


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


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







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=1       )     ->   Dict:
      _lowercase							      =tokenizer
      _lowercase							      =dataset
      _lowercase							      =len(UpperCAmelCase       ) if n_tasks is None else n_tasks
      _lowercase							      =n_copies





     def __iter__(self       )     ->   Optional[Any]:
      _lowercase							      =[]
      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()       )
      _lowercase							      =self.tokenizer(UpperCAmelCase		,     padding=UpperCAmelCase		,     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       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =start_length
      _lowercase							      =eof_strings
      _lowercase							      =tokenizer





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Dict:
      _lowercase							      =self.tokenizer.batch_decode(input_ids[:, self.start_length :]       )
      _lowercase							      =[]
      for decoded_generation in decoded_generations:
       done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings       )       )
      return all(UpperCAmelCase       )



def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =re.split('''(%s)''' % '''|'''.join(__snake_case						)     ,						__snake_case						)
 # last string should be ""
 return "".join(string_list[:-2]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=20     ,						**__snake_case						)     ->			Tuple:







 """simple docstring"""


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

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

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

 _lowercase							      =[[] for _ in range(__snake_case						)]
 for task, generated_tokens in gen_token_dict.items():
  for s in generated_tokens:
   _lowercase							      =tokenizer.decode(__snake_case     ,						skip_special_tokens=__snake_case     ,						clean_up_tokenization_spaces=__snake_case						)
   code_gens[task].append(remove_last_block(__snake_case						)						)
 return code_gens
def 				UpperCAmelCase_					(							)     ->			str:







 """simple docstring"""


 _lowercase							      =HfArgumentParser(__snake_case						)
 _lowercase							      =parser.parse_args()

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

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

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

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

 # Generation settings
 _lowercase							      ={
     '''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     ,						__snake_case     ,						__snake_case						)]						),
 }

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

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

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

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

 _lowercase      ,    _lowercase							      =accelerator.prepare(__snake_case     ,						__snake_case						)

 _lowercase							      =complete_code(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						n_tasks=__snake_case     ,						batch_size=args.batch_size     ,						**__snake_case     ,						)

 if accelerator.is_main_process:
  _lowercase							      =[]

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

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


# 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()






'''simple docstring'''






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   Optional[int]:
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''keep_in_memory'''   ,      [False, True]  )
def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[int]  )      ->   Dict:
							UpperCAmelCase :       Dict 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[Any] 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase :       str 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase   ,      keep_in_memory=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )


@pytest.mark.parametrize(
    '''features'''   ,      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]   ,      )
def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						int  )      ->   Optional[int]:
							UpperCAmelCase :       Any 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Tuple 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Any 							=		features.copy() if features else default_expected_features
							UpperCAmelCase :       List[Any] 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Dict 							=		JsonDatasetReader(_lowerCAmelCase   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )


@pytest.mark.parametrize(
    '''features'''   ,      [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]   ,      )
def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Tuple  )      ->   Tuple:
							UpperCAmelCase :       Optional[Any] 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Optional[int] 							=		{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
							UpperCAmelCase :       int 							=		features.copy() if features else default_expected_features
							UpperCAmelCase :       Any 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Tuple 							=		JsonDatasetReader(_lowerCAmelCase   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.num_rows == 2
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_3", "col_1", "col_2"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
							UpperCAmelCase :       Tuple 							=		{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
							UpperCAmelCase :       List[str] 							=		features.copy()
							UpperCAmelCase :       Union[str, Any] 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Tuple 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[str] 							=		JsonDatasetReader(_lowerCAmelCase   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.num_rows == 2
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_2", "col_3", "col_1"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''split'''   ,      [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						List[str]  )      ->   Optional[Any]:
							UpperCAmelCase :       Any 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[Any] 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       List[Any] 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase   ,      split=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.split == split if split else "train"


@pytest.mark.parametrize('''path_type'''   ,      [str, list]  )
def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Any  )      ->   Dict:
							if issubclass(_lowerCAmelCase   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       str 							=		jsonl_path
							elif issubclass(_lowerCAmelCase   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       Dict 							=		[jsonl_path]
							UpperCAmelCase :       int 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Dict 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Optional[int] 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict=("train",)  )      ->   Union[str, Any]:
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							for split in splits:
														UpperCAmelCase :       List[str] 							=		dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 3
														assert dataset.column_names == ["col_1", "col_2", "col_3"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''keep_in_memory'''   ,      [False, True]  )
def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str  )      ->   Any:
							UpperCAmelCase :       Optional[Any] 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[str] 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase :       Optional[int] 							=		JsonDatasetReader({'''train''': jsonl_path}   ,      cache_dir=_lowerCAmelCase   ,      keep_in_memory=_lowerCAmelCase  ).read()
							_check_json_datasetdict(_lowerCAmelCase   ,      _lowerCAmelCase  )


@pytest.mark.parametrize(
    '''features'''   ,      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]   ,      )
def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       Dict 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Dict 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Optional[int] 							=		features.copy() if features else default_expected_features
							UpperCAmelCase :       Union[str, Any] 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Tuple 							=		JsonDatasetReader({'''train''': jsonl_path}   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_datasetdict(_lowerCAmelCase   ,      _lowerCAmelCase  )


@pytest.mark.parametrize('''split'''   ,      [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							if split:
														UpperCAmelCase :       Optional[int] 							=		{split: jsonl_path}
							else:
														UpperCAmelCase :       Any 							=		'''train'''
														UpperCAmelCase :       Any 							=		{'''train''': jsonl_path, '''test''': jsonl_path}
							UpperCAmelCase :       Tuple 							=		tmp_path / '''cache'''
							UpperCAmelCase :       int 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Optional[Any] 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_datasetdict(_lowerCAmelCase   ,      _lowerCAmelCase   ,      splits=list(path.keys()  )  )
							assert all(dataset[split].split == split for split in path.keys()  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   str:
							return json.load(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Dict  )      ->   str:
							return [json.loads(_lowerCAmelCase  ) for line in buffer]




class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	@pytest.mark.parametrize('''lines, load_json_function'''		,			[(True, load_json_lines), (False, load_json)]		)
	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Optional[int]		)			->		Dict:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		).write()
															buffer.seek(0		)
															UpperCAmelCase :       Union[str, Any] 							=		load_json_function(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								assert isinstance(exported_content[0]		,			__snake_case		)
								assert len(__snake_case		) == 10







	@pytest.mark.parametrize(
	    '''orient, container, keys, len_at'''		,			[
	        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
	        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
	        ('''index''', dict, set('''0123456789'''		), None),
	        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
	        ('''values''', list, None, None),
	        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
	    ]		,			)
	def      A	(						self			:     Optional[int]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     str		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Optional[Any]		)			->		List[Any]:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		,			orient=__snake_case		).write()
															buffer.seek(0		)
															UpperCAmelCase :       Union[str, Any] 							=		load_json(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								if keys:
															if container is dict:
																						assert exported_content.keys() == keys
															else:
																						assert exported_content[0].keys() == keys
								else:
															assert not hasattr(__snake_case		,			'''keys'''		) and not hasattr(exported_content[0]		,			'''keys'''		)
								if len_at:
															assert len(exported_content[len_at]		) == 10
								else:
															assert len(__snake_case		) == 10







	@pytest.mark.parametrize('''lines, load_json_function'''		,			[(True, load_json_lines), (False, load_json)]		)
	def      A	(						self			:     str		,			__snake_case			:     str		,			__snake_case			:     str		,			__snake_case			:     int		)			->		Any:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		,			num_proc=2		).write()
															buffer.seek(0		)
															UpperCAmelCase :       Any 							=		load_json_function(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								assert isinstance(exported_content[0]		,			__snake_case		)
								assert len(__snake_case		) == 10







	@pytest.mark.parametrize(
	    '''orient, container, keys, len_at'''		,			[
	        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
	        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
	        ('''index''', dict, set('''0123456789'''		), None),
	        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
	        ('''values''', list, None, None),
	        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
	    ]		,			)
	def      A	(						self			:     Any		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Optional[int]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		)			->		Any:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		,			orient=__snake_case		,			num_proc=2		).write()
															buffer.seek(0		)
															UpperCAmelCase :       List[str] 							=		load_json(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								if keys:
															if container is dict:
																						assert exported_content.keys() == keys
															else:
																						assert exported_content[0].keys() == keys
								else:
															assert not hasattr(__snake_case		,			'''keys'''		) and not hasattr(exported_content[0]		,			'''keys'''		)
								if len_at:
															assert len(exported_content[len_at]		) == 10
								else:
															assert len(__snake_case		) == 10







	def      A	(						self			:     List[Any]		,			__snake_case			:     str		)			->		Dict:
								with pytest.raises(__snake_case		):
															with io.BytesIO() as buffer:
																						JsonDatasetWriter(__snake_case		,			__snake_case		,			num_proc=0		)







	@pytest.mark.parametrize('''compression, extension'''		,			[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]		)
	def      A	(						self			:     Optional[int]		,			__snake_case			:     Any		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Dict		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		tmp_path_factory.mktemp('''data'''		) / F"""test.json.{extension}"""
								UpperCAmelCase :       List[Any] 							=		str(shared_datadir / F"""test_file.json.{extension}"""		)
								JsonDatasetWriter(__snake_case		,			__snake_case		,			compression=__snake_case		).write()

								with fsspec.open(__snake_case		,			'''rb'''		,			compression='''infer'''		) as f:
															UpperCAmelCase :       str 							=		f.read()
								with fsspec.open(__snake_case		,			'''rb'''		,			compression='''infer'''		) as f:
															UpperCAmelCase :       Optional[int] 							=		f.read()
								assert exported_content == original_content





