

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor






class        A_		(       unittest.TestCase				):
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int=3   ,SCREAMING_SNAKE_CASE__		:			List[str]=3_2   ,SCREAMING_SNAKE_CASE__		:			Dict=3   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=1_0   ,SCREAMING_SNAKE_CASE__		:			List[Any]=[1_0, 2_0, 3_0, 4_0]   ,SCREAMING_SNAKE_CASE__		:			Any=[1, 1, 2, 1]   ,SCREAMING_SNAKE_CASE__		:			List[Any]=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=True   ,SCREAMING_SNAKE_CASE__		:			Tuple="relu"   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=3   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=None   ,):
								__lowerCamelCase				:				int					       = parent
								__lowerCamelCase				:				Union[str, Any]					       = batch_size
								__lowerCamelCase				:				Optional[int]					       = image_size
								__lowerCamelCase				:				int					       = num_channels
								__lowerCamelCase				:				str					       = embeddings_size
								__lowerCamelCase				:				Optional[Any]					       = hidden_sizes
								__lowerCamelCase				:				Optional[int]					       = depths
								__lowerCamelCase				:				Dict					       = is_training
								__lowerCamelCase				:				Any					       = use_labels
								__lowerCamelCase				:				Optional[int]					       = hidden_act
								__lowerCamelCase				:				Union[str, Any]					       = num_labels
								__lowerCamelCase				:				Optional[Any]					       = scope
								__lowerCamelCase				:				Tuple					       = len(UpperCamelCase__)
							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase				:				str					       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase				:				Optional[Any]					       = self.get_config()

								return config, pixel_values
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								return RegNetConfig(
								    num_channels=self.num_channels   ,embeddings_size=self.embeddings_size   ,hidden_sizes=self.hidden_sizes   ,depths=self.depths   ,hidden_act=self.hidden_act   ,num_labels=self.num_labels   ,image_size=self.image_size   ,)
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								__lowerCamelCase				:				Tuple					       = FlaxRegNetModel(config=UpperCamelCase__)
								__lowerCamelCase				:				List[Any]					       = model(UpperCamelCase__)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape   ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,)
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Any):
								__lowerCamelCase				:				str					       = self.num_labels
								__lowerCamelCase				:				Tuple					       = FlaxRegNetForImageClassification(config=UpperCamelCase__)
								__lowerCamelCase				:				Optional[Any]					       = model(UpperCamelCase__)
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels))


							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				str					       = self.prepare_config_and_inputs()
								__lowerCamelCase       , __lowerCamelCase				:				Tuple					       = config_and_inputs
								__lowerCamelCase				:				Tuple					       = {'pixel_values': pixel_values}
								return config, inputs_dict






@require_flax
class        A_		(       __UpperCAmelCase			,							unittest.TestCase				):
							_UpperCAmelCase    :  Union[str, Any]						      =      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

							_UpperCAmelCase    :  Tuple						      =      False
							_UpperCAmelCase    :  str						      =      False
							_UpperCAmelCase    :  List[Any]						      =      False
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				Tuple					       = FlaxRegNetModelTester(self)
								__lowerCamelCase				:				Optional[int]					       = ConfigTester(self   ,config_class=UpperCamelCase__   ,has_text_modality=UpperCamelCase__)
							def     lowerCAmelCase     (		self		:			int):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()
							def     lowerCAmelCase     (		self		:			Tuple):
								return
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				Union[str, Any]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__)
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__)
							@unittest.skip(reason='RegNet does not use inputs_embeds')
							def     lowerCAmelCase     (		self		:			Any):
								pass
							@unittest.skip(reason='RegNet does not support input and output embeddings')
							def     lowerCAmelCase     (		self		:			Any):
								pass
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				int					       = model_class(UpperCamelCase__)
									__lowerCamelCase				:				int					       = inspect.signature(model.__call__)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowerCamelCase				:				Tuple					       = [*signature.parameters.keys()]

									__lowerCamelCase				:				Any					       = ['pixel_values']
									self.assertListEqual(arg_names[:1]   ,UpperCamelCase__)
							def     lowerCAmelCase     (		self		:			Union[str, Any]):



								def check_hidden_states_output(SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Dict):
									__lowerCamelCase				:				List[str]					       = model_class(UpperCamelCase__)

									__lowerCamelCase				:				Optional[Any]					       = model(**self._prepare_for_class(UpperCamelCase__   ,UpperCamelCase__))

									__lowerCamelCase				:				Optional[Any]					       = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									__lowerCamelCase				:				Optional[int]					       = self.model_tester.num_stages
									self.assertEqual(len(UpperCamelCase__)   ,expected_num_stages + 1)

								__lowerCamelCase       , __lowerCamelCase				:				Dict					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				List[Any]					       = True
									check_hidden_states_output(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase				:				str					       = True

									check_hidden_states_output(UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__)


							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase       , __lowerCamelCase				:				Any					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										__lowerCamelCase				:				Optional[int]					       = self._prepare_for_class(UpperCamelCase__   ,UpperCamelCase__)
										__lowerCamelCase				:				Union[str, Any]					       = model_class(UpperCamelCase__)

										@jax.jit
										def model_jitted(SCREAMING_SNAKE_CASE__		:			List[str]   ,**SCREAMING_SNAKE_CASE__		:			int):
											return model(pixel_values=UpperCamelCase__   ,**UpperCamelCase__)

										with self.subTest('JIT Enabled'):
											__lowerCamelCase				:				Union[str, Any]					       = model_jitted(**UpperCamelCase__).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												__lowerCamelCase				:				str					       = model_jitted(**UpperCamelCase__).to_tuple()

										self.assertEqual(len(UpperCamelCase__)   ,len(UpperCamelCase__))
										for jitted_output, output in zip(UpperCamelCase__   ,UpperCamelCase__):
											self.assertEqual(jitted_output.shape   ,output.shape)






def    SCREAMING_SNAKE_CASE__       (  )  ->     Tuple:
	__lowerCamelCase				:				List[Any]					       = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image






@require_flax
class        A_		(       unittest.TestCase				):
							@cached_property
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None


							@slow
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				int					       = FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')

								__lowerCamelCase				:				List[str]					       = self.default_image_processor
								__lowerCamelCase				:				List[str]					       = prepare_img()
								__lowerCamelCase				:				List[Any]					       = image_processor(images=UpperCamelCase__   ,return_tensors='np')

								__lowerCamelCase				:				int					       = model(**UpperCamelCase__)

								# verify the logits
								__lowerCamelCase				:				Optional[Any]					       = (1, 1_0_0_0)
								self.assertEqual(outputs.logits.shape   ,UpperCamelCase__)

								__lowerCamelCase				:				Dict					       = jnp.array([-0.4180, -1.5051, -3.4836])

								self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,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




