diff --git a/WizardCoder/data/humaneval.59.8.gen.zip b/WizardCoder/data/humaneval.59.8.gen.zip deleted file mode 100644 index 3e8f8ab..0000000 Binary files a/WizardCoder/data/humaneval.59.8.gen.zip and /dev/null differ diff --git a/WizardCoder/data/mbpp.test.zip b/WizardCoder/data/mbpp.test.zip deleted file mode 100644 index f7a98fa..0000000 Binary files a/WizardCoder/data/mbpp.test.zip and /dev/null differ diff --git a/WizardCoder/download.py b/WizardCoder/download.py new file mode 100644 index 0000000..e15039b --- /dev/null +++ b/WizardCoder/download.py @@ -0,0 +1,14 @@ + +# Load model directly +from transformers import AutoTokenizer, AutoModelForCausalLM + +import os +cdir=os.getcwd() +print(cdir) + +tokenizer = AutoTokenizer.from_pretrained("WizardLM/WizardCoder-15B-V1.0", cache_dir=cdir) +print(tokenizer) + +model = AutoModelForCausalLM.from_pretrained("WizardLM/WizardCoder-15B-V1.0", cache_dir=cdir) +print(model) +print(sum(p.numel() for p in model.parameters())) diff --git a/WizardCoder/inf.sh b/WizardCoder/inf.sh index 792862c..4d17f42 100644 --- a/WizardCoder/inf.sh +++ b/WizardCoder/inf.sh @@ -2,15 +2,17 @@ # File Name: inf.sh # Author: Xianchao Wu # mail: xianchaow@nvidia.com -# Created Time: Mon Jul 3 04:13:18 2023 +# Created Time: Fri Jul 28 07:55:20 2023 ######################################################################### #!/bin/bash -ckpt="/workspace/asr/Llama-X/src/checkpoints_wcode/models--WizardLM--WizardCoder-15B-V1.0/snapshots/69e87732535159460155972c3fac32a6241cc0ca" -indata="/workspace/asr/WizardLM/WizardCoder/data/in.data.jsonl" -outdata="/workspace/asr/WizardLM/WizardCoder/data/out.res.jsonl" +bmodel="/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4 +631bc5f8c3e47173381452c23e5c" +inpath="./data/in.jsonl" +outpath="./data/out.jsonl" python -m ipdb src/inference_wizardcoder.py \ - --base_model $ckpt \ - --input_data_path $indata \ - --output_data_path $outdata + --base_model $bmodel \ + --input_data_path $inpath \ + --output_data_path $outpath + diff --git a/WizardCoder/inf_a100.sh b/WizardCoder/inf_a100.sh new file mode 100644 index 0000000..bb8fec0 --- /dev/null +++ b/WizardCoder/inf_a100.sh @@ -0,0 +1,17 @@ +######################################################################### +# File Name: inf.sh +# Author: Xianchao Wu +# mail: xianchaow@nvidia.com +# Created Time: Mon Jul 3 04:13:18 2023 +######################################################################### +#!/bin/bash + +ckpt="/workspace/asr/Llama-X/src/checkpoints_wcode/models--WizardLM--WizardCoder-15B-V1.0/snapshots/69e87732535159460155972c3fac32a6241cc0ca" +inpath="./data/in.jsonl" +outpath="./data/out.jsonl" + +python -m ipdb src/inference_wizardcoder.py \ + --base_model $ckpt \ + --input_data_path $inpath \ + --output_data_path $outpath + diff --git a/WizardCoder/mbpp_1.sh b/WizardCoder/mbpp_1.sh new file mode 100644 index 0000000..9a8f4d3 --- /dev/null +++ b/WizardCoder/mbpp_1.sh @@ -0,0 +1,66 @@ +######################################################################### +# File Name: mbpp_1.sh +# Author: Xianchao Wu +# mail: xianchaow@nvidia.com +# Created Time: Thu Aug 10 06:39:18 2023 +######################################################################### +#!/bin/bash + +#model="/path/to/your/model" +model="/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4631bc5f8c3e47173381452c23e5c" +temp=0.2 # temperature, TODO reset this 温度 +max_len=2048 +pred_num=200 +num_seqs_per_iter=2 + +output_path=preds/MBPP_T${temp}_N${pred_num} +mbpp_path=data/mbpp.test.jsonl # we provide this file in data/mbpp.test.zip + +mkdir -p ${output_path} +echo 'Output path: '$output_path +echo 'Model to eval: '$model + +# for debug NOTE +debug=0 +if [[ $debug == 1 ]] +then + gpu=1 + start_index=0 + end_index=2 + + CUDA_VISIBLE_DEVICES=$gpu python -m ipdb src/mbpp_gen.py --model ${model} \ + --start_index ${start_index} \ + --end_index ${end_index} \ + --temperature ${temp} \ + --num_seqs_per_iter ${num_seqs_per_iter} \ + --N ${pred_num} \ + --max_len ${max_len} \ + --output_path ${output_path} \ + --mbpp_path ${mbpp_path} + + exit 0 +fi + +# 500 problems, 63 per GPU if GPU=8 +index=0 +gpu_num=8 +for ((i = 0; i < $gpu_num; i++)); do + start_index=$((i * 50)) + end_index=$(((i + 1) * 50)) + + gpu=$((i)) + echo 'Running process #' ${i} 'from' $start_index 'to' $end_index 'on GPU' ${gpu} + ((index++)) + ( + CUDA_VISIBLE_DEVICES=$gpu python src/mbpp_gen.py --model ${model} \ + --start_index ${start_index} \ + --end_index ${end_index} \ + --temperature ${temp} \ + --num_seqs_per_iter ${num_seqs_per_iter} \ + --N ${pred_num} \ + --max_len ${max_len} \ + --output_path ${output_path} \ + --mbpp_path ${mbpp_path} + ) & + if (($index % $gpu_num == 0)); then wait; fi +done diff --git a/WizardCoder/src/inference_wizardcoder.py b/WizardCoder/src/inference_wizardcoder.py index 5eebed7..d24e3c9 100644 --- a/WizardCoder/src/inference_wizardcoder.py +++ b/WizardCoder/src/inference_wizardcoder.py @@ -15,73 +15,73 @@ try: if torch.backends.mps.is_available(): - device = "mps" + device = "mps" # metal performance shaders (MPS) 苹果Apple公司的MPS作为pytorch的后端 except: pass def evaluate( - batch_data, - tokenizer, - model, + batch_data, # 'Write a Python code to count 1 to 10.' + tokenizer, # GPT2TokenizerFast(name_or_path='/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4631bc5f8c3e47173381452c23e5c', vocab_size=49152, model_max_length=2048, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '[PAD]', 'additional_special_tokens': ['<|endoftext|>', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']}, clean_up_tokenization_spaces=True) + model, # type(model)= input=None, temperature=1, top_p=0.9, top_k=40, num_beams=1, max_new_tokens=2048, - **kwargs, + **kwargs, # {} ): prompts = generate_prompt(batch_data, input) - inputs = tokenizer(prompts, return_tensors="pt", max_length=256, truncation=True, padding=True) + inputs = tokenizer(prompts, return_tensors="pt", max_length=256, truncation=True, padding=True) # ipdb> p inputs: {'input_ids': tensor([[27400, 438, 600, 12404, 688, 18872, 312, 2899, 32, 5950, 312, 1789, 688, 36808, 30772, 322, 1326, 32, 203, 203, 1482, 21081, 44, 203, 2538, 312, 4865, 1340, 372, 2385, 225, 35, 372, 225, 35, 34, 32, 203, 203, 1482, 5170, 44]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])} ipdb> inputs['input_ids'].shape = torch.Size([1, 42]), ipdb> inputs['attention_mask'].shape = torch.Size([1, 42]) input_ids = inputs["input_ids"].to(device) generation_config = GenerationConfig( - temperature=temperature, - top_p=top_p, - top_k=top_k, - num_beams=num_beams, - eos_token_id=tokenizer.eos_token_id, - pad_token_id=tokenizer.pad_token_id, - **kwargs, + temperature=temperature, # 1 + top_p=top_p, # 0.9 + top_k=top_k, # 40 + num_beams=num_beams, # 1 + eos_token_id=tokenizer.eos_token_id, # 0 + pad_token_id=tokenizer.pad_token_id, # 49152 + **kwargs, # {} ) import ipdb; ipdb.set_trace() with torch.no_grad(): - generation_output = model.generate( - input_ids=input_ids, + generation_output = model.generate( # NOTE, > /opt/conda/lib/python3.8/site-packages/transformers/generation/utils.py(1160)generate() + input_ids=input_ids, # [1, 42], batch-size=1 generation_config=generation_config, return_dict_in_generate=True, output_scores=True, - max_new_tokens=max_new_tokens, + max_new_tokens=max_new_tokens, # 2048 ) - s = generation_output.sequences + s = generation_output.sequences # size=[1, 220], alike=tensor([[27400, 438, 600, 12404, 688, 18872, 312, 2899, 32, 5950, ...]], device='cuda:0') output = tokenizer.batch_decode(s, skip_special_tokens=True) - return output + return output # ["Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a Python code to count 1 to 10.\n\n### Response:Here's the Python code to count 1 to 10:\r\n\r\n```python\r\nfor i in range(1, 11):\r\n print(i)\r\n```\r\n\r\nOutput:\r\n\r\n```\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n7\r\n8\r\n9\r\n10\r\n```\r\n\r\nExplanation:\r\n\r\n- The `range()` function generates a sequence of numbers from the starting value (inclusive) to the ending value (exclusive).\r\n- In this case, we start with 1 and go up to 11 (exclusive) because we want to count 10.\r\n- The `for` loop iterates over each number in the sequence and assigns it to the variable `i`.\r\n- The `print()` function outputs the value of `i` on a new line."] NOTE 可以看到这里的输出output，也包括了最初的输入的prompt和instruction. -def generate_prompt(instruction, input=None): +def generate_prompt(instruction, input=None): # NOTE input这个参数没有被使用到 return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} -### Response:""" +### Response:""" # out = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a Python code to count 1 to 10.\n\n### Response:' def main( - load_8bit: bool = False, - base_model: str = "Model_Path", - input_data_path = "Input.jsonl", - output_data_path = "Output.jsonl", + load_8bit: bool = False, # False + base_model: str = "Model_Path", # '/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4631bc5f8c3e47173381452c23e5c' + input_data_path = "Input.jsonl", # './data/in.jsonl' + output_data_path = "Output.jsonl", # './data/out.jsonl' ): assert base_model, ( - "Please specify a --base_model, e.g. --base_model='bigcode/starcoder'" + "Please specify a --base_model, e.g. --base_model='bigcode/starcoder'" # NOTE 这里支持具体的model name或者本地的model path ) - tokenizer = AutoTokenizer.from_pretrained(base_model) + tokenizer = AutoTokenizer.from_pretrained(base_model) # GPT2TokenizerFast(name_or_path='/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4631bc5f8c3e47173381452c23e5c', vocab_size=49152, model_max_length=2048, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '[PAD]', 'additional_special_tokens': ['<|endoftext|>', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']}, clean_up_tokenization_spaces=True) if device == "cuda": model = AutoModelForCausalLM.from_pretrained( base_model, - load_in_8bit=load_8bit, - torch_dtype=torch.float16, + load_in_8bit=load_8bit, # False + torch_dtype=torch.float16, # TODO 应该通过命令行管理 device_map="auto", ) elif device == "mps": @@ -91,31 +91,31 @@ def main( torch_dtype=torch.float16, ) - model.config.pad_token_id = tokenizer.pad_token_id + model.config.pad_token_id = tokenizer.pad_token_id # 49152 if not load_8bit: - model.half() + model.half() # NOTE in, 本来就是float16导入的，这个half()没有导致什么变化... model.eval() - if torch.__version__ >= "2" and sys.platform != "win32": + if torch.__version__ >= "2" and sys.platform != "win32": # '1.13.0+cu116', 'linux'; so not in model = torch.compile(model) input_data = jsonlines.open(input_data_path, mode='r') output_data = jsonlines.open(output_data_path, mode='w') for num, line in enumerate(input_data): - one_data = line + one_data = line # {'idx': 11, 'Instruction': 'Write a Python code to count 1 to 10.'} id = one_data["idx"] instruction = one_data["Instruction"] print(instruction) - _output = evaluate(instruction, tokenizer, model) - final_output = _output[0].split("### Response:")[1].strip() + _output = evaluate(instruction, tokenizer, model) # NOTE + final_output = _output[0].split("### Response:")[1].strip() # 因为这里是逐行循环的，所以_output中只可能有一个元素! NOTE final_output="Here's the Python code to count 1 to 10:\r\n\r\n```python\r\nfor i in range(1, 11):\r\n print(i)\r\n```\r\n\r\nOutput:\r\n\r\n```\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n7\r\n8\r\n9\r\n10\r\n```\r\n\r\nExplanation:\r\n\r\n- The `range()` function generates a sequence of numbers from the starting value (inclusive) to the ending value (exclusive).\r\n- In this case, we start with 1 and go up to 11 (exclusive) because we want to count 10.\r\n- The `for` loop iterates over each number in the sequence and assigns it to the variable `i`.\r\n- The `print()` function outputs the value of `i` on a new line." new_data = { - "id": id, - "instruction": instruction, - "wizardcoder": final_output + "id": id, # 11 + "instruction": instruction, # 'Write a Python code to count 1 to 10.' + "wizardcoder": final_output # "Here's the Python code to count 1 to 10:\r\n\r\n```python\r\nfor i in range(1, 11):\r\n print(i)\r\n```\r\n\r\nOutput:\r\n\r\n```\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n7\r\n8\r\n9\r\n10\r\n```\r\n\r\nExplanation:\r\n\r\n- The `range()` function generates a sequence of numbers from the starting value (inclusive) to the ending value (exclusive).\r\n- In this case, we start with 1 and go up to 11 (exclusive) because we want to count 10.\r\n- The `for` loop iterates over each number in the sequence and assigns it to the variable `i`.\r\n- The `print()` function outputs the value of `i` on a new line." } - output_data.write(new_data) + output_data.write(new_data) # {'id': 11, 'instruction': 'Write a Python code to count 1 to 10.', 'wizardcoder': "Here's the Python code to count 1 to 10:\r\n\r\n```python\r\nfor i in range(1, 11):\r\n print(i)\r\n```\r\n\r\nOutput:\r\n\r\n```\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n7\r\n8\r\n9\r\n10\r\n```\r\n\r\nExplanation:\r\n\r\n- The `range()` function generates a sequence of numbers from the starting value (inclusive) to the ending value (exclusive).\r\n- In this case, we start with 1 and go up to 11 (exclusive) because we want to count 10.\r\n- The `for` loop iterates over each number in the sequence and assigns it to the variable `i`.\r\n- The `print()` function outputs the value of `i` on a new line."} if __name__ == "__main__": diff --git a/WizardCoder/src/mbpp_gen.py b/WizardCoder/src/mbpp_gen.py index 9b12bcd..d358603 100644 --- a/WizardCoder/src/mbpp_gen.py +++ b/WizardCoder/src/mbpp_gen.py @@ -25,7 +25,7 @@ def read_mbpp(path): with jsonlines.open(path, "r") as fin: for obj in fin: mbpp_problems[obj["task_id"]] = obj - return mbpp_problems + return mbpp_problems # 500 个问题 def extract_text(prompt, remove_lines=True): token = '\"\"\"' @@ -50,7 +50,7 @@ def generate_prompt(input): {input} ### Response:""" - return INSTRUCTION + return INSTRUCTION # e.g., 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a Python script for this problem:\n\nWrite a python function to remove first and last occurrence of a given character from the string.\nTest examples:\nassert remove_Occ("hello","l") == "heo"\nassert remove_Occ("abcda","a") == "bcd"\nassert remove_Occ("PHP","P") == "H"\n\n### Response:' def get_model( load_8bit: bool = False, @@ -60,21 +60,21 @@ def get_model( "Please specify a --base_model, e.g. --base_model='bigcode/starcoder'" ) - tokenizer = AutoTokenizer.from_pretrained(base_model) + tokenizer = AutoTokenizer.from_pretrained(base_model) # GPT2TokenizerFast(name_or_path='/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4631bc5f8c3e47173381452c23e5c', vocab_size=49152, model_max_length=2048, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '[PAD]', 'additional_special_tokens': ['<|endoftext|>', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']}, clean_up_tokenization_spaces=True) if device == "cuda": model = AutoModelForCausalLM.from_pretrained( base_model, - load_in_8bit=load_8bit, + load_in_8bit=load_8bit, # False torch_dtype=torch.float16, device_map="auto", - ) + ) # 15,517,462,528 -> 155亿参数的一个coding model NOTE elif device == "mps": model = AutoModelForCausalLM.from_pretrained( base_model, device_map={"": device}, torch_dtype=torch.float16, ) - model.config.pad_token_id = tokenizer.pad_token_id + model.config.pad_token_id = tokenizer.pad_token_id # 49152 if not load_8bit: model.half() # seems to fix bugs for some users. @@ -88,7 +88,7 @@ def get_model( def main(): parser = argparse.ArgumentParser() - + #import ipdb; ipdb.set_trace() parser.add_argument('--model', type=str, default='bigcode/starcoder', help="") parser.add_argument('--output_path', type=str, help="") parser.add_argument('--start_index', type=int, default=0, help="") @@ -101,7 +101,7 @@ def main(): parser.add_argument('--overwrite', action='store_true', help='') parser.add_argument('--mbpp_path', type=str, help="") - args = parser.parse_args() + args = parser.parse_args() # Namespace(N=200, decoding_style='sampling', end_index=2, max_len=2048, mbpp_path='data/mbpp.test.jsonl', model='/workspace/asr/WizardLM/WizardCoder/models--WizardLM--WizardCoder-15B-V1.0/snapshots/926ca1b215c4631bc5f8c3e47173381452c23e5c', num_seqs_per_iter=2, output_path='preds/MBPP_T0.2_N200', overwrite=False, start_index=0, temperature=0.2) argsdict = vars(args) print(pprint.pformat(argsdict)) @@ -122,70 +122,70 @@ def main(): for test_example in problems[task_id]['test_list']: prompt += f"\n{test_example}" prompts.append(prompt) - + # prompts = ['\nWrite a python function to remove first and last occurrence of a given character from the string.\nTest examples:\nassert remove_Occ("hello","l") == "heo"\nassert remove_Occ("abcda","a") == "bcd"\nassert remove_Occ("PHP","P") == "H"', '\nWrite a function to sort a given matrix in ascending order according to the sum of its rows.\nTest examples:\nassert sort_matrix([[1, 2, 3], [2, 4, 5], [1, 1, 1]])==[[1, 1, 1], [1, 2, 3], [2, 4, 5]]\nassert sort_matrix([[1, 2, 3], [-2, 4, -5], [1, -1, 1]])==[[-2, 4, -5], [1, -1, 1], [1, 2, 3]]\nassert sort_matrix([[5,8,9],[6,4,3],[2,1,4]])==[[2, 1, 4], [6, 4, 3], [5, 8, 9]]'], for debugging only two examples, NOTE num_samples = len(prompts) print("Number of samples: {}".format(num_samples)) tokenizer, model = get_model(base_model=args.model) generation_config = GenerationConfig( - pad_token_id=tokenizer.pad_token_id, + pad_token_id=tokenizer.pad_token_id, # 49152 do_sample=True, - temperature=args.temperature, - max_length=args.max_len, - num_return_sequences=args.num_seqs_per_iter, - eos_token_id=tokenizer.eos_token_id, + temperature=args.temperature, # 0.2 + max_length=args.max_len, # 2048 + num_return_sequences=args.num_seqs_per_iter, # 2 + eos_token_id=tokenizer.eos_token_id, # 0 top_p=0.95 ) print(f"Loaded {args.model}.") for i in tqdm(range(num_samples), ncols=0, total=num_samples): output_file = args.output_path + '/{}.jsonl'.format(args.start_index + i) - + # output_file = 'preds/MBPP_T0.2_N200/0.jsonl' if os.path.exists(output_file) and not args.overwrite: print(f'Skip {output_file} as it already exists') continue - prompt = prompts[i].replace(' ', '\t') + prompt = prompts[i].replace(' ', '\t') # '\nWrite a python function to remove first and last occurrence of a given character from the string.\nTest examples:\nassert remove_Occ("hello","l") == "heo"\nassert remove_Occ("abcda","a") == "bcd"\nassert remove_Occ("PHP","P") == "H"' prompt_batch = [generate_prompt(prompt)] ids_batch = [task_ids[i]] completion_seqs = [] - - encoding = tokenizer(prompt_batch, return_tensors="pt", truncation=True, max_length=args.max_len).to(device) - - if args.decoding_style == 'sampling': - loops = int(args.N / args.num_seqs_per_iter) + # encoding['input_ids'].shape=[1, 105]; encoding['attention_mask'].shape=[1, 105] + encoding = tokenizer(prompt_batch, return_tensors="pt", + truncation=True, max_length=args.max_len).to(device) + if args.decoding_style == 'sampling': # NOTE, yes, in + loops = int(args.N / args.num_seqs_per_iter) # 100=loops else: loops = 1 for _ in tqdm(range(loops), total=loops, leave=False, ncols=0): - import ipdb; ipdb.set_trace() + #import ipdb; ipdb.set_trace() with torch.no_grad(): if args.decoding_style == 'sampling': gen_tokens = model.generate( - **encoding, - generation_config=generation_config - ) - + **encoding, # 使用tokenizer准备好的，包括了'input_ids'和'attention_mask' + generation_config=generation_config # 生成算法的配置信息 + ) # NOTE important, 在dgx1机器上，这个花了很长时间5minutes??? TODO + # gen_tokens.shape=[2, 404] tensor, 其中的2，是由num_return_sequences来控制的. if gen_tokens is not None: - gen_seqs = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True) + gen_seqs = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True) # '''['Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a Python script for this problem:\n\nWrite a python function to remove first and last occurrence of a given character from the string.\nTest examples:\nassert remove_Occ("hello","l") == "heo"\nassert remove_Occ("abcda","a") == "bcd"\nassert remove_Occ("PHP","P") == "H"\n\n### Response:Here\'s the Python function to remove first and last occurrence of a given character from the string:\r\n\r\n```python\r\ndef remove_Occ(string, char):\r\n if char not in string:\r\n return string\r\n else:\r\n if string.count(char) == 1:\r\n return string.replace(char, "")\r\n else:\r\n return string[:string.index(char)] + string[string.index(char)+1:string.rindex(char)] + string[string.rindex(char)+1:]\r\n```\r\n\r\nHere\'s how the function works:\r\n\r\n1. First, we check if the given character is present in the string or not. If it\'s not present, we simply return the original string.\r\n2. If the character is present in the string, we check if it appears only once or multiple times.\r\n3. If it appears only once, we simply remove it using the `replace()` method.\r\n4. If it appears multiple times, we remove the first and last occurrence of the character using string slicing.\r\n\r\nLet\'s test the function with the given test examples:\r\n\r\n```python\r\nassert remove_Occ("hello","l") == "heo"\r\nassert remove_Occ("abcda","a") == "bcd"\r\nassert remove_Occ("PHP","P") == "H"\r\n```\r\n\r\nAll the test cases pass.', 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a Python script for this problem:\n\nWrite a python function to remove first and last occurrence of a given character from the string.\nTest examples:\nassert remove_Occ("hello","l") == "heo"\nassert remove_Occ("abcda","a") == "bcd"\nassert remove_Occ("PHP","P") == "H"\n\n### Response:Here\'s the Python function to remove first and last occurrence of a given character from the string:\r\n\r\n```python\r\ndef remove_Occ(string, char):\r\n if char in string:\r\n if string.count(char) == 1:\r\n return string.replace(char, "")\r\n else:\r\n return string.replace(char, "", 1)[:-1]\r\n else:\r\n return string\r\n```\r\n\r\nThe function takes two arguments: `string` and `char`. It first checks if the given character is present in the string using the `in` operator. If it is present, it checks if the character appears only once in the string using the `count` method. If it appears only once, it removes the character using the `replace` method and returns the modified string. If the character appears more than once, it removes the first occurrence using the `replace` method with the `count` argument set to 1, and then removes the last character using slicing.\r\n\r\nIf the given character is not present in the string, the function simply returns the original string.\r\n\r\nHere are some test examples:\r\n\r\n```python\r\nassert remove_Occ("hello","l") == "heo"\r\nassert remove_Occ("abcda","a") == "bcd"\r\nassert remove_Occ("PHP","P") == "H"\r\n```\r\n\r\nAll the test cases pass.']''' else: gen_seqs = None if gen_seqs is not None: assert len(ids_batch) == 1 task_id = ids_batch[0] - + # NOTE 下面是把所有的2个候选输出，都放入'completion_seqs'中！ for seq_idx, gen_seq in enumerate(gen_seqs): - completion_seq = gen_seq.split("### Response:")[-1] + completion_seq = gen_seq.split("### Response:")[-1] # 提取结果：'Here\'s the Python function to remove first and last occurrence of a given character from the string:\r\n\r\n```python\r\ndef remove_Occ(string, char):\r\n if char not in string:\r\n return string\r\n else:\r\n if string.count(char) == 1:\r\n return string.replace(char, "")\r\n else:\r\n return string[:string.index(char)] + string[string.index(char)+1:string.rindex(char)] + string[string.rindex(char)+1:]\r\n```\r\n\r\nHere\'s how the function works:\r\n\r\n1. First, we check if the given character is present in the string or not. If it\'s not present, we simply return the original string.\r\n2. If the character is present in the string, we check if it appears only once or multiple times.\r\n3. If it appears only once, we simply remove it using the `replace()` method.\r\n4. If it appears multiple times, we remove the first and last occurrence of the character using string slicing.\r\n\r\nLet\'s test the function with the given test examples:\r\n\r\n```python\r\nassert remove_Occ("hello","l") == "heo"\r\nassert remove_Occ("abcda","a") == "bcd"\r\nassert remove_Occ("PHP","P") == "H"\r\n```\r\n\r\nAll the test cases pass.' completion_seq = completion_seq.replace('\t', ' ') all_code = gen_seq.replace('\t', ' ') completion_seqs.append( - {'task_id': task_id, - 'completion': completion_seq, - 'all_code': all_code, + {'task_id': task_id, # 11 + 'completion': completion_seq, # 'Here\'s the Python function to remove first and last occurrence of a given character from the string:\r\n\r\n```python\r\ndef remove_Occ(string, char):\r\n if char not in string:\r\n return string\r\n else:\r\n if string.count(char) == 1:\r\n return string.replace(char, "")\r\n else:\r\n return string[:string.index(char)] + string[string.index(char)+1:string.rindex(char)] + string[string.rindex(char)+1:]\r\n```\r\n\r\nHere\'s how the function works:\r\n\r\n1. First, we check if the given character is present in the string or not. If it\'s not present, we simply return the original string.\r\n2. If the character is present in the string, we check if it appears only once or multiple times.\r\n3. If it appears only once, we simply remove it using the `replace()` method.\r\n4. If it appears multiple times, we remove the first and last occurrence of the character using string slicing.\r\n\r\nLet\'s test the function with the given test examples:\r\n\r\n```python\r\nassert remove_Occ("hello","l") == "heo"\r\nassert remove_Occ("abcda","a") == "bcd"\r\nassert remove_Occ("PHP","P") == "H"\r\n```\r\n\r\nAll the test cases pass.' + 'all_code': all_code, # 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a Python script for this problem:\n\nWrite a python function to remove first and last occurrence of a given character from the string.\nTest examples:\nassert remove_Occ("hello","l") == "heo"\nassert remove_Occ("abcda","a") == "bcd"\nassert remove_Occ("PHP","P") == "H"\n\n### Response:Here\'s the Python function to remove first and last occurrence of a given character from the string:\r\n\r\n```python\r\ndef remove_Occ(string, char):\r\n if char not in string:\r\n return string\r\n else:\r\n if string.count(char) == 1:\r\n return string.replace(char, "")\r\n else:\r\n return string[:string.index(char)] + string[string.index(char)+1:string.rindex(char)] + string[string.rindex(char)+1:]\r\n```\r\n\r\nHere\'s how the function works:\r\n\r\n1. First, we check if the given character is present in the string or not. If it\'s not present, we simply return the original string.\r\n2. If the character is present in the string, we check if it appears only once or multiple times.\r\n3. If it appears only once, we simply remove it using the `replace()` method.\r\n4. If it appears multiple times, we remove the first and last occurrence of the character using string slicing.\r\n\r\nLet\'s test the function with the given test examples:\r\n\r\n```python\r\nassert remove_Occ("hello","l") == "heo"\r\nassert remove_Occ("abcda","a") == "bcd"\r\nassert remove_Occ("PHP","P") == "H"\r\n```\r\n\r\nAll the test cases pass.' } )