
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     :	Union[str, Any]    = ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case=None		,     _snake_case=1				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    tokenizer
								__a										=    dataset
								__a										=    len(_snake_case				) if n_tasks is None else n_tasks
								__a										=    n_copies




				def __iter__(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    []
								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()				)
								__a										=    self.tokenizer(_snake_case		,     padding=_snake_case		,     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( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    start_length
								__a										=    eof_strings
								__a										=    tokenizer




				def __call__(     self		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.tokenizer.batch_decode(input_ids[:, self.start_length :]				)
								__a										=    []
								for decoded_generation in decoded_generations:
												done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings				)				)
								return all(_snake_case				)



def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				__a										=    re.split('''(%s)''' % '''|'''.join(a__  )						,    a__  )
				# last string should be ""
				return "".join(string_list[:-2]  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__						,    a__=20						,    **a__  )       ->  Dict:
				__a										=    defaultdict(a__  )  # dict of list of generated tokens
				for step, batch in tqdm(enumerate(a__  )  ):
								with torch.no_grad():
												__a										=    batch['''ids'''].shape[-1]
												__a										=    accelerator.unwrap_model(a__  ).generate(
												    input_ids=batch['''ids'''][:, : batch['''input_len''']]						,    num_return_sequences=a__						,    **a__  )
												# each task is generated batch_size times
												__a										=    batch['''task_id'''].repeat(a__  )
												__a										=    accelerator.pad_across_processes(
												    a__						,    dim=1						,    pad_index=tokenizer.pad_token_id  )

												__a							,     __a										=    accelerator.gather((generated_tokens, generated_tasks)  )
												__a										=    generated_tokens.cpu().numpy()
												__a										=    generated_tasks.cpu().numpy()

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

				__a										=    [[] for _ in range(a__  )]
				for task, generated_tokens in gen_token_dict.items():
								for s in generated_tokens:
												__a										=    tokenizer.decode(a__						,    skip_special_tokens=a__						,    clean_up_tokenization_spaces=a__  )
												code_gens[task].append(remove_last_block(a__  )  )
				return code_gens


def     __lowerCAmelCase			(			)       ->  Optional[int]:
				# Setup configuration
				__a										=    HfArgumentParser(a__  )
				__a										=    parser.parse_args()

				transformers.logging.set_verbosity_error()
				# enables code execution in code_eval metric
				__a										=    args.HF_ALLOW_CODE_EVAL
				# make sure tokenizer plays nice with multiprocessing
				__a										=    '''false'''

				if args.num_workers is None:
								__a										=    multiprocessing.cpu_count()

				# Use dataset load to feed to accelerate
				__a										=    Accelerator()
				set_seed(args.seed						,    device_specific=a__  )

				# Load model and tokenizer
				__a										=    AutoTokenizer.from_pretrained(args.model_ckpt  )
				__a										=    tokenizer.eos_token
				__a										=    AutoModelForCausalLM.from_pretrained(args.model_ckpt  )

				# Generation settings
				__a										=    {
				    '''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						,    a__						,    a__  )]  ),
				}

				# Load evaluation dataset and metric
				__a										=    load_dataset('''openai_humaneval'''  )
				__a										=    load_metric('''code_eval'''  )

				__a										=    args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']  )
				__a										=    args.n_samples // args.batch_size

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

				# Run a quick test to see if code evaluation is enabled
				try:
								__a										=    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

				__a							,     __a										=    accelerator.prepare(a__						,    a__  )

				__a										=    complete_code(
				    a__						,    a__						,    a__						,    a__						,    n_tasks=a__						,    batch_size=args.batch_size						,    **a__						,    )

				if accelerator.is_main_process:
								__a										=    []

								for task in tqdm(range(a__  )  ):
												__a										=    human_eval['''test'''][task]['''test''']
												__a										=    F"""check({human_eval['test'][task]['entry_point']})"""
												references.append('''\n''' + test_func + '''\n''' + entry_point  )

								# Evaluate completions with "code_eval" metric
								__a							,     __a										=    code_eval_metric.compute(
								    references=a__						,    predictions=a__						,    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(a__						,    a__  )


# 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()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['XLMRobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)