

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

a		=random.Random()






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__=1.0	, lowerCamelCase__=None	, lowerCamelCase__=None			)  ->     List[Any]:
	if rng is None:
		__lowerCamelCase				:				Union[str, Any]					       = global_rng

	__lowerCamelCase				:				Any					       = []
	for batch_idx in range(shape[0]			):
		values.append([]			)
		for _ in range(shape[1]			):
			values[-1].append(rng.random() * scale			)

	return values






class        A_		(       unittest.TestCase				):
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]=7   ,SCREAMING_SNAKE_CASE__		:			str=4_0_0   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=2_0_0_0   ,SCREAMING_SNAKE_CASE__		:			Tuple=2_0_4_8   ,SCREAMING_SNAKE_CASE__		:			str=1_2_8   ,SCREAMING_SNAKE_CASE__		:			Dict=1   ,SCREAMING_SNAKE_CASE__		:			Any=5_1_2   ,SCREAMING_SNAKE_CASE__		:			List[Any]=3_0   ,SCREAMING_SNAKE_CASE__		:			List[Any]=4_4_1_0_0   ,):
								__lowerCamelCase				:				List[Any]					       = parent
								__lowerCamelCase				:				Optional[Any]					       = batch_size
								__lowerCamelCase				:				List[Any]					       = min_seq_length
								__lowerCamelCase				:				int					       = max_seq_length
								__lowerCamelCase				:				List[str]					       = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__lowerCamelCase				:				List[str]					       = spectrogram_length
								__lowerCamelCase				:				List[str]					       = feature_size
								__lowerCamelCase				:				Any					       = num_audio_channels
								__lowerCamelCase				:				Any					       = hop_length
								__lowerCamelCase				:				str					       = chunk_length
								__lowerCamelCase				:				List[Any]					       = sampling_rate
							def     lowerCAmelCase     (		self		:			int):
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}


							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			str=False   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=False):



								def _flatten(SCREAMING_SNAKE_CASE__		:			Optional[Any]):
									return list(itertools.chain(*UpperCamelCase__))

								if equal_length:
									__lowerCamelCase				:				List[str]					       = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
								else:
									# make sure that inputs increase in size
									__lowerCamelCase				:				Optional[int]					       = [
									    floats_list((x, self.feature_size))
									    for x in range(self.min_seq_length   ,self.max_seq_length   ,self.seq_length_diff)
									]
								if numpify:
									__lowerCamelCase				:				Optional[int]					       = [np.asarray(UpperCamelCase__) for x in speech_inputs]
								return speech_inputs






@require_torch
@require_torchaudio
class        A_		(       __UpperCAmelCase			,							unittest.TestCase				):
							_UpperCAmelCase    :  List[str]						      =      TvltFeatureExtractor
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				List[Any]					       = TvltFeatureExtractionTester(self)
							def     lowerCAmelCase     (		self		:			Dict):
								__lowerCamelCase				:				Optional[int]					       = self.feature_extraction_class(**self.feat_extract_dict)
								self.assertTrue(hasattr(UpperCamelCase__   ,'spectrogram_length'))
								self.assertTrue(hasattr(UpperCamelCase__   ,'feature_size'))
								self.assertTrue(hasattr(UpperCamelCase__   ,'num_audio_channels'))
								self.assertTrue(hasattr(UpperCamelCase__   ,'hop_length'))
								self.assertTrue(hasattr(UpperCamelCase__   ,'chunk_length'))
								self.assertTrue(hasattr(UpperCamelCase__   ,'sampling_rate'))
							def     lowerCAmelCase     (		self		:			List[Any]):
								__lowerCamelCase				:				List[Any]					       = self.feature_extraction_class(**self.feat_extract_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									__lowerCamelCase				:				int					       = feat_extract_first.save_pretrained(UpperCamelCase__)[0]
									check_json_file_has_correct_format(UpperCamelCase__)
									__lowerCamelCase				:				Union[str, Any]					       = self.feature_extraction_class.from_pretrained(UpperCamelCase__)

								__lowerCamelCase				:				Optional[Any]					       = feat_extract_first.to_dict()
								__lowerCamelCase				:				Optional[Any]					       = feat_extract_second.to_dict()
								__lowerCamelCase				:				Tuple					       = dict_first.pop('mel_filters')
								__lowerCamelCase				:				List[str]					       = dict_second.pop('mel_filters')
								self.assertTrue(np.allclose(UpperCamelCase__   ,UpperCamelCase__))
								self.assertEqual(UpperCamelCase__   ,UpperCamelCase__)
							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				Optional[Any]					       = self.feature_extraction_class(**self.feat_extract_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									__lowerCamelCase				:				str					       = os.path.join(UpperCamelCase__   ,'feat_extract.json')
									feat_extract_first.to_json_file(UpperCamelCase__)
									__lowerCamelCase				:				Optional[Any]					       = self.feature_extraction_class.from_json_file(UpperCamelCase__)

								__lowerCamelCase				:				int					       = feat_extract_first.to_dict()
								__lowerCamelCase				:				Any					       = feat_extract_second.to_dict()
								__lowerCamelCase				:				str					       = dict_first.pop('mel_filters')
								__lowerCamelCase				:				int					       = dict_second.pop('mel_filters')
								self.assertTrue(np.allclose(UpperCamelCase__   ,UpperCamelCase__))
								self.assertEqual(UpperCamelCase__   ,UpperCamelCase__)
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Union[str, Any]					       = self.feature_extraction_class(**self.feat_extract_dict)

								# create three inputs of length 800, 1000, and 1200
								__lowerCamelCase				:				Dict					       = [floats_list((1, x))[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0)]
								__lowerCamelCase				:				Union[str, Any]					       = [np.asarray(UpperCamelCase__) for speech_input in speech_inputs]

								# Test not batched input
								__lowerCamelCase				:				int					       = feature_extractor(np_speech_inputs[0]   ,return_tensors='np'   ,sampling_rate=4_4_1_0_0).audio_values

								self.assertTrue(encoded_audios.ndim == 4)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

								# Test batched
								__lowerCamelCase				:				List[Any]					       = feature_extractor(UpperCamelCase__   ,return_tensors='np'   ,sampling_rate=4_4_1_0_0).audio_values

								self.assertTrue(encoded_audios.ndim == 4)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

								# Test audio masking
								__lowerCamelCase				:				List[Any]					       = feature_extractor(
								    UpperCamelCase__   ,return_tensors='np'   ,sampling_rate=4_4_1_0_0   ,mask_audio=UpperCamelCase__).audio_values

								self.assertTrue(encoded_audios.ndim == 4)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

								# Test 2-D numpy arrays are batched.
								__lowerCamelCase				:				Optional[int]					       = [floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__lowerCamelCase				:				int					       = np.asarray(UpperCamelCase__)
								__lowerCamelCase				:				Any					       = feature_extractor(UpperCamelCase__   ,return_tensors='np'   ,sampling_rate=4_4_1_0_0).audio_values
								self.assertTrue(encoded_audios.ndim == 4)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								__lowerCamelCase				:				Optional[int]					       = load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,'clean'   ,split='validation')
								# automatic decoding with librispeech
								__lowerCamelCase				:				List[str]					       = ds.sort('id').select(range(UpperCamelCase__))[:num_samples]['audio']

								return [x["array"] for x in speech_samples]


							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				Dict					       = self._load_datasamples(1)
								__lowerCamelCase				:				Dict					       = TvltFeatureExtractor()
								__lowerCamelCase				:				str					       = feature_extractor(UpperCamelCase__   ,return_tensors='pt').audio_values

								self.assertEquals(audio_values.shape   ,(1, 1, 1_9_2, 1_2_8))

								__lowerCamelCase				:				List[Any]					       = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]])
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,UpperCamelCase__   ,atol=1E-4))










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 __UpperCamelCase			(       _A ,  _A  ):
						assert isinstance(_A ,  _A  )
						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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A ,  keep_in_memory=_A  ).read()
						_check_json_dataset(_A ,  _A  )





@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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						_check_json_dataset(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						assert isinstance(_A ,  _A  )
						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 __UpperCamelCase			(       _A ,  _A  ):
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						lowerCAmelCase_      			=  {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
						lowerCAmelCase_      			=  features.copy()
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						assert isinstance(_A ,  _A  )
						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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A ,  split=_A  ).read()
						_check_json_dataset(_A ,  _A  )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type''' ,  [str, list]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						if issubclass(_A ,  _A  ):
												lowerCAmelCase_      			=  jsonl_path
						elif issubclass(_A ,  _A  ):
												lowerCAmelCase_      			=  [jsonl_path]
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A  ).read()
						_check_json_dataset(_A ,  _A  )





def __UpperCamelCase			(       _A ,  _A ,  _A=("train",)  ):
						assert isinstance(_A ,  _A  )
						for split in splits:
												lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowerCAmelCase_      			=  JsonDatasetReader({'''train''': jsonl_path} ,  cache_dir=_A ,  keep_in_memory=_A  ).read()
						_check_json_datasetdict(_A ,  _A  )





@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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader({'''train''': jsonl_path} ,  features=_A ,  cache_dir=_A  ).read()
						_check_json_datasetdict(_A ,  _A  )





@pytest.mark.parametrize('''split''' ,  [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						if split:
												lowerCAmelCase_      			=  {split: jsonl_path}
						else:
												lowerCAmelCase_      			=  '''train'''
												lowerCAmelCase_      			=  {'''train''': jsonl_path, '''test''': jsonl_path}
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A  ).read()
						_check_json_datasetdict(_A ,  _A ,  splits=list(path.keys()  )  )
						assert all(dataset[split].split == split for split in path.keys()  )





def __UpperCamelCase			(       _A  ):
						return json.load(_A  )





def __UpperCamelCase			(       _A  ):
						return [json.loads(_A  ) for line in buffer]






class  A		:



		@pytest.mark.parametrize('''lines, load_json_function''',	[(True, load_json_lines), (False, load_json)]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json_function(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								assert isinstance(exported_content[0],	UpperCamelCase__   )
								assert len(UpperCamelCase__   ) == 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,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	orient=UpperCamelCase__   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(UpperCamelCase__,	'''keys'''   ) and not hasattr(exported_content[0],	'''keys'''   )
								if len_at:
														assert len(exported_content[len_at]   ) == 10
								else:
														assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize('''lines, load_json_function''',	[(True, load_json_lines), (False, load_json)]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	num_proc=2   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json_function(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								assert isinstance(exported_content[0],	UpperCamelCase__   )
								assert len(UpperCamelCase__   ) == 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,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	orient=UpperCamelCase__,	num_proc=2   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(UpperCamelCase__,	'''keys'''   ) and not hasattr(exported_content[0],	'''keys'''   )
								if len_at:
														assert len(exported_content[len_at]   ) == 10
								else:
														assert len(UpperCamelCase__   ) == 10



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								with pytest.raises(UpperCamelCase__   ):
														with io.BytesIO() as buffer:
																				JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	num_proc=0   )







		@pytest.mark.parametrize('''compression, extension''',	[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  tmp_path_factory.mktemp('''data'''   ) / f"test.json.{extension}"
								lowerCAmelCase_      			=  str(shared_datadir / f"test_file.json.{extension}"   )
								JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	compression=UpperCamelCase__   ).write()

								with fsspec.open(UpperCamelCase__,	'''rb''',	compression='''infer'''   ) as f:
														lowerCAmelCase_      			=  f.read()
								with fsspec.open(UpperCamelCase__,	'''rb''',	compression='''infer'''   ) as f:
														lowerCAmelCase_      			=  f.read()
								assert exported_content == original_content




