoldenRod|DarkGr[ae]y|DarkGreen|DarkKhaki|DarkMagenta|DarkOliveGreen|DarkOrange|DarkOrchid|DarkRed|DarkSalmon|DarkSeaGreen|DarkSlateBlue|DarkSlateGr[ae]y|DarkTurquoise|DarkViolet|DeepPink|DeepSkyBlue|DimGr[ae]y|DodgerBlue|FireBrick|FloralWhite|ForestGreen|Fuchsia|Gainsboro|GhostWhite|Gold|GoldenRod|Gr[ae]y|Green|GreenYellow|HoneyDew|HotPink|IndianRed|Indigo|Ivory|Khaki|Lavender|LavenderBlush|LawnGreen|LemonChiffon|LightBlue|LightCoral|LightCyan|LightGoldenRodYellow|LightGr[ae]y|LightGreen|LightPink|LightSalmon|LightSeaGreen|LightSkyBlue|LightSlateGr[ae]y|LightSteelBlue|LightYellow|Lime|LimeGreen|Linen|Magenta|Maroon|MediumAquaMarine|MediumBlue|MediumOrchid|MediumPurple|MediumSeaGreen|MediumSlateBlue|MediumSpringGreen|MediumTurquoise|MediumVioletRed|MidnightBlue|MintCream|MistyRose|Moccasin|NavajoWhite|Navy|OldLace|Olive|OliveDrab|Orange|OrangeRed|Orchid|PaleGoldenRod|PaleGreen|PaleTurquoise|PaleVioletRed|PapayaWhip|PeachPuff|Peru|Pink|Plum|PowderBlue|Purple|Red|RosyBrown|RoyalBlue|SaddleBrown|Salmon|SandyBrown|SeaGreen|SeaShell|Sienna|Silver|SkyBlue|SlateBlue|SlateGr[ae]y|Snow|SpringGreen|SteelBlue|Tan|Teal|Thistle|Tomato|Transparent|Turquoise|Violet|Wheat|White|WhiteSmoke|Yellow|YellowGreen)(?![\w-])/i,lookbehind:!0},{pattern:/\b(?:hsl|rgb)\(\s*\d{1,3}\s*,\s*\d{1,3}%?\s*,\s*\d{1,3}%?\s*\)\B|\b(?:hsl|rgb)a\(\s*\d{1,3}\s*,\s*\d{1,3}%?\s*,\s*\d{1,3}%?\s*,\s*(?:0|0?\.\d+|1)\s*\)\B/i,inside:{unit:r,number:a,function:/[\w-]+(?=\()/,punctuation:/[(),]/}}],entity:/\\[\da-f]{1,8}/i,unit:r,number:a})}(a),a.languages.javascript=a.languages.extend("clike",{"class-name":[a.languages.clike["class-name"],{pattern:/(^|[^$\w\xA0-\uFFFF])(?!\s)[_$A-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\.(?:constructor|prototype))/,lookbehind:!0}],keyword:[{pattern:/((?:^|\})\s*)catch\b/,lookbehind:!0},{pattern:/(^|[^.]|\.\.\.\s*)\b(?:as|assert(?=\s*\{)|async(?=\s*(?:function\b|\(|[$\w\xA0-\uFFFF]|$))|await|break|case|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally(?=\s*(?:\{|$))|for|from(?=\s*(?:['"]|$))|function|(?:get|set)(?=\s*(?:[#\[$\w\xA0-\uFFFF]|$))|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|static|super|switch|this|throw|try|typeof|undefined|var|void|while|with|yield)\b/,lookbehind:!0}],function:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*(?:\.\s*(?:apply|bind|call)\s*)?\()/,number:{pattern:RegExp(/(^|[^\w$])/.source+"(?:"+/NaN|Infinity/.source+"|"+/0[bB][01]+(?:_[01]+)*n?/.source+"|"+/0[oO][0-7]+(?:_[0-7]+)*n?/.source+"|"+/0[xX][\dA-Fa-f]+(?:_[\dA-Fa-f]+)*n?/.source+"|"+/\d+(?:_\d+)*n/.source+"|"+/(?:\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\.\d+(?:_\d+)*)(?:[Ee][+-]?\d+(?:_\d+)*)?/.source+")"+/(?![\w$])/.source),lookbehind:!0},operator:/--|\+\+|\*\*=?|=>|&&=?|\|\|=?|[!=]==|<<=?|>>>?=?|[-+*/%&|^!=<>]=?|\.{3}|\?\?=?|\?\.?|[~:]/}),a.languages.javascript["class-name"][0].pattern=/(\b(?:class|extends|implements|instanceof|interface|new)\s+)[\w.\\]+/,a.languages.insertBefore("javascript","keyword",{regex:{pattern:/((?:^|[^$\w\xA0-\uFFFF."'\])\s]|\b(?:return|yield))\s*)\/(?:\[(?:[^\]\\\r\n]|\\.)*\]|\\.|[^/\\\[\r\n])+\/[dgimyus]{0,7}(?=(?:\s|\/\*(?:[^*]|\*(?!\/))*\*\/)*(?:$|[\r\n,.;:})\]]|\/\/))/,lookbehind:!0,greedy:!0,inside:{"regex-source":{pattern:/^(\/)[\s\S]+(?=\/[a-z]*$)/,lookbehind:!0,alias:"language-regex",inside:a.languages.regex},"regex-delimiter":/^\/|\/$/,"regex-flags":/^[a-z]+$/}},"function-variable":{pattern:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*[=:]\s*(?:async\s*)?(?:\bfunction\b|(?:\((?:[^()]|\([^()]*\))*\)|(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)\s*=>))/,alias:"function"},parameter:[{pattern:/(function(?:\s+(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)?\s*\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\))/,lookbehind:!0,inside:a.languages.javascript},{pattern:/(^|[^$\w\xA0-\uFFFF])(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*=>)/i,lookbehind:!0,inside:a.languages.javascript},{pattern:/(\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\)\s*=>)/,lookbehind:!0,inside:a.languages.javascript},{pattern:/((?:\b|\s|^)(?!(?:as|async|await|break|case|catch|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|set|static|super|switch|this|throw|try|typeof|undefined|var|void|while|with|yield)(?![$\w\xA0-\uFFFF]))(?:(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*\s*)\(\s*|\]\s*\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\)\s*\{)/,lookbehind:!0,inside:a.languages.javascript}],constant:/\b[A-Z](?:[A-Z_]|\dx?)*\b/}),a.languages.insertBefore("javascript","string",{hashbang:{pattern:/^#!.*/,greedy:!0,alias:"comment"},"template-string":{pattern:/`(?:\\[\s\S]|\$\{(?:[^{}]|\{(?:[^{}]|\{[^}]*\})*\})+\}|(?!\$\{)[^\\`])*`/,greedy:!0,inside:{"template-punctuation":{pattern:/^`|`$/,alias:"string"},interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$\{(?:[^{}]|\{(?:[^{}]|\{[^}]*\})*\})+\}/,lookbehind:!0,inside:{"interpolation-punctuation":{pattern:/^\$\{|\}$/,alias:"punctuation"},rest:a.languages.javascript}},string:/[\s\S]+/}},"string-property":{pattern:/((?:^|[,{])[ \t]*)(["'])(?:\\(?:\r\n|[\s\S])|(?!\2)[^\\\r\n])*\2(?=\s*:)/m,lookbehind:!0,greedy:!0,alias:"property"}}),a.languages.insertBefore("javascript","operator",{"literal-property":{pattern:/((?:^|[,{])[ \t]*)(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*:)/m,lookbehind:!0,alias:"property"}}),a.languages.markup&&(a.languages.markup.tag.addInlined("script","javascript"),a.languages.markup.tag.addAttribute(/on(?:abort|blur|change|click|composition(?:end|start|update)|dblclick|error|focus(?:in|out)?|key(?:down|up)|load|mouse(?:down|enter|leave|move|out|over|up)|reset|resize|scroll|select|slotchange|submit|unload|wheel)/.source,"javascript")),a.languages.js=a.languages.javascript,function(e){var t=/#(?!\{).+/,n={pattern:/#\{[^}]+\}/,alias:"variable"};e.languages.coffeescript=e.languages.extend("javascript",{comment:t,string:[{pattern:/'(?:\\[\s\S]|[^\\'])*'/,greedy:!0},{pattern:/"(?:\\[\s\S]|[^\\"])*"/,greedy:!0,inside:{interpolation:n}}],keyword:/\b(?:and|break|by|catch|class|continue|debugger|delete|do|each|else|extend|extends|false|finally|for|if|in|instanceof|is|isnt|let|loop|namespace|new|no|not|null|of|off|on|or|own|return|super|switch|then|this|throw|true|try|typeof|undefined|unless|until|when|while|window|with|yes|yield)\b/,"class-member":{pattern:/@(?!\d)\w+/,alias:"variable"}}),e.languages.insertBefore("coffeescript","comment",{"multiline-comment":{pattern:/###[\s\S]+?###/,alias:"comment"},"block-regex":{pattern:/\/{3}[\s\S]*?\/{3}/,alias:"regex",inside:{comment:t,interpolation:n}}}),e.languages.insertBefore("coffeescript","string",{"inline-javascript":{pattern:/`(?:\\[\s\S]|[^\\`])*`/,inside:{delimiter:{pattern:/^`|`$/,alias:"punctuation"},script:{pattern:/[\s\S]+/,alias:"language-javascript",inside:e.languages.javascript}}},"multiline-string":[{pattern:/'''[\s\S]*?'''/,greedy:!0,alias:"string"},{pattern:/"""[\s\S]*?"""/,greedy:!0,alias:"string",inside:{interpolation:n}}]}),e.languages.insertBefore("coffeescript","keyword",{property:/(?!\d)\w+(?=\s*:(?!:))/}),delete e.languages.coffeescript["template-string"],e.languages.coffee=e.languages.coffeescript}(a),function(e){var t=/[*&][^\s[\]{},]+/,n=/!(?:<[\w\-%#;/?:@&=+$,.!~*'()[\]]+>|(?:[a-zA-Z\d-]*!)?[\w\-%#;/?:@&=+$.~*'()]+)?/,r="(?:"+n.source+"(?:[ \t]+"+t.source+")?|"+t.source+"(?:[ \t]+"+n.source+")?)",a=/(?:[^\s\x00-\x08\x0e-\x1f!"#%&'*,\-:>?@[\]`{|}\x7f-\x84\x86-\x9f\ud800-\udfff\ufffe\uffff]|[?:-])(?:[ \t]*(?:(?![#:])|:))*/.source.replace(//g,(function(){return/[^\s\x00-\x08\x0e-\x1f,[\]{}\x7f-\x84\x86-\x9f\ud800-\udfff\ufffe\uffff]/.source})),o=/"(?:[^"\\\r\n]|\\.)*"|'(?:[^'\\\r\n]|\\.)*'/.source;function i(e,t){t=(t||"").replace(/m/g,"")+"m";var n=/([:\-,[{]\s*(?:\s<>[ \t]+)?)(?:<>)(?=[ \t]*(?:$|,|\]|\}|(?:[\r\n]\s*)?#))/.source.replace(/<>/g,(function(){return r})).replace(/<>/g,(function(){return e}));return RegExp(n,t)}e.languages.yaml={scalar:{pattern:RegExp(/([\-:]\s*(?:\s<>[ \t]+)?[|>])[ \t]*(?:((?:\r?\n|\r)[ \t]+)\S[^\r\n]*(?:\2[^\r\n]+)*)/.source.replace(/<>/g,(function(){return r}))),lookbehind:!0,alias:"string"},comment:/#.*/,key:{pattern:RegExp(/((?:^|[:\-,[{\r\n?])[ \t]*(?:<>[ \t]+)?)<>(?=\s*:\s)/.source.replace(/<>/g,(function(){return r})).replace(/<>/g,(function(){return"(?:"+a+"|"+o+")"}))),lookbehind:!0,greedy:!0,alias:"atrule"},directive:{pattern:/(^[ \t]*)%.+/m,lookbehind:!0,alias:"important"},datetime:{pattern:i(/\d{4}-\d\d?-\d\d?(?:[tT]|[ \t]+)\d\d?:\d{2}:\d{2}(?:\.\d*)?(?:[ \t]*(?:Z|[-+]\d\d?(?::\d{2})?))?|\d{4}-\d{2}-\d{2}|\d\d?:\d{2}(?::\d{2}(?:\.\d*)?)?/.source),lookbehind:!0,alias:"number"},boolean:{pattern:i(/false|true/.source,"i"),lookbehind:!0,alias:"important"},null:{pattern:i(/null|~/.source,"i"),lookbehind:!0,alias:"important"},string:{pattern:i(o),lookbehind:!0,greedy:!0},number:{pattern:i(/[+-]?(?:0x[\da-f]+|0o[0-7]+|(?:\d+(?:\.\d*)?|\.\d+)(?:e[+-]?\d+)?|\.inf|\.nan)/.source,"i"),lookbehind:!0},tag:n,important:t,punctuation:/---|[:[\]{}\-,|>?]|\.\.\./},e.languages.yml=e.languages.yaml}(a),function(e){var t=/(?:\\.|[^\\\n\r]|(?:\n|\r\n?)(?![\r\n]))/.source;function n(e){return e=e.replace(//g,(function(){return t})),RegExp(/((?:^|[^\\])(?:\\{2})*)/.source+"(?:"+e+")")}var r=/(?:\\.|``(?:[^`\r\n]|`(?!`))+``|`[^`\r\n]+`|[^\\|\r\n`])+/.source,a=/\|?__(?:\|__)+\|?(?:(?:\n|\r\n?)|(?![\s\S]))/.source.replace(/__/g,(function(){return r})),o=/\|?[ \t]*:?-{3,}:?[ \t]*(?:\|[ \t]*:?-{3,}:?[ \t]*)+\|?(?:\n|\r\n?)/.source;e.languages.markdown=e.languages.extend("markup",{}),e.languages.insertBefore("markdown","prolog",{"front-matter-block":{pattern:/(^(?:\s*[\r\n])?)---(?!.)[\s\S]*?[\r\n]---(?!.)/,lookbehind:!0,greedy:!0,inside:{punctuation:/^---|---$/,"front-matter":{pattern:/\S+(?:\s+\S+)*/,alias:["yaml","language-yaml"],inside:e.languages.yaml}}},blockquote:{pattern:/^>(?:[\t ]*>)*/m,alias:"punctuation"},table:{pattern:RegExp("^"+a+o+"(?:"+a+")*","m"),inside:{"table-data-rows":{pattern:RegExp("^("+a+o+")(?:"+a+")*$"),lookbehind:!0,inside:{"table-data":{pattern:RegExp(r),inside:e.languages.markdown},punctuation:/\|/}},"table-line":{pattern:RegExp("^("+a+")"+o+"$"),lookbehind:!0,inside:{punctuation:/\||:?-{3,}:?/}},"table-header-row":{pattern:RegExp("^"+a+"$"),inside:{"table-header":{pattern:RegExp(r),alias:"important",inside:e.languages.markdown},punctuation:/\|/}}}},code:[{pattern:/((?:^|\n)[ \t]*\n|(?:^|\r\n?)[ \t]*\r\n?)(?: {4}|\t).+(?:(?:\n|\r\n?)(?: {4}|\t).+)*/,lookbehind:!0,alias:"keyword"},{pattern:/^```[\s\S]*?^```$/m,greedy:!0,inside:{"code-block":{pattern:/^(```.*(?:\n|\r\n?))[\s\S]+?(?=(?:\n|\r\n?)^```$)/m,lookbehind:!0},"code-language":{pattern:/^(```).+/,lookbehind:!0},punctuation:/```/}}],title:[{pattern:/\S.*(?:\n|\r\n?)(?:==+|--+)(?=[ \t]*$)/m,alias:"important",inside:{punctuation:/==+$|--+$/}},{pattern:/(^\s*)#.+/m,lookbehind:!0,alias:"important",inside:{punctuation:/^#+|#+$/}}],hr:{pattern:/(^\s*)([*-])(?:[\t ]*\2){2,}(?=\s*$)/m,lookbehind:!0,alias:"punctuation"},list:{pattern:/(^\s*)(?:[*+-]|\d+\.)(?=[\t ].)/m,lookbehind:!0,alias:"punctuation"},"url-reference":{pattern:/!?\[[^\]]+\]:[\t ]+(?:\S+|<(?:\\.|[^>\\])+>)(?:[\t ]+(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\)))?/,inside:{variable:{pattern:/^(!?\[)[^\]]+/,lookbehind:!0},string:/(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\))$/,punctuation:/^[\[\]!:]|[<>]/},alias:"url"},bold:{pattern:n(/\b__(?:(?!_)|_(?:(?!_))+_)+__\b|\*\*(?:(?!\*)|\*(?:(?!\*))+\*)+\*\*/.source),lookbehind:!0,greedy:!0,inside:{content:{pattern:/(^..)[\s\S]+(?=..$)/,lookbehind:!0,inside:{}},punctuation:/\*\*|__/}},italic:{pattern:n(/\b_(?:(?!_)|__(?:(?!_))+__)+_\b|\*(?:(?!\*)|\*\*(?:(?!\*))+\*\*)+\*/.source),lookbehind:!0,greedy:!0,inside:{content:{pattern:/(^.)[\s\S]+(?=.$)/,lookbehind:!0,inside:{}},punctuation:/[*_]/}},strike:{pattern:n(/(~~?)(?:(?!~))+\2/.source),lookbehind:!0,greedy:!0,inside:{content:{pattern:/(^~~?)[\s\S]+(?=\1$)/,lookbehind:!0,inside:{}},punctuation:/~~?/}},"code-snippet":{pattern:/(^|[^\\`])(?:``[^`\r\n]+(?:`[^`\r\n]+)*``(?!`)|`[^`\r\n]+`(?!`))/,lookbehind:!0,greedy:!0,alias:["code","keyword"]},url:{pattern:n(/!?\[(?:(?!\]))+\](?:\([^\s)]+(?:[\t ]+"(?:\\.|[^"\\])*")?\)|[ \t]?\[(?:(?!\]))+\])/.source),lookbehind:!0,greedy:!0,inside:{operator:/^!/,content:{pattern:/(^\[)[^\]]+(?=\])/,lookbehind:!0,inside:{}},variable:{pattern:/(^\][ \t]?\[)[^\]]+(?=\]$)/,lookbehind:!0},url:{pattern:/(^\]\()[^\s)]+/,lookbehind:!0},string:{pattern:/(^[ \t]+)"(?:\\.|[^"\\])*"(?=\)$)/,lookbehind:!0}}}}),["url","bold","italic","strike"].forEach((function(t){["url","bold","italic","strike","code-snippet"].forEach((function(n){t!==n&&(e.languages.markdown[t].inside.content.inside[n]=e.languages.markdown[n])}))})),e.hooks.add("after-tokenize",(function(e){"markdown"!==e.language&&"md"!==e.language||function e(t){if(t&&"string"!=typeof t)for(var n=0,r=t.length;n",quot:'"'},s=String.fromCodePoint||String.fromCharCode;e.languages.md=e.languages.markdown}(a),a.languages.graphql={comment:/#.*/,description:{pattern:/(?:"""(?:[^"]|(?!""")")*"""|"(?:\\.|[^\\"\r\n])*")(?=\s*[a-z_])/i,greedy:!0,alias:"string",inside:{"language-markdown":{pattern:/(^"(?:"")?)(?!\1)[\s\S]+(?=\1$)/,lookbehind:!0,inside:a.languages.markdown}}},string:{pattern:/"""(?:[^"]|(?!""")")*"""|"(?:\\.|[^\\"\r\n])*"/,greedy:!0},number:/(?:\B-|\b)\d+(?:\.\d+)?(?:e[+-]?\d+)?\b/i,boolean:/\b(?:false|true)\b/,variable:/\$[a-z_]\w*/i,directive:{pattern:/@[a-z_]\w*/i,alias:"function"},"attr-name":{pattern:/\b[a-z_]\w*(?=\s*(?:\((?:[^()"]|"(?:\\.|[^\\"\r\n])*")*\))?:)/i,greedy:!0},"atom-input":{pattern:/\b[A-Z]\w*Input\b/,alias:"class-name"},scalar:/\b(?:Boolean|Float|ID|Int|String)\b/,constant:/\b[A-Z][A-Z_\d]*\b/,"class-name":{pattern:/(\b(?:enum|implements|interface|on|scalar|type|union)\s+|&\s*|:\s*|\[)[A-Z_]\w*/,lookbehind:!0},fragment:{pattern:/(\bfragment\s+|\.{3}\s*(?!on\b))[a-zA-Z_]\w*/,lookbehind:!0,alias:"function"},"definition-mutation":{pattern:/(\bmutation\s+)[a-zA-Z_]\w*/,lookbehind:!0,alias:"function"},"definition-query":{pattern:/(\bquery\s+)[a-zA-Z_]\w*/,lookbehind:!0,alias:"function"},keyword:/\b(?:directive|enum|extend|fragment|implements|input|interface|mutation|on|query|repeatable|scalar|schema|subscription|type|union)\b/,operator:/[!=|&]|\.{3}/,"property-query":/\w+(?=\s*\()/,object:/\w+(?=\s*\{)/,punctuation:/[!(){}\[\]:=,]/,property:/\w+/},a.hooks.add("after-tokenize",(function(e){if("graphql"===e.language)for(var t=e.tokens.filter((function(e){return"string"!=typeof e&&"comment"!==e.type&&"scalar"!==e.type})),n=0;n0)){var l=f(/^\{$/,/^\}$/);if(-1===l)continue;for(var s=n;s=0&&p(u,"variable-input")}}}}function c(e){return t[n+e]}function d(e,t){t=t||0;for(var n=0;n?|<|>)?|>[>=]?|\b(?:AND|BETWEEN|DIV|ILIKE|IN|IS|LIKE|NOT|OR|REGEXP|RLIKE|SOUNDS LIKE|XOR)\b/i,punctuation:/[;[\]()`,.]/},function(e){var t=e.languages.javascript["template-string"],n=t.pattern.source,r=t.inside.interpolation,a=r.inside["interpolation-punctuation"],o=r.pattern.source;function i(t,r){if(e.languages[t])return{pattern:RegExp("((?:"+r+")\\s*)"+n),lookbehind:!0,greedy:!0,inside:{"template-punctuation":{pattern:/^`|`$/,alias:"string"},"embedded-code":{pattern:/[\s\S]+/,alias:t}}}}function l(e,t){return"___"+t.toUpperCase()+"_"+e+"___"}function s(t,n,r){var a={code:t,grammar:n,language:r};return e.hooks.run("before-tokenize",a),a.tokens=e.tokenize(a.code,a.grammar),e.hooks.run("after-tokenize",a),a.tokens}function u(t){var n={};n["interpolation-punctuation"]=a;var o=e.tokenize(t,n);if(3===o.length){var i=[1,1];i.push.apply(i,s(o[1],e.languages.javascript,"javascript")),o.splice.apply(o,i)}return new e.Token("interpolation",o,r.alias,t)}function c(t,n,r){var a=e.tokenize(t,{interpolation:{pattern:RegExp(o),lookbehind:!0}}),i=0,c={},d=s(a.map((function(e){if("string"==typeof e)return e;for(var n,a=e.content;-1!==t.indexOf(n=l(i++,r)););return c[n]=a,n})).join(""),n,r),f=Object.keys(c);return i=0,function e(t){for(var n=0;n=f.length)return;var r=t[n];if("string"==typeof r||"string"==typeof r.content){var a=f[i],o="string"==typeof r?r:r.content,l=o.indexOf(a);if(-1!==l){++i;var s=o.substring(0,l),d=u(c[a]),p=o.substring(l+a.length),m=[];if(s&&m.push(s),m.push(d),p){var h=[p];e(h),m.push.apply(m,h)}"string"==typeof r?(t.splice.apply(t,[n,1].concat(m)),n+=m.length-1):r.content=m}}else{var g=r.content;Array.isArray(g)?e(g):e([g])}}}(d),new e.Token(r,d,"language-"+r,t)}e.languages.javascript["template-string"]=[i("css",/\b(?:styled(?:\([^)]*\))?(?:\s*\.\s*\w+(?:\([^)]*\))*)*|css(?:\s*\.\s*(?:global|resolve))?|createGlobalStyle|keyframes)/.source),i("html",/\bhtml|\.\s*(?:inner|outer)HTML\s*\+?=/.source),i("svg",/\bsvg/.source),i("markdown",/\b(?:markdown|md)/.source),i("graphql",/\b(?:gql|graphql(?:\s*\.\s*experimental)?)/.source),i("sql",/\bsql/.source),t].filter(Boolean);var d={javascript:!0,js:!0,typescript:!0,ts:!0,jsx:!0,tsx:!0};function f(e){return"string"==typeof e?e:Array.isArray(e)?e.map(f).join(""):f(e.content)}e.hooks.add("after-tokenize",(function(t){t.language in d&&function t(n){for(var r=0,a=n.length;r]|<(?:[^<>]|<[^<>]*>)*>)*>)?/,lookbehind:!0,greedy:!0,inside:null},builtin:/\b(?:Array|Function|Promise|any|boolean|console|never|number|string|symbol|unknown)\b/}),e.languages.typescript.keyword.push(/\b(?:abstract|declare|is|keyof|readonly|require)\b/,/\b(?:asserts|infer|interface|module|namespace|type)\b(?=\s*(?:[{_$a-zA-Z\xA0-\uFFFF]|$))/,/\btype\b(?=\s*(?:[\{*]|$))/),delete e.languages.typescript.parameter,delete e.languages.typescript["literal-property"];var t=e.languages.extend("typescript",{});delete t["class-name"],e.languages.typescript["class-name"].inside=t,e.languages.insertBefore("typescript","function",{decorator:{pattern:/@[$\w\xA0-\uFFFF]+/,inside:{at:{pattern:/^@/,alias:"operator"},function:/^[\s\S]+/}},"generic-function":{pattern:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*\s*<(?:[^<>]|<(?:[^<>]|<[^<>]*>)*>)*>(?=\s*\()/,greedy:!0,inside:{function:/^#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*/,generic:{pattern:/<[\s\S]+/,alias:"class-name",inside:t}}}}),e.languages.ts=e.languages.typescript}(a),function(e){function t(e,t){return RegExp(e.replace(//g,(function(){return/(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*/.source})),t)}e.languages.insertBefore("javascript","function-variable",{"method-variable":{pattern:RegExp("(\\.\\s*)"+e.languages.javascript["function-variable"].pattern.source),lookbehind:!0,alias:["function-variable","method","function","property-access"]}}),e.languages.insertBefore("javascript","function",{method:{pattern:RegExp("(\\.\\s*)"+e.languages.javascript.function.source),lookbehind:!0,alias:["function","property-access"]}}),e.languages.insertBefore("javascript","constant",{"known-class-name":[{pattern:/\b(?:(?:Float(?:32|64)|(?:Int|Uint)(?:8|16|32)|Uint8Clamped)?Array|ArrayBuffer|BigInt|Boolean|DataView|Date|Error|Function|Intl|JSON|(?:Weak)?(?:Map|Set)|Math|Number|Object|Promise|Proxy|Reflect|RegExp|String|Symbol|WebAssembly)\b/,alias:"class-name"},{pattern:/\b(?:[A-Z]\w*)Error\b/,alias:"class-name"}]}),e.languages.insertBefore("javascript","keyword",{imports:{pattern:t(/(\bimport\b\s*)(?:(?:\s*,\s*(?:\*\s*as\s+|\{[^{}]*\}))?|\*\s*as\s+|\{[^{}]*\})(?=\s*\bfrom\b)/.source),lookbehind:!0,inside:e.languages.javascript},exports:{pattern:t(/(\bexport\b\s*)(?:\*(?:\s*as\s+)?(?=\s*\bfrom\b)|\{[^{}]*\})/.source),lookbehind:!0,inside:e.languages.javascript}}),e.languages.javascript.keyword.unshift({pattern:/\b(?:as|default|export|from|import)\b/,alias:"module"},{pattern:/\b(?:await|break|catch|continue|do|else|finally|for|if|return|switch|throw|try|while|yield)\b/,alias:"control-flow"},{pattern:/\bnull\b/,alias:["null","nil"]},{pattern:/\bundefined\b/,alias:"nil"}),e.languages.insertBefore("javascript","operator",{spread:{pattern:/\.{3}/,alias:"operator"},arrow:{pattern:/=>/,alias:"operator"}}),e.languages.insertBefore("javascript","punctuation",{"property-access":{pattern:t(/(\.\s*)#?/.source),lookbehind:!0},"maybe-class-name":{pattern:/(^|[^$\w\xA0-\uFFFF])[A-Z][$\w\xA0-\uFFFF]+/,lookbehind:!0},dom:{pattern:/\b(?:document|(?:local|session)Storage|location|navigator|performance|window)\b/,alias:"variable"},console:{pattern:/\bconsole(?=\s*\.)/,alias:"class-name"}});for(var n=["function","function-variable","method","method-variable","property-access"],r=0;r*\.{3}(?:[^{}]|)*\})/.source;function o(e,t){return e=e.replace(//g,(function(){return n})).replace(//g,(function(){return r})).replace(//g,(function(){return a})),RegExp(e,t)}a=o(a).source,e.languages.jsx=e.languages.extend("markup",t),e.languages.jsx.tag.pattern=o(/<\/?(?:[\w.:-]+(?:+(?:[\w.:$-]+(?:=(?:"(?:\\[\s\S]|[^\\"])*"|'(?:\\[\s\S]|[^\\'])*'|[^\s{'"/>=]+|))?|))**\/?)?>/.source),e.languages.jsx.tag.inside.tag.pattern=/^<\/?[^\s>\/]*/,e.languages.jsx.tag.inside["attr-value"].pattern=/=(?!\{)(?:"(?:\\[\s\S]|[^\\"])*"|'(?:\\[\s\S]|[^\\'])*'|[^\s'">]+)/,e.languages.jsx.tag.inside.tag.inside["class-name"]=/^[A-Z]\w*(?:\.[A-Z]\w*)*$/,e.languages.jsx.tag.inside.comment=t.comment,e.languages.insertBefore("inside","attr-name",{spread:{pattern:o(//.source),inside:e.languages.jsx}},e.languages.jsx.tag),e.languages.insertBefore("inside","special-attr",{script:{pattern:o(/=/.source),alias:"language-javascript",inside:{"script-punctuation":{pattern:/^=(?=\{)/,alias:"punctuation"},rest:e.languages.jsx}}},e.languages.jsx.tag);var i=function(e){return e?"string"==typeof e?e:"string"==typeof e.content?e.content:e.content.map(i).join(""):""},l=function(t){for(var n=[],r=0;r0&&n[n.length-1].tagName===i(a.content[0].content[1])&&n.pop():"/>"===a.content[a.content.length-1].content||n.push({tagName:i(a.content[0].content[1]),openedBraces:0}):n.length>0&&"punctuation"===a.type&&"{"===a.content?n[n.length-1].openedBraces++:n.length>0&&n[n.length-1].openedBraces>0&&"punctuation"===a.type&&"}"===a.content?n[n.length-1].openedBraces--:o=!0),(o||"string"==typeof a)&&n.length>0&&0===n[n.length-1].openedBraces){var s=i(a);r0&&("string"==typeof t[r-1]||"plain-text"===t[r-1].type)&&(s=i(t[r-1])+s,t.splice(r-1,1),r--),t[r]=new e.Token("plain-text",s,null,s)}a.content&&"string"!=typeof a.content&&l(a.content)}};e.hooks.add("after-tokenize",(function(e){"jsx"!==e.language&&"tsx"!==e.language||l(e.tokens)}))}(a),function(e){e.languages.diff={coord:[/^(?:\*{3}|-{3}|\+{3}).*$/m,/^@@.*@@$/m,/^\d.*$/m]};var t={"deleted-sign":"-","deleted-arrow":"<","inserted-sign":"+","inserted-arrow":">",unchanged:" ",diff:"!"};Object.keys(t).forEach((function(n){var r=t[n],a=[];/^\w+$/.test(n)||a.push(/\w+/.exec(n)[0]),"diff"===n&&a.push("bold"),e.languages.diff[n]={pattern:RegExp("^(?:["+r+"].*(?:\r\n?|\n|(?![\\s\\S])))+","m"),alias:a,inside:{line:{pattern:/(.)(?=[\s\S]).*(?:\r\n?|\n)?/,lookbehind:!0},prefix:{pattern:/[\s\S]/,alias:/\w+/.exec(n)[0]}}}})),Object.defineProperty(e.languages.diff,"PREFIXES",{value:t})}(a),a.languages.git={comment:/^#.*/m,deleted:/^[-\u2013].*/m,inserted:/^\+.*/m,string:/("|')(?:\\.|(?!\1)[^\\\r\n])*\1/,command:{pattern:/^.*\$ git .*$/m,inside:{parameter:/\s--?\w+/}},coord:/^@@.*@@$/m,"commit-sha1":/^commit \w{40}$/m},a.languages.go=a.languages.extend("clike",{string:{pattern:/(^|[^\\])"(?:\\.|[^"\\\r\n])*"|`[^`]*`/,lookbehind:!0,greedy:!0},keyword:/\b(?:break|case|chan|const|continue|default|defer|else|fallthrough|for|func|go(?:to)?|if|import|interface|map|package|range|return|select|struct|switch|type|var)\b/,boolean:/\b(?:_|false|iota|nil|true)\b/,number:[/\b0(?:b[01_]+|o[0-7_]+)i?\b/i,/\b0x(?:[a-f\d_]+(?:\.[a-f\d_]*)?|\.[a-f\d_]+)(?:p[+-]?\d+(?:_\d+)*)?i?(?!\w)/i,/(?:\b\d[\d_]*(?:\.[\d_]*)?|\B\.\d[\d_]*)(?:e[+-]?[\d_]+)?i?(?!\w)/i],operator:/[*\/%^!=]=?|\+[=+]?|-[=-]?|\|[=|]?|&(?:=|&|\^=?)?|>(?:>=?|=)?|<(?:<=?|=|-)?|:=|\.\.\./,builtin:/\b(?:append|bool|byte|cap|close|complex|complex(?:64|128)|copy|delete|error|float(?:32|64)|u?int(?:8|16|32|64)?|imag|len|make|new|panic|print(?:ln)?|real|recover|rune|string|uintptr)\b/}),a.languages.insertBefore("go","string",{char:{pattern:/'(?:\\.|[^'\\\r\n]){0,10}'/,greedy:!0}}),delete a.languages.go["class-name"],function(e){function t(e,t){return"___"+e.toUpperCase()+t+"___"}Object.defineProperties(e.languages["markup-templating"]={},{buildPlaceholders:{value:function(n,r,a,o){if(n.language===r){var i=n.tokenStack=[];n.code=n.code.replace(a,(function(e){if("function"==typeof o&&!o(e))return e;for(var a,l=i.length;-1!==n.code.indexOf(a=t(r,l));)++l;return i[l]=e,a})),n.grammar=e.languages.markup}}},tokenizePlaceholders:{value:function(n,r){if(n.language===r&&n.tokenStack){n.grammar=e.languages[r];var a=0,o=Object.keys(n.tokenStack);!function i(l){for(var s=0;s=o.length);s++){var u=l[s];if("string"==typeof u||u.content&&"string"==typeof u.content){var c=o[a],d=n.tokenStack[c],f="string"==typeof u?u:u.content,p=t(r,c),m=f.indexOf(p);if(m>-1){++a;var h=f.substring(0,m),g=new e.Token(r,e.tokenize(d,n.grammar),"language-"+r,d),v=f.substring(m+p.length),b=[];h&&b.push.apply(b,i([h])),b.push(g),v&&b.push.apply(b,i([v])),"string"==typeof u?l.splice.apply(l,[s,1].concat(b)):u.content=b}}else u.content&&i(u.content)}return l}(n.tokens)}}}})}(a),function(e){e.languages.handlebars={comment:/\{\{![\s\S]*?\}\}/,delimiter:{pattern:/^\{\{\{?|\}\}\}?$/,alias:"punctuation"},string:/(["'])(?:\\.|(?!\1)[^\\\r\n])*\1/,number:/\b0x[\dA-Fa-f]+\b|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:[Ee][+-]?\d+)?/,boolean:/\b(?:false|true)\b/,block:{pattern:/^(\s*(?:~\s*)?)[#\/]\S+?(?=\s*(?:~\s*)?$|\s)/,lookbehind:!0,alias:"keyword"},brackets:{pattern:/\[[^\]]+\]/,inside:{punctuation:/\[|\]/,variable:/[\s\S]+/}},punctuation:/[!"#%&':()*+,.\/;<=>@\[\\\]^`{|}~]/,variable:/[^!"#%&'()*+,\/;<=>@\[\\\]^`{|}~\s]+/},e.hooks.add("before-tokenize",(function(t){e.languages["markup-templating"].buildPlaceholders(t,"handlebars",/\{\{\{[\s\S]+?\}\}\}|\{\{[\s\S]+?\}\}/g)})),e.hooks.add("after-tokenize",(function(t){e.languages["markup-templating"].tokenizePlaceholders(t,"handlebars")})),e.languages.hbs=e.languages.handlebars}(a),a.languages.json={property:{pattern:/(^|[^\\])"(?:\\.|[^\\"\r\n])*"(?=\s*:)/,lookbehind:!0,greedy:!0},string:{pattern:/(^|[^\\])"(?:\\.|[^\\"\r\n])*"(?!\s*:)/,lookbehind:!0,greedy:!0},comment:{pattern:/\/\/.*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},number:/-?\b\d+(?:\.\d+)?(?:e[+-]?\d+)?\b/i,punctuation:/[{}[\],]/,operator:/:/,boolean:/\b(?:false|true)\b/,null:{pattern:/\bnull\b/,alias:"keyword"}},a.languages.webmanifest=a.languages.json,a.languages.less=a.languages.extend("css",{comment:[/\/\*[\s\S]*?\*\//,{pattern:/(^|[^\\])\/\/.*/,lookbehind:!0}],atrule:{pattern:/@[\w-](?:\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{punctuation:/[:()]/}},selector:{pattern:/(?:@\{[\w-]+\}|[^{};\s@])(?:@\{[\w-]+\}|\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};@\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{variable:/@+[\w-]+/}},property:/(?:@\{[\w-]+\}|[\w-])+(?:\+_?)?(?=\s*:)/,operator:/[+\-*\/]/}),a.languages.insertBefore("less","property",{variable:[{pattern:/@[\w-]+\s*:/,inside:{punctuation:/:/}},/@@?[\w-]+/],"mixin-usage":{pattern:/([{;]\s*)[.#](?!\d)[\w-].*?(?=[(;])/,lookbehind:!0,alias:"function"}}),a.languages.makefile={comment:{pattern:/(^|[^\\])#(?:\\(?:\r\n|[\s\S])|[^\\\r\n])*/,lookbehind:!0},string:{pattern:/(["'])(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,greedy:!0},"builtin-target":{pattern:/\.[A-Z][^:#=\s]+(?=\s*:(?!=))/,alias:"builtin"},target:{pattern:/^(?:[^:=\s]|[ \t]+(?![\s:]))+(?=\s*:(?!=))/m,alias:"symbol",inside:{variable:/\$+(?:(?!\$)[^(){}:#=\s]+|(?=[({]))/}},variable:/\$+(?:(?!\$)[^(){}:#=\s]+|\([@*%<^+?][DF]\)|(?=[({]))/,keyword:/-include\b|\b(?:define|else|endef|endif|export|ifn?def|ifn?eq|include|override|private|sinclude|undefine|unexport|vpath)\b/,function:{pattern:/(\()(?:abspath|addsuffix|and|basename|call|dir|error|eval|file|filter(?:-out)?|findstring|firstword|flavor|foreach|guile|if|info|join|lastword|load|notdir|or|origin|patsubst|realpath|shell|sort|strip|subst|suffix|value|warning|wildcard|word(?:list|s)?)(?=[ \t])/,lookbehind:!0},operator:/(?:::|[?:+!])?=|[|@]/,punctuation:/[:;(){}]/},a.languages.objectivec=a.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete a.languages.objectivec["class-name"],a.languages.objc=a.languages.objectivec,a.languages.ocaml={comment:{pattern:/\(\*[\s\S]*?\*\)/,greedy:!0},char:{pattern:/'(?:[^\\\r\n']|\\(?:.|[ox]?[0-9a-f]{1,3}))'/i,greedy:!0},string:[{pattern:/"(?:\\(?:[\s\S]|\r\n)|[^\\\r\n"])*"/,greedy:!0},{pattern:/\{([a-z_]*)\|[\s\S]*?\|\1\}/,greedy:!0}],number:[/\b(?:0b[01][01_]*|0o[0-7][0-7_]*)\b/i,/\b0x[a-f0-9][a-f0-9_]*(?:\.[a-f0-9_]*)?(?:p[+-]?\d[\d_]*)?(?!\w)/i,/\b\d[\d_]*(?:\.[\d_]*)?(?:e[+-]?\d[\d_]*)?(?!\w)/i],directive:{pattern:/\B#\w+/,alias:"property"},label:{pattern:/\B~\w+/,alias:"property"},"type-variable":{pattern:/\B'\w+/,alias:"function"},variant:{pattern:/`\w+/,alias:"symbol"},keyword:/\b(?:as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|match|method|module|mutable|new|nonrec|object|of|open|private|rec|sig|struct|then|to|try|type|val|value|virtual|when|where|while|with)\b/,boolean:/\b(?:false|true)\b/,"operator-like-punctuation":{pattern:/\[[<>|]|[>|]\]|\{<|>\}/,alias:"punctuation"},operator:/\.[.~]|:[=>]|[=<>@^|&+\-*\/$%!?~][!$%&*+\-.\/:<=>?@^|~]*|\b(?:and|asr|land|lor|lsl|lsr|lxor|mod|or)\b/,punctuation:/;;|::|[(){}\[\].,:;#]|\b_\b/},a.languages.python={comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0,greedy:!0},"string-interpolation":{pattern:/(?:f|fr|rf)(?:("""|''')[\s\S]*?\1|("|')(?:\\.|(?!\2)[^\\\r\n])*\2)/i,greedy:!0,inside:{interpolation:{pattern:/((?:^|[^{])(?:\{\{)*)\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}])+\})+\})+\}/,lookbehind:!0,inside:{"format-spec":{pattern:/(:)[^:(){}]+(?=\}$)/,lookbehind:!0},"conversion-option":{pattern:/![sra](?=[:}]$)/,alias:"punctuation"},rest:null}},string:/[\s\S]+/}},"triple-quoted-string":{pattern:/(?:[rub]|br|rb)?("""|''')[\s\S]*?\1/i,greedy:!0,alias:"string"},string:{pattern:/(?:[rub]|br|rb)?("|')(?:\\.|(?!\1)[^\\\r\n])*\1/i,greedy:!0},function:{pattern:/((?:^|\s)def[ \t]+)[a-zA-Z_]\w*(?=\s*\()/g,lookbehind:!0},"class-name":{pattern:/(\bclass\s+)\w+/i,lookbehind:!0},decorator:{pattern:/(^[\t ]*)@\w+(?:\.\w+)*/m,lookbehind:!0,alias:["annotation","punctuation"],inside:{punctuation:/\./}},keyword:/\b(?:_(?=\s*:)|and|as|assert|async|await|break|case|class|continue|def|del|elif|else|except|exec|finally|for|from|global|if|import|in|is|lambda|match|nonlocal|not|or|pass|print|raise|return|try|while|with|yield)\b/,builtin:/\b(?:__import__|abs|all|any|apply|ascii|basestring|bin|bool|buffer|bytearray|bytes|callable|chr|classmethod|cmp|coerce|compile|complex|delattr|dict|dir|divmod|enumerate|eval|execfile|file|filter|float|format|frozenset|getattr|globals|hasattr|hash|help|hex|id|input|int|intern|isinstance|issubclass|iter|len|list|locals|long|map|max|memoryview|min|next|object|oct|open|ord|pow|property|range|raw_input|reduce|reload|repr|reversed|round|set|setattr|slice|sorted|staticmethod|str|sum|super|tuple|type|unichr|unicode|vars|xrange|zip)\b/,boolean:/\b(?:False|None|True)\b/,number:/\b0(?:b(?:_?[01])+|o(?:_?[0-7])+|x(?:_?[a-f0-9])+)\b|(?:\b\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\B\.\d+(?:_\d+)*)(?:e[+-]?\d+(?:_\d+)*)?j?(?!\w)/i,operator:/[-+%=]=?|!=|:=|\*\*?=?|\/\/?=?|<[<=>]?|>[=>]?|[&|^~]/,punctuation:/[{}[\];(),.:]/},a.languages.python["string-interpolation"].inside.interpolation.inside.rest=a.languages.python,a.languages.py=a.languages.python,a.languages.reason=a.languages.extend("clike",{string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^\\\r\n"])*"/,greedy:!0},"class-name":/\b[A-Z]\w*/,keyword:/\b(?:and|as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|method|module|mutable|new|nonrec|object|of|open|or|private|rec|sig|struct|switch|then|to|try|type|val|virtual|when|while|with)\b/,operator:/\.{3}|:[:=]|\|>|->|=(?:==?|>)?|<=?|>=?|[|^?'#!~`]|[+\-*\/]\.?|\b(?:asr|land|lor|lsl|lsr|lxor|mod)\b/}),a.languages.insertBefore("reason","class-name",{char:{pattern:/'(?:\\x[\da-f]{2}|\\o[0-3][0-7][0-7]|\\\d{3}|\\.|[^'\\\r\n])'/,greedy:!0},constructor:/\b[A-Z]\w*\b(?!\s*\.)/,label:{pattern:/\b[a-z]\w*(?=::)/,alias:"symbol"}}),delete a.languages.reason.function,function(e){e.languages.sass=e.languages.extend("css",{comment:{pattern:/^([ \t]*)\/[\/*].*(?:(?:\r?\n|\r)\1[ \t].+)*/m,lookbehind:!0,greedy:!0}}),e.languages.insertBefore("sass","atrule",{"atrule-line":{pattern:/^(?:[ \t]*)[@+=].+/m,greedy:!0,inside:{atrule:/(?:@[\w-]+|[+=])/}}}),delete e.languages.sass.atrule;var t=/\$[-\w]+|#\{\$[-\w]+\}/,n=[/[+*\/%]|[=!]=|<=?|>=?|\b(?:and|not|or)\b/,{pattern:/(\s)-(?=\s)/,lookbehind:!0}];e.languages.insertBefore("sass","property",{"variable-line":{pattern:/^[ \t]*\$.+/m,greedy:!0,inside:{punctuation:/:/,variable:t,operator:n}},"property-line":{pattern:/^[ \t]*(?:[^:\s]+ *:.*|:[^:\s].*)/m,greedy:!0,inside:{property:[/[^:\s]+(?=\s*:)/,{pattern:/(:)[^:\s]+/,lookbehind:!0}],punctuation:/:/,variable:t,operator:n,important:e.languages.sass.important}}}),delete e.languages.sass.property,delete e.languages.sass.important,e.languages.insertBefore("sass","punctuation",{selector:{pattern:/^([ \t]*)\S(?:,[^,\r\n]+|[^,\r\n]*)(?:,[^,\r\n]+)*(?:,(?:\r?\n|\r)\1[ \t]+\S(?:,[^,\r\n]+|[^,\r\n]*)(?:,[^,\r\n]+)*)*/m,lookbehind:!0,greedy:!0}})}(a),a.languages.scss=a.languages.extend("css",{comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0},atrule:{pattern:/@[\w-](?:\([^()]+\)|[^()\s]|\s+(?!\s))*?(?=\s+[{;])/,inside:{rule:/@[\w-]+/}},url:/(?:[-a-z]+-)?url(?=\()/i,selector:{pattern:/(?=\S)[^@;{}()]?(?:[^@;{}()\s]|\s+(?!\s)|#\{\$[-\w]+\})+(?=\s*\{(?:\}|\s|[^}][^:{}]*[:{][^}]))/,inside:{parent:{pattern:/&/,alias:"important"},placeholder:/%[-\w]+/,variable:/\$[-\w]+|#\{\$[-\w]+\}/}},property:{pattern:/(?:[-\w]|\$[-\w]|#\{\$[-\w]+\})+(?=\s*:)/,inside:{variable:/\$[-\w]+|#\{\$[-\w]+\}/}}}),a.languages.insertBefore("scss","atrule",{keyword:[/@(?:content|debug|each|else(?: if)?|extend|for|forward|function|if|import|include|mixin|return|use|warn|while)\b/i,{pattern:/( )(?:from|through)(?= )/,lookbehind:!0}]}),a.languages.insertBefore("scss","important",{variable:/\$[-\w]+|#\{\$[-\w]+\}/}),a.languages.insertBefore("scss","function",{"module-modifier":{pattern:/\b(?:as|hide|show|with)\b/i,alias:"keyword"},placeholder:{pattern:/%[-\w]+/,alias:"selector"},statement:{pattern:/\B!(?:default|optional)\b/i,alias:"keyword"},boolean:/\b(?:false|true)\b/,null:{pattern:/\bnull\b/,alias:"keyword"},operator:{pattern:/(\s)(?:[-+*\/%]|[=!]=|<=?|>=?|and|not|or)(?=\s)/,lookbehind:!0}}),a.languages.scss.atrule.inside.rest=a.languages.scss,function(e){var t={pattern:/(\b\d+)(?:%|[a-z]+)/,lookbehind:!0},n={pattern:/(^|[^\w.-])-?(?:\d+(?:\.\d+)?|\.\d+)/,lookbehind:!0},r={comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0},url:{pattern:/\burl\((["']?).*?\1\)/i,greedy:!0},string:{pattern:/("|')(?:(?!\1)[^\\\r\n]|\\(?:\r\n|[\s\S]))*\1/,greedy:!0},interpolation:null,func:null,important:/\B!(?:important|optional)\b/i,keyword:{pattern:/(^|\s+)(?:(?:else|for|if|return|unless)(?=\s|$)|@[\w-]+)/,lookbehind:!0},hexcode:/#[\da-f]{3,6}/i,color:[/\b(?:AliceBlue|AntiqueWhite|Aqua|Aquamarine|Azure|Beige|Bisque|Black|BlanchedAlmond|Blue|BlueViolet|Brown|BurlyWood|CadetBlue|Chartreuse|Chocolate|Coral|CornflowerBlue|Cornsilk|Crimson|Cyan|DarkBlue|DarkCyan|DarkGoldenRod|DarkGr[ae]y|DarkGreen|DarkKhaki|DarkMagenta|DarkOliveGreen|DarkOrange|DarkOrchid|DarkRed|DarkSalmon|DarkSeaGreen|DarkSlateBlue|DarkSlateGr[ae]y|DarkTurquoise|DarkViolet|DeepPink|DeepSkyBlue|DimGr[ae]y|DodgerBlue|FireBrick|FloralWhite|ForestGreen|Fuchsia|Gainsboro|GhostWhite|Gold|GoldenRod|Gr[ae]y|Green|GreenYellow|HoneyDew|HotPink|IndianRed|Indigo|Ivory|Khaki|Lavender|LavenderBlush|LawnGreen|LemonChiffon|LightBlue|LightCoral|LightCyan|LightGoldenRodYellow|LightGr[ae]y|LightGreen|LightPink|LightSalmon|LightSeaGreen|LightSkyBlue|LightSlateGr[ae]y|LightSteelBlue|LightYellow|Lime|LimeGreen|Linen|Magenta|Maroon|MediumAquaMarine|MediumBlue|MediumOrchid|MediumPurple|MediumSeaGreen|MediumSlateBlue|MediumSpringGreen|MediumTurquoise|MediumVioletRed|MidnightBlue|MintCream|MistyRose|Moccasin|NavajoWhite|Navy|OldLace|Olive|OliveDrab|Orange|OrangeRed|Orchid|PaleGoldenRod|PaleGreen|PaleTurquoise|PaleVioletRed|PapayaWhip|PeachPuff|Peru|Pink|Plum|PowderBlue|Purple|Red|RosyBrown|RoyalBlue|SaddleBrown|Salmon|SandyBrown|SeaGreen|SeaShell|Sienna|Silver|SkyBlue|SlateBlue|SlateGr[ae]y|Snow|SpringGreen|SteelBlue|Tan|Teal|Thistle|Tomato|Transparent|Turquoise|Violet|Wheat|White|WhiteSmoke|Yellow|YellowGreen)\b/i,{pattern:/\b(?:hsl|rgb)\(\s*\d{1,3}\s*,\s*\d{1,3}%?\s*,\s*\d{1,3}%?\s*\)\B|\b(?:hsl|rgb)a\(\s*\d{1,3}\s*,\s*\d{1,3}%?\s*,\s*\d{1,3}%?\s*,\s*(?:0|0?\.\d+|1)\s*\)\B/i,inside:{unit:t,number:n,function:/[\w-]+(?=\()/,punctuation:/[(),]/}}],entity:/\\[\da-f]{1,8}/i,unit:t,boolean:/\b(?:false|true)\b/,operator:[/~|[+!\/%<>?=]=?|[-:]=|\*[*=]?|\.{2,3}|&&|\|\||\B-\B|\b(?:and|in|is(?: a| defined| not|nt)?|not|or)\b/],number:n,punctuation:/[{}()\[\];:,]/};r.interpolation={pattern:/\{[^\r\n}:]+\}/,alias:"variable",inside:{delimiter:{pattern:/^\{|\}$/,alias:"punctuation"},rest:r}},r.func={pattern:/[\w-]+\([^)]*\).*/,inside:{function:/^[^(]+/,rest:r}},e.languages.stylus={"atrule-declaration":{pattern:/(^[ \t]*)@.+/m,lookbehind:!0,inside:{atrule:/^@[\w-]+/,rest:r}},"variable-declaration":{pattern:/(^[ \t]*)[\w$-]+\s*.?=[ \t]*(?:\{[^{}]*\}|\S.*|$)/m,lookbehind:!0,inside:{variable:/^\S+/,rest:r}},statement:{pattern:/(^[ \t]*)(?:else|for|if|return|unless)[ \t].+/m,lookbehind:!0,inside:{keyword:/^\S+/,rest:r}},"property-declaration":{pattern:/((?:^|\{)([ \t]*))(?:[\w-]|\{[^}\r\n]+\})+(?:\s*:\s*|[ \t]+)(?!\s)[^{\r\n]*(?:;|[^{\r\n,]$(?!(?:\r?\n|\r)(?:\{|\2[ \t])))/m,lookbehind:!0,inside:{property:{pattern:/^[^\s:]+/,inside:{interpolation:r.interpolation}},rest:r}},selector:{pattern:/(^[ \t]*)(?:(?=\S)(?:[^{}\r\n:()]|::?[\w-]+(?:\([^)\r\n]*\)|(?![\w-]))|\{[^}\r\n]+\})+)(?:(?:\r?\n|\r)(?:\1(?:(?=\S)(?:[^{}\r\n:()]|::?[\w-]+(?:\([^)\r\n]*\)|(?![\w-]))|\{[^}\r\n]+\})+)))*(?:,$|\{|(?=(?:\r?\n|\r)(?:\{|\1[ \t])))/m,lookbehind:!0,inside:{interpolation:r.interpolation,comment:r.comment,punctuation:/[{},]/}},func:r.func,string:r.string,comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0,greedy:!0},interpolation:r.interpolation,punctuation:/[{}()\[\];:.]/}}(a),function(e){var t=e.util.clone(e.languages.typescript);e.languages.tsx=e.languages.extend("jsx",t),delete e.languages.tsx.parameter,delete e.languages.tsx["literal-property"];var n=e.languages.tsx.tag;n.pattern=RegExp(/(^|[^\w$]|(?=<\/))/.source+"(?:"+n.pattern.source+")",n.pattern.flags),n.lookbehind=!0}(a),a.languages.wasm={comment:[/\(;[\s\S]*?;\)/,{pattern:/;;.*/,greedy:!0}],string:{pattern:/"(?:\\[\s\S]|[^"\\])*"/,greedy:!0},keyword:[{pattern:/\b(?:align|offset)=/,inside:{operator:/=/}},{pattern:/\b(?:(?:f32|f64|i32|i64)(?:\.(?:abs|add|and|ceil|clz|const|convert_[su]\/i(?:32|64)|copysign|ctz|demote\/f64|div(?:_[su])?|eqz?|extend_[su]\/i32|floor|ge(?:_[su])?|gt(?:_[su])?|le(?:_[su])?|load(?:(?:8|16|32)_[su])?|lt(?:_[su])?|max|min|mul|neg?|nearest|or|popcnt|promote\/f32|reinterpret\/[fi](?:32|64)|rem_[su]|rot[lr]|shl|shr_[su]|sqrt|store(?:8|16|32)?|sub|trunc(?:_[su]\/f(?:32|64))?|wrap\/i64|xor))?|memory\.(?:grow|size))\b/,inside:{punctuation:/\./}},/\b(?:anyfunc|block|br(?:_if|_table)?|call(?:_indirect)?|data|drop|elem|else|end|export|func|get_(?:global|local)|global|if|import|local|loop|memory|module|mut|nop|offset|param|result|return|select|set_(?:global|local)|start|table|tee_local|then|type|unreachable)\b/],variable:/\$[\w!#$%&'*+\-./:<=>?@\\^`|~]+/,number:/[+-]?\b(?:\d(?:_?\d)*(?:\.\d(?:_?\d)*)?(?:[eE][+-]?\d(?:_?\d)*)?|0x[\da-fA-F](?:_?[\da-fA-F])*(?:\.[\da-fA-F](?:_?[\da-fA-D])*)?(?:[pP][+-]?\d(?:_?\d)*)?)\b|\binf\b|\bnan(?::0x[\da-fA-F](?:_?[\da-fA-D])*)?\b/,punctuation:/[()]/};const o=a},9901:e=>{e.exports&&(e.exports={core:{meta:{path:"components/prism-core.js",option:"mandatory"},core:"Core"},themes:{meta:{path:"themes/{id}.css",link:"index.html?theme={id}",exclusive:!0},prism:{title:"Default",option:"default"},"prism-dark":"Dark","prism-funky":"Funky","prism-okaidia":{title:"Okaidia",owner:"ocodia"},"prism-twilight":{title:"Twilight",owner:"remybach"},"prism-coy":{title:"Coy",owner:"tshedor"},"prism-solarizedlight":{title:"Solarized Light",owner:"hectormatos2011 "},"prism-tomorrow":{title:"Tomorrow Night",owner:"Rosey"}},languages:{meta:{path:"components/prism-{id}",noCSS:!0,examplesPath:"examples/prism-{id}",addCheckAll:!0},markup:{title:"Markup",alias:["html","xml","svg","mathml","ssml","atom","rss"],aliasTitles:{html:"HTML",xml:"XML",svg:"SVG",mathml:"MathML",ssml:"SSML",atom:"Atom",rss:"RSS"},option:"default"},css:{title:"CSS",option:"default",modify:"markup"},clike:{title:"C-like",option:"default"},javascript:{title:"JavaScript",require:"clike",modify:"markup",optional:"regex",alias:"js",option:"default"},abap:{title:"ABAP",owner:"dellagustin"},abnf:{title:"ABNF",owner:"RunDevelopment"},actionscript:{title:"ActionScript",require:"javascript",modify:"markup",owner:"Golmote"},ada:{title:"Ada",owner:"Lucretia"},agda:{title:"Agda",owner:"xy-ren"},al:{title:"AL",owner:"RunDevelopment"},antlr4:{title:"ANTLR4",alias:"g4",owner:"RunDevelopment"},apacheconf:{title:"Apache Configuration",owner:"GuiTeK"},apex:{title:"Apex",require:["clike","sql"],owner:"RunDevelopment"},apl:{title:"APL",owner:"ngn"},applescript:{title:"AppleScript",owner:"Golmote"},aql:{title:"AQL",owner:"RunDevelopment"},arduino:{title:"Arduino",require:"cpp",alias:"ino",owner:"dkern"},arff:{title:"ARFF",owner:"Golmote"},armasm:{title:"ARM Assembly",alias:"arm-asm",owner:"RunDevelopment"},arturo:{title:"Arturo",alias:"art",optional:["bash","css","javascript","markup","markdown","sql"],owner:"drkameleon"},asciidoc:{alias:"adoc",title:"AsciiDoc",owner:"Golmote"},aspnet:{title:"ASP.NET (C#)",require:["markup","csharp"],owner:"nauzilus"},asm6502:{title:"6502 Assembly",owner:"kzurawel"},asmatmel:{title:"Atmel AVR Assembly",owner:"cerkit"},autohotkey:{title:"AutoHotkey",owner:"aviaryan"},autoit:{title:"AutoIt",owner:"Golmote"},avisynth:{title:"AviSynth",alias:"avs",owner:"Zinfidel"},"avro-idl":{title:"Avro IDL",alias:"avdl",owner:"RunDevelopment"},awk:{title:"AWK",alias:"gawk",aliasTitles:{gawk:"GAWK"},owner:"RunDevelopment"},bash:{title:"Bash",alias:["sh","shell"],aliasTitles:{sh:"Shell",shell:"Shell"},owner:"zeitgeist87"},basic:{title:"BASIC",owner:"Golmote"},batch:{title:"Batch",owner:"Golmote"},bbcode:{title:"BBcode",alias:"shortcode",aliasTitles:{shortcode:"Shortcode"},owner:"RunDevelopment"},bbj:{title:"BBj",owner:"hyyan"},bicep:{title:"Bicep",owner:"johnnyreilly"},birb:{title:"Birb",require:"clike",owner:"Calamity210"},bison:{title:"Bison",require:"c",owner:"Golmote"},bnf:{title:"BNF",alias:"rbnf",aliasTitles:{rbnf:"RBNF"},owner:"RunDevelopment"},bqn:{title:"BQN",owner:"yewscion"},brainfuck:{title:"Brainfuck",owner:"Golmote"},brightscript:{title:"BrightScript",owner:"RunDevelopment"},bro:{title:"Bro",owner:"wayward710"},bsl:{title:"BSL (1C:Enterprise)",alias:"oscript",aliasTitles:{oscript:"OneScript"},owner:"Diversus23"},c:{title:"C",require:"clike",owner:"zeitgeist87"},csharp:{title:"C#",require:"clike",alias:["cs","dotnet"],owner:"mvalipour"},cpp:{title:"C++",require:"c",owner:"zeitgeist87"},cfscript:{title:"CFScript",require:"clike",alias:"cfc",owner:"mjclemente"},chaiscript:{title:"ChaiScript",require:["clike","cpp"],owner:"RunDevelopment"},cil:{title:"CIL",owner:"sbrl"},cilkc:{title:"Cilk/C",require:"c",alias:"cilk-c",owner:"OpenCilk"},cilkcpp:{title:"Cilk/C++",require:"cpp",alias:["cilk-cpp","cilk"],owner:"OpenCilk"},clojure:{title:"Clojure",owner:"troglotit"},cmake:{title:"CMake",owner:"mjrogozinski"},cobol:{title:"COBOL",owner:"RunDevelopment"},coffeescript:{title:"CoffeeScript",require:"javascript",alias:"coffee",owner:"R-osey"},concurnas:{title:"Concurnas",alias:"conc",owner:"jasontatton"},csp:{title:"Content-Security-Policy",owner:"ScottHelme"},cooklang:{title:"Cooklang",owner:"ahue"},coq:{title:"Coq",owner:"RunDevelopment"},crystal:{title:"Crystal",require:"ruby",owner:"MakeNowJust"},"css-extras":{title:"CSS Extras",require:"css",modify:"css",owner:"milesj"},csv:{title:"CSV",owner:"RunDevelopment"},cue:{title:"CUE",owner:"RunDevelopment"},cypher:{title:"Cypher",owner:"RunDevelopment"},d:{title:"D",require:"clike",owner:"Golmote"},dart:{title:"Dart",require:"clike",owner:"Golmote"},dataweave:{title:"DataWeave",owner:"machaval"},dax:{title:"DAX",owner:"peterbud"},dhall:{title:"Dhall",owner:"RunDevelopment"},diff:{title:"Diff",owner:"uranusjr"},django:{title:"Django/Jinja2",require:"markup-templating",alias:"jinja2",owner:"romanvm"},"dns-zone-file":{title:"DNS zone file",owner:"RunDevelopment",alias:"dns-zone"},docker:{title:"Docker",alias:"dockerfile",owner:"JustinBeckwith"},dot:{title:"DOT (Graphviz)",alias:"gv",optional:"markup",owner:"RunDevelopment"},ebnf:{title:"EBNF",owner:"RunDevelopment"},editorconfig:{title:"EditorConfig",owner:"osipxd"},eiffel:{title:"Eiffel",owner:"Conaclos"},ejs:{title:"EJS",require:["javascript","markup-templating"],owner:"RunDevelopment",alias:"eta",aliasTitles:{eta:"Eta"}},elixir:{title:"Elixir",owner:"Golmote"},elm:{title:"Elm",owner:"zwilias"},etlua:{title:"Embedded Lua templating",require:["lua","markup-templating"],owner:"RunDevelopment"},erb:{title:"ERB",require:["ruby","markup-templating"],owner:"Golmote"},erlang:{title:"Erlang",owner:"Golmote"},"excel-formula":{title:"Excel Formula",alias:["xlsx","xls"],owner:"RunDevelopment"},fsharp:{title:"F#",require:"clike",owner:"simonreynolds7"},factor:{title:"Factor",owner:"catb0t"},false:{title:"False",owner:"edukisto"},"firestore-security-rules":{title:"Firestore security rules",require:"clike",owner:"RunDevelopment"},flow:{title:"Flow",require:"javascript",owner:"Golmote"},fortran:{title:"Fortran",owner:"Golmote"},ftl:{title:"FreeMarker Template Language",require:"markup-templating",owner:"RunDevelopment"},gml:{title:"GameMaker Language",alias:"gamemakerlanguage",require:"clike",owner:"LiarOnce"},gap:{title:"GAP (CAS)",owner:"RunDevelopment"},gcode:{title:"G-code",owner:"RunDevelopment"},gdscript:{title:"GDScript",owner:"RunDevelopment"},gedcom:{title:"GEDCOM",owner:"Golmote"},gettext:{title:"gettext",alias:"po",owner:"RunDevelopment"},gherkin:{title:"Gherkin",owner:"hason"},git:{title:"Git",owner:"lgiraudel"},glsl:{title:"GLSL",require:"c",owner:"Golmote"},gn:{title:"GN",alias:"gni",owner:"RunDevelopment"},"linker-script":{title:"GNU Linker Script",alias:"ld",owner:"RunDevelopment"},go:{title:"Go",require:"clike",owner:"arnehormann"},"go-module":{title:"Go module",alias:"go-mod",owner:"RunDevelopment"},gradle:{title:"Gradle",require:"clike",owner:"zeabdelkhalek-badido18"},graphql:{title:"GraphQL",optional:"markdown",owner:"Golmote"},groovy:{title:"Groovy",require:"clike",owner:"robfletcher"},haml:{title:"Haml",require:"ruby",optional:["css","css-extras","coffeescript","erb","javascript","less","markdown","scss","textile"],owner:"Golmote"},handlebars:{title:"Handlebars",require:"markup-templating",alias:["hbs","mustache"],aliasTitles:{mustache:"Mustache"},owner:"Golmote"},haskell:{title:"Haskell",alias:"hs",owner:"bholst"},haxe:{title:"Haxe",require:"clike",optional:"regex",owner:"Golmote"},hcl:{title:"HCL",owner:"outsideris"},hlsl:{title:"HLSL",require:"c",owner:"RunDevelopment"},hoon:{title:"Hoon",owner:"matildepark"},http:{title:"HTTP",optional:["csp","css","hpkp","hsts","javascript","json","markup","uri"],owner:"danielgtaylor"},hpkp:{title:"HTTP Public-Key-Pins",owner:"ScottHelme"},hsts:{title:"HTTP Strict-Transport-Security",owner:"ScottHelme"},ichigojam:{title:"IchigoJam",owner:"BlueCocoa"},icon:{title:"Icon",owner:"Golmote"},"icu-message-format":{title:"ICU Message Format",owner:"RunDevelopment"},idris:{title:"Idris",alias:"idr",owner:"KeenS",require:"haskell"},ignore:{title:".ignore",owner:"osipxd",alias:["gitignore","hgignore","npmignore"],aliasTitles:{gitignore:".gitignore",hgignore:".hgignore",npmignore:".npmignore"}},inform7:{title:"Inform 7",owner:"Golmote"},ini:{title:"Ini",owner:"aviaryan"},io:{title:"Io",owner:"AlesTsurko"},j:{title:"J",owner:"Golmote"},java:{title:"Java",require:"clike",owner:"sherblot"},javadoc:{title:"JavaDoc",require:["markup","java","javadoclike"],modify:"java",optional:"scala",owner:"RunDevelopment"},javadoclike:{title:"JavaDoc-like",modify:["java","javascript","php"],owner:"RunDevelopment"},javastacktrace:{title:"Java stack trace",owner:"RunDevelopment"},jexl:{title:"Jexl",owner:"czosel"},jolie:{title:"Jolie",require:"clike",owner:"thesave"},jq:{title:"JQ",owner:"RunDevelopment"},jsdoc:{title:"JSDoc",require:["javascript","javadoclike","typescript"],modify:"javascript",optional:["actionscript","coffeescript"],owner:"RunDevelopment"},"js-extras":{title:"JS Extras",require:"javascript",modify:"javascript",optional:["actionscript","coffeescript","flow","n4js","typescript"],owner:"RunDevelopment"},json:{title:"JSON",alias:"webmanifest",aliasTitles:{webmanifest:"Web App Manifest"},owner:"CupOfTea696"},json5:{title:"JSON5",require:"json",owner:"RunDevelopment"},jsonp:{title:"JSONP",require:"json",owner:"RunDevelopment"},jsstacktrace:{title:"JS stack trace",owner:"sbrl"},"js-templates":{title:"JS Templates",require:"javascript",modify:"javascript",optional:["css","css-extras","graphql","markdown","markup","sql"],owner:"RunDevelopment"},julia:{title:"Julia",owner:"cdagnino"},keepalived:{title:"Keepalived Configure",owner:"dev-itsheng"},keyman:{title:"Keyman",owner:"mcdurdin"},kotlin:{title:"Kotlin",alias:["kt","kts"],aliasTitles:{kts:"Kotlin Script"},require:"clike",owner:"Golmote"},kumir:{title:"KuMir (\u041a\u0443\u041c\u0438\u0440)",alias:"kum",owner:"edukisto"},kusto:{title:"Kusto",owner:"RunDevelopment"},latex:{title:"LaTeX",alias:["tex","context"],aliasTitles:{tex:"TeX",context:"ConTeXt"},owner:"japborst"},latte:{title:"Latte",require:["clike","markup-templating","php"],owner:"nette"},less:{title:"Less",require:"css",optional:"css-extras",owner:"Golmote"},lilypond:{title:"LilyPond",require:"scheme",alias:"ly",owner:"RunDevelopment"},liquid:{title:"Liquid",require:"markup-templating",owner:"cinhtau"},lisp:{title:"Lisp",alias:["emacs","elisp","emacs-lisp"],owner:"JuanCaicedo"},livescript:{title:"LiveScript",owner:"Golmote"},llvm:{title:"LLVM IR",owner:"porglezomp"},log:{title:"Log file",optional:"javastacktrace",owner:"RunDevelopment"},lolcode:{title:"LOLCODE",owner:"Golmote"},lua:{title:"Lua",owner:"Golmote"},magma:{title:"Magma (CAS)",owner:"RunDevelopment"},makefile:{title:"Makefile",owner:"Golmote"},markdown:{title:"Markdown",require:"markup",optional:"yaml",alias:"md",owner:"Golmote"},"markup-templating":{title:"Markup templating",require:"markup",owner:"Golmote"},mata:{title:"Mata",owner:"RunDevelopment"},matlab:{title:"MATLAB",owner:"Golmote"},maxscript:{title:"MAXScript",owner:"RunDevelopment"},mel:{title:"MEL",owner:"Golmote"},mermaid:{title:"Mermaid",owner:"RunDevelopment"},metafont:{title:"METAFONT",owner:"LaeriExNihilo"},mizar:{title:"Mizar",owner:"Golmote"},mongodb:{title:"MongoDB",owner:"airs0urce",require:"javascript"},monkey:{title:"Monkey",owner:"Golmote"},moonscript:{title:"MoonScript",alias:"moon",owner:"RunDevelopment"},n1ql:{title:"N1QL",owner:"TMWilds"},n4js:{title:"N4JS",require:"javascript",optional:"jsdoc",alias:"n4jsd",owner:"bsmith-n4"},"nand2tetris-hdl":{title:"Nand To Tetris HDL",owner:"stephanmax"},naniscript:{title:"Naninovel Script",owner:"Elringus",alias:"nani"},nasm:{title:"NASM",owner:"rbmj"},neon:{title:"NEON",owner:"nette"},nevod:{title:"Nevod",owner:"nezaboodka"},nginx:{title:"nginx",owner:"volado"},nim:{title:"Nim",owner:"Golmote"},nix:{title:"Nix",owner:"Golmote"},nsis:{title:"NSIS",owner:"idleberg"},objectivec:{title:"Objective-C",require:"c",alias:"objc",owner:"uranusjr"},ocaml:{title:"OCaml",owner:"Golmote"},odin:{title:"Odin",owner:"edukisto"},opencl:{title:"OpenCL",require:"c",modify:["c","cpp"],owner:"Milania1"},openqasm:{title:"OpenQasm",alias:"qasm",owner:"RunDevelopment"},oz:{title:"Oz",owner:"Golmote"},parigp:{title:"PARI/GP",owner:"Golmote"},parser:{title:"Parser",require:"markup",owner:"Golmote"},pascal:{title:"Pascal",alias:"objectpascal",aliasTitles:{objectpascal:"Object Pascal"},owner:"Golmote"},pascaligo:{title:"Pascaligo",owner:"DefinitelyNotAGoat"},psl:{title:"PATROL Scripting Language",owner:"bertysentry"},pcaxis:{title:"PC-Axis",alias:"px",owner:"RunDevelopment"},peoplecode:{title:"PeopleCode",alias:"pcode",owner:"RunDevelopment"},perl:{title:"Perl",owner:"Golmote"},php:{title:"PHP",require:"markup-templating",owner:"milesj"},phpdoc:{title:"PHPDoc",require:["php","javadoclike"],modify:"php",owner:"RunDevelopment"},"php-extras":{title:"PHP Extras",require:"php",modify:"php",owner:"milesj"},"plant-uml":{title:"PlantUML",alias:"plantuml",owner:"RunDevelopment"},plsql:{title:"PL/SQL",require:"sql",owner:"Golmote"},powerquery:{title:"PowerQuery",alias:["pq","mscript"],owner:"peterbud"},powershell:{title:"PowerShell",owner:"nauzilus"},processing:{title:"Processing",require:"clike",owner:"Golmote"},prolog:{title:"Prolog",owner:"Golmote"},promql:{title:"PromQL",owner:"arendjr"},properties:{title:".properties",owner:"Golmote"},protobuf:{title:"Protocol Buffers",require:"clike",owner:"just-boris"},pug:{title:"Pug",require:["markup","javascript"],optional:["coffeescript","ejs","handlebars","less","livescript","markdown","scss","stylus","twig"],owner:"Golmote"},puppet:{title:"Puppet",owner:"Golmote"},pure:{title:"Pure",optional:["c","cpp","fortran"],owner:"Golmote"},purebasic:{title:"PureBasic",require:"clike",alias:"pbfasm",owner:"HeX0R101"},purescript:{title:"PureScript",require:"haskell",alias:"purs",owner:"sriharshachilakapati"},python:{title:"Python",alias:"py",owner:"multipetros"},qsharp:{title:"Q#",require:"clike",alias:"qs",owner:"fedonman"},q:{title:"Q (kdb+ database)",owner:"Golmote"},qml:{title:"QML",require:"javascript",owner:"RunDevelopment"},qore:{title:"Qore",require:"clike",owner:"temnroegg"},r:{title:"R",owner:"Golmote"},racket:{title:"Racket",require:"scheme",alias:"rkt",owner:"RunDevelopment"},cshtml:{title:"Razor C#",alias:"razor",require:["markup","csharp"],optional:["css","css-extras","javascript","js-extras"],owner:"RunDevelopment"},jsx:{title:"React JSX",require:["markup","javascript"],optional:["jsdoc","js-extras","js-templates"],owner:"vkbansal"},tsx:{title:"React TSX",require:["jsx","typescript"]},reason:{title:"Reason",require:"clike",owner:"Golmote"},regex:{title:"Regex",owner:"RunDevelopment"},rego:{title:"Rego",owner:"JordanSh"},renpy:{title:"Ren'py",alias:"rpy",owner:"HyuchiaDiego"},rescript:{title:"ReScript",alias:"res",owner:"vmarcosp"},rest:{title:"reST (reStructuredText)",owner:"Golmote"},rip:{title:"Rip",owner:"ravinggenius"},roboconf:{title:"Roboconf",owner:"Golmote"},robotframework:{title:"Robot Framework",alias:"robot",owner:"RunDevelopment"},ruby:{title:"Ruby",require:"clike",alias:"rb",owner:"samflores"},rust:{title:"Rust",owner:"Golmote"},sas:{title:"SAS",optional:["groovy","lua","sql"],owner:"Golmote"},sass:{title:"Sass (Sass)",require:"css",optional:"css-extras",owner:"Golmote"},scss:{title:"Sass (SCSS)",require:"css",optional:"css-extras",owner:"MoOx"},scala:{title:"Scala",require:"java",owner:"jozic"},scheme:{title:"Scheme",owner:"bacchus123"},"shell-session":{title:"Shell session",require:"bash",alias:["sh-session","shellsession"],owner:"RunDevelopment"},smali:{title:"Smali",owner:"RunDevelopment"},smalltalk:{title:"Smalltalk",owner:"Golmote"},smarty:{title:"Smarty",require:"markup-templating",optional:"php",owner:"Golmote"},sml:{title:"SML",alias:"smlnj",aliasTitles:{smlnj:"SML/NJ"},owner:"RunDevelopment"},solidity:{title:"Solidity (Ethereum)",alias:"sol",require:"clike",owner:"glachaud"},"solution-file":{title:"Solution file",alias:"sln",owner:"RunDevelopment"},soy:{title:"Soy (Closure Template)",require:"markup-templating",owner:"Golmote"},sparql:{title:"SPARQL",require:"turtle",owner:"Triply-Dev",alias:"rq"},"splunk-spl":{title:"Splunk SPL",owner:"RunDevelopment"},sqf:{title:"SQF: Status Quo Function (Arma 3)",require:"clike",owner:"RunDevelopment"},sql:{title:"SQL",owner:"multipetros"},squirrel:{title:"Squirrel",require:"clike",owner:"RunDevelopment"},stan:{title:"Stan",owner:"RunDevelopment"},stata:{title:"Stata Ado",require:["mata","java","python"],owner:"RunDevelopment"},iecst:{title:"Structured Text (IEC 61131-3)",owner:"serhioromano"},stylus:{title:"Stylus",owner:"vkbansal"},supercollider:{title:"SuperCollider",alias:"sclang",owner:"RunDevelopment"},swift:{title:"Swift",owner:"chrischares"},systemd:{title:"Systemd configuration file",owner:"RunDevelopment"},"t4-templating":{title:"T4 templating",owner:"RunDevelopment"},"t4-cs":{title:"T4 Text Templates (C#)",require:["t4-templating","csharp"],alias:"t4",owner:"RunDevelopment"},"t4-vb":{title:"T4 Text Templates (VB)",require:["t4-templating","vbnet"],owner:"RunDevelopment"},tap:{title:"TAP",owner:"isaacs",require:"yaml"},tcl:{title:"Tcl",owner:"PeterChaplin"},tt2:{title:"Template Toolkit 2",require:["clike","markup-templating"],owner:"gflohr"},textile:{title:"Textile",require:"markup",optional:"css",owner:"Golmote"},toml:{title:"TOML",owner:"RunDevelopment"},tremor:{title:"Tremor",alias:["trickle","troy"],owner:"darach",aliasTitles:{trickle:"trickle",troy:"troy"}},turtle:{title:"Turtle",alias:"trig",aliasTitles:{trig:"TriG"},owner:"jakubklimek"},twig:{title:"Twig",require:"markup-templating",owner:"brandonkelly"},typescript:{title:"TypeScript",require:"javascript",optional:"js-templates",alias:"ts",owner:"vkbansal"},typoscript:{title:"TypoScript",alias:"tsconfig",aliasTitles:{tsconfig:"TSConfig"},owner:"dkern"},unrealscript:{title:"UnrealScript",alias:["uscript","uc"],owner:"RunDevelopment"},uorazor:{title:"UO Razor Script",owner:"jaseowns"},uri:{title:"URI",alias:"url",aliasTitles:{url:"URL"},owner:"RunDevelopment"},v:{title:"V",require:"clike",owner:"taggon"},vala:{title:"Vala",require:"clike",optional:"regex",owner:"TemplarVolk"},vbnet:{title:"VB.Net",require:"basic",owner:"Bigsby"},velocity:{title:"Velocity",require:"markup",owner:"Golmote"},verilog:{title:"Verilog",owner:"a-rey"},vhdl:{title:"VHDL",owner:"a-rey"},vim:{title:"vim",owner:"westonganger"},"visual-basic":{title:"Visual Basic",alias:["vb","vba"],aliasTitles:{vba:"VBA"},owner:"Golmote"},warpscript:{title:"WarpScript",owner:"RunDevelopment"},wasm:{title:"WebAssembly",owner:"Golmote"},"web-idl":{title:"Web IDL",alias:"webidl",owner:"RunDevelopment"},wgsl:{title:"WGSL",owner:"Dr4gonthree"},wiki:{title:"Wiki markup",require:"markup",owner:"Golmote"},wolfram:{title:"Wolfram language",alias:["mathematica","nb","wl"],aliasTitles:{mathematica:"Mathematica",nb:"Mathematica Notebook"},owner:"msollami"},wren:{title:"Wren",owner:"clsource"},xeora:{title:"Xeora",require:"markup",alias:"xeoracube",aliasTitles:{xeoracube:"XeoraCube"},owner:"freakmaxi"},"xml-doc":{title:"XML doc (.net)",require:"markup",modify:["csharp","fsharp","vbnet"],owner:"RunDevelopment"},xojo:{title:"Xojo (REALbasic)",owner:"Golmote"},xquery:{title:"XQuery",require:"markup",owner:"Golmote"},yaml:{title:"YAML",alias:"yml",owner:"hason"},yang:{title:"YANG",owner:"RunDevelopment"},zig:{title:"Zig",owner:"RunDevelopment"}},plugins:{meta:{path:"plugins/{id}/prism-{id}",link:"plugins/{id}/"},"line-highlight":{title:"Line Highlight",description:"Highlights specific lines and/or line ranges."},"line-numbers":{title:"Line Numbers",description:"Line number at the beginning of code lines.",owner:"kuba-kubula"},"show-invisibles":{title:"Show Invisibles",description:"Show hidden characters such as tabs and line breaks.",optional:["autolinker","data-uri-highlight"]},autolinker:{title:"Autolinker",description:"Converts URLs and emails in code to clickable links. Parses Markdown links in comments."},wpd:{title:"WebPlatform Docs",description:'Makes tokens link to WebPlatform.org documentation. The links open in a new tab.'},"custom-class":{title:"Custom Class",description:"This plugin allows you to prefix Prism's default classes (.comment can become .namespace--comment) or replace them with your defined ones (like .editor__comment). You can even add new classes.",owner:"dvkndn",noCSS:!0},"file-highlight":{title:"File Highlight",description:"Fetch external files and highlight them with Prism. Used on the Prism website itself.",noCSS:!0},"show-language":{title:"Show Language",description:"Display the highlighted language in code blocks (inline code does not show the label).",owner:"nauzilus",noCSS:!0,require:"toolbar"},"jsonp-highlight":{title:"JSONP Highlight",description:"Fetch content with JSONP and highlight some interesting content (e.g. GitHub/Gists or Bitbucket API).",noCSS:!0,owner:"nauzilus"},"highlight-keywords":{title:"Highlight Keywords",description:"Adds special CSS classes for each keyword for fine-grained highlighting.",owner:"vkbansal",noCSS:!0},"remove-initial-line-feed":{title:"Remove initial line feed",description:"Removes the initial line feed in code blocks.",owner:"Golmote",noCSS:!0},"inline-color":{title:"Inline color",description:"Adds a small inline preview for colors in style sheets.",require:"css-extras",owner:"RunDevelopment"},previewers:{title:"Previewers",description:"Previewers for angles, colors, gradients, easing and time.",require:"css-extras",owner:"Golmote"},autoloader:{title:"Autoloader",description:"Automatically loads the needed languages to highlight the code blocks.",owner:"Golmote",noCSS:!0},"keep-markup":{title:"Keep Markup",description:"Prevents custom markup from being dropped out during highlighting.",owner:"Golmote",optional:"normalize-whitespace",noCSS:!0},"command-line":{title:"Command Line",description:"Display a command line with a prompt and, optionally, the output/response from the commands.",owner:"chriswells0"},"unescaped-markup":{title:"Unescaped Markup",description:"Write markup without having to escape anything."},"normalize-whitespace":{title:"Normalize Whitespace",description:"Supports multiple operations to normalize whitespace in code blocks.",owner:"zeitgeist87",optional:"unescaped-markup",noCSS:!0},"data-uri-highlight":{title:"Data-URI Highlight",description:"Highlights data-URI contents.",owner:"Golmote",noCSS:!0},toolbar:{title:"Toolbar",description:"Attach a toolbar for plugins to easily register buttons on the top of a code block.",owner:"mAAdhaTTah"},"copy-to-clipboard":{title:"Copy to Clipboard Button",description:"Add a button that copies the code block to the clipboard when clicked.",owner:"mAAdhaTTah",require:"toolbar",noCSS:!0},"download-button":{title:"Download Button",description:"A button in the toolbar of a code block adding a convenient way to download a code file.",owner:"Golmote",require:"toolbar",noCSS:!0},"match-braces":{title:"Match braces",description:"Highlights matching braces.",owner:"RunDevelopment"},"diff-highlight":{title:"Diff Highlight",description:"Highlights the code inside diff blocks.",owner:"RunDevelopment",require:"diff"},"filter-highlight-all":{title:"Filter highlightAll",description:"Filters the elements the highlightAll and highlightAllUnder methods actually highlight.",owner:"RunDevelopment",noCSS:!0},treeview:{title:"Treeview",description:"A language with special styles to highlight file system tree structures.",owner:"Golmote"}}})},2885:(e,t,n)=>{const r=n(9901),a=n(9642),o=new Set;function i(e){void 0===e?e=Object.keys(r.languages).filter((e=>"meta"!=e)):Array.isArray(e)||(e=[e]);const t=[...o,...Object.keys(Prism.languages)];a(r,e,t).load((e=>{if(!(e in r.languages))return void(i.silent||console.warn("Language does not exist: "+e));const t="./prism-"+e;delete n.c[n(6500).resolve(t)],delete Prism.languages[e],n(6500)(t),o.add(e)}))}i.silent=!1,e.exports=i},6726:(e,t,n)=>{var r={"./":2885};function a(e){var t=o(e);return n(t)}function o(e){if(!n.o(r,e)){var t=new Error("Cannot find module '"+e+"'");throw t.code="MODULE_NOT_FOUND",t}return r[e]}a.keys=function(){return Object.keys(r)},a.resolve=o,e.exports=a,a.id=6726},6500:(e,t,n)=>{var r={"./":2885};function a(e){var t=o(e);return n(t)}function o(e){if(!n.o(r,e)){var t=new Error("Cannot find module '"+e+"'");throw t.code="MODULE_NOT_FOUND",t}return r[e]}a.keys=function(){return Object.keys(r)},a.resolve=o,e.exports=a,a.id=6500},9642:e=>{"use strict";var t=function(){var e=function(){};function t(e,t){Array.isArray(e)?e.forEach(t):null!=e&&t(e,0)}function n(e){for(var t={},n=0,r=e.length;n "));var l={},s=e[r];if(s){function u(t){if(!(t in e))throw new Error(r+" depends on an unknown component "+t);if(!(t in l))for(var i in a(t,o),l[t]=!0,n[t])l[i]=!0}t(s.require,u),t(s.optional,u),t(s.modify,u)}n[r]=l,o.pop()}}return function(e){var t=n[e];return t||(a(e,r),t=n[e]),t}}function a(e){for(var t in e)return!0;return!1}return function(o,i,l){var s=function(e){var t={};for(var n in e){var r=e[n];for(var a in r)if("meta"!=a){var o=r[a];t[a]="string"==typeof o?{title:o}:o}}return t}(o),u=function(e){var n;return function(r){if(r in e)return r;if(!n)for(var a in n={},e){var o=e[a];t(o&&o.alias,(function(t){if(t in n)throw new Error(t+" cannot be alias for both "+a+" and "+n[t]);if(t in e)throw new Error(t+" cannot be alias of "+a+" because it is a component.");n[t]=a}))}return n[r]||r}}(s);i=i.map(u),l=(l||[]).map(u);var c=n(i),d=n(l);i.forEach((function e(n){var r=s[n];t(r&&r.require,(function(t){t in d||(c[t]=!0,e(t))}))}));for(var f,p=r(s),m=c;a(m);){for(var h in f={},m){var g=s[h];t(g&&g.modify,(function(e){e in d&&(f[e]=!0)}))}for(var v in d)if(!(v in c))for(var b in p(v))if(b in c){f[v]=!0;break}for(var y in m=f)c[y]=!0}var w={getIds:function(){var e=[];return w.load((function(t){e.push(t)})),e},load:function(t,n){return function(t,n,r,a){var o=a?a.series:void 0,i=a?a.parallel:e,l={},s={};function u(e){if(e in l)return l[e];s[e]=!0;var a,c=[];for(var d in t(e))d in n&&c.push(d);if(0===c.length)a=r(e);else{var f=i(c.map((function(e){var t=u(e);return delete s[e],t})));o?a=o(f,(function(){return r(e)})):r(e)}return l[e]=a}for(var c in n)u(c);var d=[];for(var f in s)d.push(l[f]);return i(d)}(p,c,t,n)}};return w}}();e.exports=t},2703:(e,t,n)=>{"use strict";var r=n(414);function a(){}function o(){}o.resetWarningCache=a,e.exports=function(){function e(e,t,n,a,o,i){if(i!==r){var l=new Error("Calling PropTypes validators directly is not supported by the `prop-types` package. Use PropTypes.checkPropTypes() to call them. Read more at http://fb.me/use-check-prop-types");throw l.name="Invariant Violation",l}}function t(){return e}e.isRequired=e;var n={array:e,bigint:e,bool:e,func:e,number:e,object:e,string:e,symbol:e,any:e,arrayOf:t,element:e,elementType:e,instanceOf:t,node:e,objectOf:t,oneOf:t,oneOfType:t,shape:t,exact:t,checkPropTypes:o,resetWarningCache:a};return n.PropTypes=n,n}},5697:(e,t,n)=>{e.exports=n(2703)()},414:e=>{"use strict";e.exports="SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED"},4448:(e,t,n)=>{"use strict";var r=n(7294),a=n(7418),o=n(3840);function i(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n