import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowercase							:      Any      							='''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

_lowercase							:      List[Any]      							='''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


_lowercase							:      str      							='''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''



def A__  (			lowercase:     Dict					)				->		List[str]:

       def remove_articles(lowercase:     Optional[int]					):
              A						:     List[Any]         =re.compile(r'\b(a|an|the)\b',		re.UNICODE					)
              return re.sub(lowercase,		' ',		lowercase					)

       def white_space_fix(lowercase:     Optional[Any]					):
              return " ".join(text.split()					)

       def remove_punc(lowercase:     Dict					):
              A						:     Optional[Any]         =set(string.punctuation					)
              return "".join(ch for ch in text if ch not in exclude					)

       def lower(lowercase:     Union[str, Any]					):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase					)					)					)					)



def A__  (			lowercase:     Union[str, Any],		lowercase:     List[Any]					)				->		int:
       return int(normalize_answer(lowercase					) == normalize_answer(lowercase					)					)



def A__  (			lowercase:     int,		lowercase:     List[Any]					)				->		Any:
       A						:     str         =[any(compute_exact(lowercase,		lowercase					) for ref in refs					) for pred, refs in zip(lowercase,		lowercase					)]
       return (sum(lowercase					) / len(lowercase					)) * 100



def A__  (			lowercase:     Tuple,		lowercase:     str,		lowercase:     Union[str, Any],		lowercase:     List[Any]					)				->		List[str]:
       A						:     List[Any]         =[rgram for rgrams in rgramslist for rgram in rgrams]
       A						:     Optional[Any]         =Counter(lowercase					)

       A						:     Any         =Counter(lowercase					)
       A						:     int         =Counter()
       for sgram, scount in sgramcounter.items():
              A						:     str         =scount * numref

       A						:     List[Any]         =Counter(lowercase					)
       A						:     Optional[int]         =Counter()
       for cgram, ccount in cgramcounter.items():
              A						:     Dict         =ccount * numref

       # KEEP
       A						:     int         =sgramcounter_rep & cgramcounter_rep
       A						:     str         =keepgramcounter_rep & rgramcounter
       A						:     Union[str, Any]         =sgramcounter_rep & rgramcounter

       A						:     str         =0
       A						:     Optional[int]         =0
       for keepgram in keepgramcountergood_rep:
              keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
              # Fix an alleged bug [2] in the keep score computation.
              # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
              keeptmpscorea += keepgramcountergood_rep[keepgram]
       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       #      a target exactly.
       A						:     Union[str, Any]         =1
       A						:     List[Any]         =1
       if len(lowercase					) > 0:
              A						:     Optional[Any]         =keeptmpscorea / len(lowercase					)
       if len(lowercase					) > 0:
              # Fix an alleged bug [2] in the keep score computation.
              # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
              A						:     Dict         =keeptmpscorea / sum(keepgramcounterall_rep.values()					)
       A						:     Dict         =0
       if keepscore_precision > 0 or keepscore_recall > 0:
              A						:     Union[str, Any]         =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

       # DELETION
       A						:     Optional[int]         =sgramcounter_rep - cgramcounter_rep
       A						:     Union[str, Any]         =delgramcounter_rep - rgramcounter
       A						:     Tuple         =sgramcounter_rep - rgramcounter
       A						:     int         =0
       A						:     Optional[Any]         =0
       for delgram in delgramcountergood_rep:
              deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
              deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       # a target exactly.
       A						:     List[str]         =1
       if len(lowercase					) > 0:
              A						:     List[Any]         =deltmpscorea / len(lowercase					)

       # ADDITION
       A						:     List[Any]         =set(lowercase					) - set(lowercase					)
       A						:     Optional[Any]         =set(lowercase					) & set(lowercase					)
       A						:     int         =set(lowercase					) - set(lowercase					)

       A						:     int         =0
       for addgram in addgramcountergood:
              addtmpscore += 1

       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       # a target exactly.
       A						:     List[Any]         =1
       A						:     int         =1
       if len(lowercase					) > 0:
              A						:     Optional[Any]         =addtmpscore / len(lowercase					)
       if len(lowercase					) > 0:
              A						:     List[Any]         =addtmpscore / len(lowercase					)
       A						:     int         =0
       if addscore_precision > 0 or addscore_recall > 0:
              A						:     Dict         =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)



def A__  (			lowercase:     Optional[Any],		lowercase:     str,		lowercase:     Optional[Any]					)				->		Any:
       A						:     int         =len(lowercase					)

       A						:     Dict         =ssent.split(' '					)
       A						:     str         =csent.split(' '					)
       A						:     int         =[]
       A						:     Any         =[]
       A						:     List[Any]         =[]
       A						:     Any         =[]
       A						:     List[Any]         =[]
       A						:     Optional[int]         =[]

       A						:     Tuple         =[]
       A						:     List[str]         =[]
       A						:     List[Any]         =[]
       A						:     Any         =[]
       for rsent in rsents:
              A						:     Any         =rsent.split(' '					)
              A						:     List[str]         =[]
              A						:     Dict         =[]
              A						:     Dict         =[]
              ragramslist.append(lowercase					)
              for i in range(0,		len(lowercase					) - 1					):
                     if i < len(lowercase					) - 1:
                            A						:     List[Any]         =ragrams[i] + ' ' + ragrams[i + 1]
                            ragrams.append(lowercase					)
                     if i < len(lowercase					) - 2:
                            A						:     Dict         =ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                            ragrams.append(lowercase					)
                     if i < len(lowercase					) - 3:
                            A						:     Optional[Any]         =ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                            ragrams.append(lowercase					)
              ragramslist.append(lowercase					)
              ragramslist.append(lowercase					)
              ragramslist.append(lowercase					)

       for i in range(0,		len(lowercase					) - 1					):
              if i < len(lowercase					) - 1:
                     A						:     Tuple         =sagrams[i] + ' ' + sagrams[i + 1]
                     sagrams.append(lowercase					)
              if i < len(lowercase					) - 2:
                     A						:     Optional[Any]         =sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                     sagrams.append(lowercase					)
              if i < len(lowercase					) - 3:
                     A						:     Optional[int]         =sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                     sagrams.append(lowercase					)

       for i in range(0,		len(lowercase					) - 1					):
              if i < len(lowercase					) - 1:
                     A						:     Any         =cagrams[i] + ' ' + cagrams[i + 1]
                     cagrams.append(lowercase					)
              if i < len(lowercase					) - 2:
                     A						:     Tuple         =cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
                     cagrams.append(lowercase					)
              if i < len(lowercase					) - 3:
                     A						:     Tuple         =cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
                     cagrams.append(lowercase					)

       ((A)  ,  (A)  ,  (A))						:     Any         =SARIngram(lowercase,		lowercase,		lowercase,		lowercase					)
       ((A)  ,  (A)  ,  (A))						:     List[str]         =SARIngram(lowercase,		lowercase,		lowercase,		lowercase					)
       ((A)  ,  (A)  ,  (A))						:     List[Any]         =SARIngram(lowercase,		lowercase,		lowercase,		lowercase					)
       ((A)  ,  (A)  ,  (A))						:     List[Any]         =SARIngram(lowercase,		lowercase,		lowercase,		lowercase					)
       A						:     Any         =sum([keepascore, keepascore, keepascore, keepascore]					) / 4
       A						:     Tuple         =sum([delascore, delascore, delascore, delascore]					) / 4
       A						:     Union[str, Any]         =sum([addascore, addascore, addascore, addascore]					) / 4
       A						:     List[str]         =(avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore



def A__  (			lowercase:     Any,		lowercase:     bool = True,		lowercase:     str = "13a",		lowercase:     bool = True					)				->		str:
       # Normalization is requried for the ASSET dataset (one of the primary
       # datasets in sentence simplification) to allow using space
       # to split the sentence. Even though Wiki-Auto and TURK datasets,
       # do not require normalization, we do it for consistency.
       # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
       # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

       if lowercase:
              A						:     Union[str, Any]         =sentence.lower()

       if tokenizer in ["13a", "intl"]:
              if version.parse(sacrebleu.__version__					).major >= 2:
                     A						:     Union[str, Any]         =sacrebleu.metrics.bleu._get_tokenizer(lowercase					)()(lowercase					)
              else:
                     A						:     Optional[int]         =sacrebleu.TOKENIZERS[tokenizer]()(lowercase					)
       elif tokenizer == "moses":
              A						:     Union[str, Any]         =sacremoses.MosesTokenizer().tokenize(lowercase,		return_str=lowercase,		escape=lowercase					)
       elif tokenizer == "penn":
              A						:     Optional[Any]         =sacremoses.MosesTokenizer().penn_tokenize(lowercase,		return_str=lowercase					)
       else:
              A						:     Dict         =sentence

       if not return_str:
              A						:     List[Any]         =normalized_sent.split()

       return normalized_sent



def A__  (			lowercase:     Union[str, Any],		lowercase:     int,		lowercase:     List[str]					)				->		int:
       if not (len(lowercase					) == len(lowercase					) == len(lowercase					)):
              raise ValueError('Sources length must match predictions and references lengths.'					)
       A						:     int         =0
       for src, pred, refs in zip(lowercase,		lowercase,		lowercase					):
              sari_score += SARIsent(normalize(lowercase					),		normalize(lowercase					),		[normalize(lowercase					) for sent in refs]					)
       A						:     int         =sari_score / len(lowercase					)
       return 100 * sari_score



def A__  (			lowercase:     Tuple,		lowercase:     Any,		lowercase:     List[Any]="exp",		lowercase:     Tuple=None,		lowercase:     Any=False,		lowercase:     Tuple=False,		lowercase:     List[str]=False,		)				->		str:
       A						:     str         =len(references[0]					)
       if any(len(lowercase					) != references_per_prediction for refs in references					):
              raise ValueError('Sacrebleu requires the same number of references for each prediction'					)
       A						:     Tuple         =[[refs[i] for refs in references] for i in range(lowercase					)]
       A						:     Tuple         =sacrebleu.corpus_bleu(
           lowercase,		lowercase,		smooth_method=lowercase,		smooth_value=lowercase,		force=lowercase,		lowercase=lowercase,		use_effective_order=lowercase,		)
       return output.score



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION			)
class  SCREAMING_SNAKE_CASE_ (					datasets.Metric			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[Any]:
         return datasets.MetricInfo(
             description=_DESCRIPTION      ,	citation=_CITATION      ,	inputs_description=_KWARGS_DESCRIPTION      ,	features=datasets.Features(
                 {
                     'predictions': datasets.Value('string'      ,	id='sequence'     ),
                     'references': datasets.Sequence(datasets.Value('string'      ,	id='sequence'     )      ,	id='references'     ),
                 }     )      ,	codebase_urls=[
                 'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
                 'https://github.com/cocoxu/simplification/blob/master/SARI.py',
                 'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
                 'https://github.com/mjpost/sacreBLEU',
             ]      ,	reference_urls=[
                 'https://www.aclweb.org/anthology/Q16-1029.pdf',
                 'https://github.com/mjpost/sacreBLEU',
                 'https://en.wikipedia.org/wiki/BLEU',
                 'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
             ]      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Any:
         A						:     int         ={}
         result.update({'sari': compute_sari(sources=SCREAMING_SNAKE_CASE__      ,	predictions=SCREAMING_SNAKE_CASE__      ,	references=SCREAMING_SNAKE_CASE__     )}     )
         result.update({'sacrebleu': compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE__      ,	references=SCREAMING_SNAKE_CASE__     )}     )
         result.update({'exact': compute_em(predictions=SCREAMING_SNAKE_CASE__      ,	references=SCREAMING_SNAKE_CASE__     )}     )
         return result







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 A__  (			lowercase:     int,		lowercase:     str					)				->		Dict:
       assert isinstance(lowercase,		lowercase					)
       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 A__  (			lowercase:     Dict,		lowercase:     Tuple,		lowercase:     str					)				->		str:
       A						:     Any         =tmp_path / 'cache'
       A						:     Dict         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A						:     Dict         =JsonDatasetReader(lowercase,		cache_dir=lowercase,		keep_in_memory=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)



@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 A__  (			lowercase:     Optional[int],		lowercase:     Any,		lowercase:     Union[str, Any]					)				->		Tuple:
       A						:     Tuple         =tmp_path / 'cache'
       A						:     Optional[Any]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     Optional[Any]         =features.copy() if features else default_expected_features
       A						:     Union[str, Any]         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     str         =JsonDatasetReader(lowercase,		features=lowercase,		cache_dir=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)



@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ],		)
def A__  (			lowercase:     Optional[int],		lowercase:     str,		lowercase:     Dict					)				->		Optional[int]:
       A						:     int         =tmp_path / 'cache'
       A						:     Tuple         ={'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
       A						:     int         =features.copy() if features else default_expected_features
       A						:     str         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     Optional[int]         =JsonDatasetReader(lowercase,		features=lowercase,		cache_dir=lowercase					).read()
       assert isinstance(lowercase,		lowercase					)
       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 A__  (			lowercase:     Optional[Any],		lowercase:     str					)				->		Tuple:
       # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
       A						:     str         ={'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
       A						:     Dict         =features.copy()
       A						:     List[str]         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     int         =tmp_path / 'cache'
       A						:     Optional[int]         =JsonDatasetReader(lowercase,		features=lowercase,		cache_dir=lowercase					).read()
       assert isinstance(lowercase,		lowercase					)
       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 A__  (			lowercase:     Union[str, Any],		lowercase:     Any,		lowercase:     str					)				->		Optional[Any]:
       A						:     Optional[int]         =tmp_path / 'cache'
       A						:     Optional[Any]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     str         =JsonDatasetReader(lowercase,		cache_dir=lowercase,		split=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)
       assert dataset.split == split if split else "train"



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



def A__  (			lowercase:     List[str],		lowercase:     Tuple,		lowercase:     Optional[Any]=("train",)					)				->		Tuple:
       assert isinstance(lowercase,		lowercase					)
       for split in splits:
              A						:     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 A__  (			lowercase:     Tuple,		lowercase:     Optional[int],		lowercase:     Any					)				->		str:
       A						:     List[str]         =tmp_path / 'cache'
       A						:     Union[str, 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():
              A						:     str         =JsonDatasetReader({'train': jsonl_path},		cache_dir=lowercase,		keep_in_memory=lowercase					).read()
       _check_json_datasetdict(lowercase,		lowercase					)



@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 A__  (			lowercase:     Optional[int],		lowercase:     Optional[int],		lowercase:     Optional[int]					)				->		Tuple:
       A						:     Any         =tmp_path / 'cache'
       A						:     List[str]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     str         =features.copy() if features else default_expected_features
       A						:     Dict         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     Optional[Any]         =JsonDatasetReader({'train': jsonl_path},		features=lowercase,		cache_dir=lowercase					).read()
       _check_json_datasetdict(lowercase,		lowercase					)



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



def A__  (			lowercase:     List[Any]					)				->		Tuple:
       return json.load(lowercase					)



def A__  (			lowercase:     List[Any]					)				->		Tuple:
       return [json.loads(lowercase					) 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 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Any:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__     ).write()
                buffer.seek(0     )
                A						:     int         =load_json_function(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         assert isinstance(exported_content[0]      ,	SCREAMING_SNAKE_CASE__     )
         assert len(SCREAMING_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 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Optional[Any]:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__      ,	orient=SCREAMING_SNAKE_CASE__     ).write()
                buffer.seek(0     )
                A						:     Any         =load_json(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(SCREAMING_SNAKE_CASE__      ,	'keys'     ) and not hasattr(exported_content[0]      ,	'keys'     )
         if len_at:
                assert len(exported_content[len_at]     ) == 10
         else:
                assert len(SCREAMING_SNAKE_CASE__     ) == 10







  @pytest.mark.parametrize('lines, load_json_function'      ,	[(True, load_json_lines), (False, load_json)]     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Optional[int]:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__      ,	num_proc=2     ).write()
                buffer.seek(0     )
                A						:     int         =load_json_function(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         assert isinstance(exported_content[0]      ,	SCREAMING_SNAKE_CASE__     )
         assert len(SCREAMING_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 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[Any]:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__      ,	orient=SCREAMING_SNAKE_CASE__      ,	num_proc=2     ).write()
                buffer.seek(0     )
                A						:     List[Any]         =load_json(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(SCREAMING_SNAKE_CASE__      ,	'keys'     ) and not hasattr(exported_content[0]      ,	'keys'     )
         if len_at:
                assert len(exported_content[len_at]     ) == 10
         else:
                assert len(SCREAMING_SNAKE_CASE__     ) == 10







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







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

         with fsspec.open(SCREAMING_SNAKE_CASE__      ,	'rb'      ,	compression='infer'     ) as f:
                A						:     str         =f.read()
         with fsspec.open(SCREAMING_SNAKE_CASE__      ,	'rb'      ,	compression='infer'     ) as f:
                A						:     List[str]         =f.read()
         assert exported_content == original_content






