



'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a   :			int					  =    logging.get_logger(__name__)

a   :			str					  =    {
    """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
    """distilbert-base-uncased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
    """distilbert-base-cased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
    """distilbert-base-multilingual-cased""": (
        """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
    ),
    """distilbert-base-uncased-finetuned-sst-2-english""": (
        """https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
    ),
}

class 			UpperCamelCase_ (							__lowerCamelCase			):
	lowercase          = """distilbert"""
	lowercase          = {
	    """hidden_size""": """dim""",
	    """num_attention_heads""": """n_heads""",
	    """num_hidden_layers""": """n_layers""",
	}





	def __init__(  self		,		A=30522		,		A=512		,		A=False		,		A=6		,		A=12		,		A=768		,		A=4 * 768		,		A=0.1		,		A=0.1		,		A="gelu"		,		A=0.0_2		,		A=0.1		,		A=0.2		,		A=0		,		**A		,		)			->			Optional[int]:
								UpperCAmelCase     :		List[str]							     =		vocab_size
								UpperCAmelCase     :		Tuple							     =		max_position_embeddings
								UpperCAmelCase     :		Optional[int]							     =		sinusoidal_pos_embds
								UpperCAmelCase     :		str							     =		n_layers
								UpperCAmelCase     :		List[Any]							     =		n_heads
								UpperCAmelCase     :		Tuple							     =		dim
								UpperCAmelCase     :		str							     =		hidden_dim
								UpperCAmelCase     :		int							     =		dropout
								UpperCAmelCase     :		Dict							     =		attention_dropout
								UpperCAmelCase     :		Tuple							     =		activation
								UpperCAmelCase     :		int							     =		initializer_range
								UpperCAmelCase     :		Optional[Any]							     =		qa_dropout
								UpperCAmelCase     :		Union[str, Any]							     =		seq_classif_dropout
								super().__init__(**__lowercase		,		pad_token_id=__lowercase    )




class 			UpperCamelCase_ (							__lowerCamelCase			):





	@property
	def  _lowercase(  self    )			->			Union[str, Any]:
								if self.task == "multiple-choice":
															UpperCAmelCase     :		str							     =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
															UpperCAmelCase     :		Optional[int]							     =		{0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ]    )






import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


A__								=						['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']


class       a							(			__lowerCamelCase				):



		def __init__(     self     :str   ,__lowercase     :Dict   ,__lowercase     :str   ,__lowercase     :Any=None   ,__lowercase     :Union[str, Any]=1  ):
						snake_case__	:      Optional[int]     		=	tokenizer
						snake_case__	:      List[Any]     		=	dataset
						snake_case__	:      Any     		=	len(__lowercase  ) if n_tasks is None else n_tasks
						snake_case__	:      List[Any]     		=	n_copies






		def __iter__(     self     :Tuple  ):
						snake_case__	:      Union[str, Any]     		=	[]
						for task in range(self.n_tasks  ):
										# without strip, the model generate commented codes ...
										prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip()  )
						snake_case__	:      Optional[int]     		=	self.tokenizer(__lowercase   ,padding=__lowercase   ,return_tensors='''pt'''  )
						for task in range(self.n_tasks  ):
										for _ in range(self.n_copies  ):
														yield {
														    "ids": outputs.input_ids[task],
														    "task_id": task,
														    "input_len": outputs.attention_mask[task].sum(),
														}


class       a							(			__lowerCamelCase				):



		def __init__(     self     :Union[str, Any]   ,__lowercase     :List[Any]   ,__lowercase     :int   ,__lowercase     :List[str]  ):
						snake_case__	:      List[str]     		=	start_length
						snake_case__	:      Any     		=	eof_strings
						snake_case__	:      Optional[Any]     		=	tokenizer






		def __call__(     self     :str   ,__lowercase     :str   ,__lowercase     :Any   ,**__lowercase     :Optional[int]  ):
						snake_case__	:      Tuple     		=	self.tokenizer.batch_decode(input_ids[:, self.start_length :]  )
						snake_case__	:      Union[str, Any]     		=	[]
						for decoded_generation in decoded_generations:
										done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings  )  )
						return all(__lowercase  )



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      List[Any]     		=	re.split('''(%s)''' % '''|'''.join(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
				# last string should be ""
				return "".join(string_list[:-2]      )



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase=20  ,  **__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	defaultdict(__lowerCAmelCase      )  # dict of list of generated tokens
				for step, batch in tqdm(enumerate(__lowerCAmelCase      )      ):
								with torch.no_grad():
												snake_case__	:      List[Any]     		=	batch['''ids'''].shape[-1]
												snake_case__	:      str     		=	accelerator.unwrap_model(__lowerCAmelCase      ).generate(
												    input_ids=batch['''ids'''][:, : batch['''input_len''']]  ,  num_return_sequences=__lowerCAmelCase  ,  **__lowerCAmelCase      )
												# each task is generated batch_size times
												snake_case__	:      str     		=	batch['''task_id'''].repeat(__lowerCAmelCase      )
												snake_case__	:      Optional[int]     		=	accelerator.pad_across_processes(
												    __lowerCAmelCase  ,  dim=1  ,  pad_index=tokenizer.pad_token_id      )

												snake_case__		, snake_case__	:      Any     		=	accelerator.gather((generated_tokens, generated_tasks)      )
												snake_case__	:      Union[str, Any]     		=	generated_tokens.cpu().numpy()
												snake_case__	:      Optional[Any]     		=	generated_tasks.cpu().numpy()

												for task, generated_tokens in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
																gen_token_dict[task].append(__lowerCAmelCase      )

				snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
				for task, generated_tokens in gen_token_dict.items():
								for s in generated_tokens:
												snake_case__	:      int     		=	tokenizer.decode(__lowerCAmelCase  ,  skip_special_tokens=__lowerCAmelCase  ,  clean_up_tokenization_spaces=__lowerCAmelCase      )
												code_gens[task].append(remove_last_block(__lowerCAmelCase      )      )
				return code_gens



def 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	HfArgumentParser(__lowerCAmelCase      )
				snake_case__	:      Optional[Any]     		=	parser.parse_args()

				transformers.logging.set_verbosity_error()
				# enables code execution in code_eval metric
				snake_case__	:      Union[str, Any]     		=	args.HF_ALLOW_CODE_EVAL
				# make sure tokenizer plays nice with multiprocessing
				snake_case__	:      Union[str, Any]     		=	'''false'''

				if args.num_workers is None:
								snake_case__	:      Union[str, Any]     		=	multiprocessing.cpu_count()

				# Use dataset load to feed to accelerate
				snake_case__	:      Dict     		=	Accelerator()
				set_seed(args.seed  ,  device_specific=__lowerCAmelCase      )

				# Load model and tokenizer
				snake_case__	:      str     		=	AutoTokenizer.from_pretrained(args.model_ckpt      )
				snake_case__	:      List[str]     		=	tokenizer.eos_token
				snake_case__	:      Optional[Any]     		=	AutoModelForCausalLM.from_pretrained(args.model_ckpt      )

				# Generation settings
				snake_case__	:      List[Any]     		=	{
				    '''do_sample''': args.do_sample,
				    '''temperature''': args.temperature,
				    '''max_new_tokens''': args.max_new_tokens,
				    '''top_p''': args.top_p,
				    '''top_k''': args.top_k,
				    '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )]      ),
				}

				# Load evaluation dataset and metric
				snake_case__	:      List[str]     		=	load_dataset('''openai_humaneval'''      )
				snake_case__	:      str     		=	load_metric('''code_eval'''      )

				snake_case__	:      List[Any]     		=	args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']      )
				snake_case__	:      str     		=	args.n_samples // args.batch_size

				snake_case__	:      Tuple     		=	TokenizedDataset(__lowerCAmelCase  ,  human_eval['''test''']  ,  n_copies=__lowerCAmelCase  ,  n_tasks=__lowerCAmelCase      )
				# do not confuse args.batch_size, which is actually the num_return_sequences
				snake_case__	:      Tuple     		=	DataLoader(__lowerCAmelCase  ,  batch_size=1      )

				# Run a quick test to see if code evaluation is enabled
				try:
								snake_case__	:      Optional[Any]     		=	code_eval_metric.compute(references=['''''']  ,  predictions=[['''''']]      )
				except ValueError as exception:
								print(
								    '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
								    ''' flag to enable code evaluation.'''      )
								raise exception

				snake_case__		, snake_case__	:      Optional[Any]     		=	accelerator.prepare(__lowerCAmelCase  ,  __lowerCAmelCase      )

				snake_case__	:      Dict     		=	complete_code(
				    __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  n_tasks=__lowerCAmelCase  ,  batch_size=args.batch_size  ,  **__lowerCAmelCase  ,  )

				if accelerator.is_main_process:
								snake_case__	:      Optional[Any]     		=	[]

								for task in tqdm(range(__lowerCAmelCase      )      ):
												snake_case__	:      str     		=	human_eval['''test'''][task]['''test''']
												snake_case__	:      int     		=	f"""check({human_eval['test'][task]['entry_point']})"""
												references.append('''\n''' + test_func + '''\n''' + entry_point      )

								# Evaluate completions with "code_eval" metric
								snake_case__		, snake_case__	:      Any     		=	code_eval_metric.compute(
								    references=__lowerCAmelCase  ,  predictions=__lowerCAmelCase  ,  num_workers=args.num_workers      )
								print(f"""Results: {pass_at_k}"""      )

								# Save results to json file
								with open(args.output_file  ,  '''w'''      ) as fp:
												json.dump(__lowerCAmelCase  ,  __lowerCAmelCase      )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
				main()

