





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor



class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_ = True   ,				A_ = None   ,				A_ = 32   ,				A_ = True   ,				A_ = 1 / 2_55   ,				A_ = True   ,				A_ = True   ,				A_ = [0.48145466, 0.4578275, 0.40821073]   ,				A_ = [0.26862954, 0.26130258, 0.27577711]   ,				A_ = True   ,				A_=7   ,				A_=30   ,				A_=4_00   ,				A_=3   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			do_resize
        SCREAMING_SNAKE_CASE__											=			size if size is not None else {'''shortest_edge''': 2_88}
        SCREAMING_SNAKE_CASE__											=			size_divisor
        SCREAMING_SNAKE_CASE__											=			do_rescale
        SCREAMING_SNAKE_CASE__											=			rescale_factor
        SCREAMING_SNAKE_CASE__											=			do_normalize
        SCREAMING_SNAKE_CASE__											=			do_center_crop
        SCREAMING_SNAKE_CASE__											=			image_mean
        SCREAMING_SNAKE_CASE__											=			image_std
        SCREAMING_SNAKE_CASE__											=			do_pad
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			num_channels
        SCREAMING_SNAKE_CASE__											=			min_resolution
        SCREAMING_SNAKE_CASE__											=			max_resolution



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }




    def  lowercase_					(       self   ,				A_   ,				A_=False     ):





        '''simple docstring'''

        if not batched:
            SCREAMING_SNAKE_CASE__											=			self.size['''shortest_edge''']
            SCREAMING_SNAKE_CASE__											=			image_inputs[0]
            if isinstance(A_   ,				Image.Image     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			image.size
            else:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			image.shape[1], image.shape[2]
            SCREAMING_SNAKE_CASE__											=			size / min(A_   ,				A_     )
            if h < w:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			size, scale * w
            else:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			scale * h, size

            SCREAMING_SNAKE_CASE__											=			int((13_33 / 8_00) * size     )
            if max(A_   ,				A_     ) > max_size:
                SCREAMING_SNAKE_CASE__											=			max_size / max(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			newh * scale
                SCREAMING_SNAKE_CASE__											=			neww * scale

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			int(newh + 0.5     ), int(neww + 0.5     )
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			(
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            SCREAMING_SNAKE_CASE__											=			[]
            for image in image_inputs:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.get_expected_values([image]     )
                expected_values.append((expected_height, expected_width)     )
            SCREAMING_SNAKE_CASE__											=			max(A_   ,				key=lambda A_     : item[0]     )[0]
            SCREAMING_SNAKE_CASE__											=			max(A_   ,				key=lambda A_     : item[1]     )[1]

        return expected_height, expected_width





@require_torch
@require_vision
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	BridgeTowerImageProcessor if is_vision_available() else None



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BridgeTowerImageProcessingTester(self     )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.image_processor_tester.prepare_image_processor_dict()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(A_   ,				'''image_mean'''     )     )
        self.assertTrue(hasattr(A_   ,				'''image_std'''     )     )
        self.assertTrue(hasattr(A_   ,				'''do_normalize'''     )     )
        self.assertTrue(hasattr(A_   ,				'''do_resize'''     )     )
        self.assertTrue(hasattr(A_   ,				'''size'''     )     )
        self.assertTrue(hasattr(A_   ,				'''size_divisor'''     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        SCREAMING_SNAKE_CASE__											=			prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=A_     )
        for image in image_inputs:
            self.assertIsInstance(A_   ,				Image.Image     )

        # Test not batched input
        SCREAMING_SNAKE_CASE__											=			image_processing(image_inputs[0]   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor_tester.get_expected_values(A_     )
        self.assertEqual(
            encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

        # Test batched
        SCREAMING_SNAKE_CASE__											=			image_processing(A_   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor_tester.get_expected_values(A_   ,				batched=A_     )
        self.assertEqual(
            encoded_images.shape   ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   ,				)



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE__											=			prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=A_   ,				numpify=A_     )
        for image in image_inputs:
            self.assertIsInstance(A_   ,				np.ndarray     )

        # Test not batched input
        SCREAMING_SNAKE_CASE__											=			image_processing(image_inputs[0]   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor_tester.get_expected_values(A_     )
        self.assertEqual(
            encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

        # Test batched
        SCREAMING_SNAKE_CASE__											=			image_processing(A_   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor_tester.get_expected_values(A_   ,				batched=A_     )
        self.assertEqual(
            encoded_images.shape   ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   ,				)




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE__											=			prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=A_   ,				torchify=A_     )
        for image in image_inputs:
            self.assertIsInstance(A_   ,				torch.Tensor     )

        # Test not batched input
        SCREAMING_SNAKE_CASE__											=			image_processing(image_inputs[0]   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor_tester.get_expected_values(A_     )
        self.assertEqual(
            encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

        # Test batched
        SCREAMING_SNAKE_CASE__											=			image_processing(A_   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.image_processor_tester.get_expected_values(A_   ,				batched=A_     )
        self.assertEqual(
            encoded_images.shape   ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   ,				)







"""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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""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				:		Tuple				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

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

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

@pytest.mark.parametrize("""path_type"""							,     [str, list]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			jsonl_path
						elif issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			[jsonl_path]
						UpperCamelCase				:		List[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		List[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[int]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=("train",)     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						for split in splits:
												UpperCamelCase				:		Optional[Any]				   =			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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			tmp_path / """cache"""
						UpperCamelCase				:		int				   =			{"""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				:		Any				   =			JsonDatasetReader({"""train""": jsonl_path}							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		Optional[Any]				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[Any]				   =			JsonDatasetReader({"""train""": jsonl_path}							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return json.load(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return [json.loads(SCREAMING_SNAKE_CASE     ) for line in buffer]






class 							lowercase__			:

			"""simple docstring"""







			@pytest.mark.parametrize("""lines, load_json_function"""						,   [(True, load_json_lines), (False, load_json)]    )
			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		Optional[Any]				   =			load_json_function(_A    )
									assert isinstance(_A						,   _A    )
									assert isinstance(exported_content[0]						,   _A    )
									assert len(_A    ) == 1_0







			@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						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   orient=_A    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		str				   =			load_json(_A    )
									assert isinstance(_A						,   _A    )
									if keys:
															if container is dict:
																					assert exported_content.keys() == keys
															else:
																					assert exported_content[0].keys() == keys
									else:
															assert not hasattr(_A						,   """keys"""    ) and not hasattr(exported_content[0]						,   """keys"""    )
									if len_at:
															assert len(exported_content[len_at]    ) == 1_0
									else:
															assert len(_A    ) == 1_0







			@pytest.mark.parametrize("""lines, load_json_function"""						,   [(True, load_json_lines), (False, load_json)]    )
			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   num_proc=2    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		List[Any]				   =			load_json_function(_A    )
									assert isinstance(_A						,   _A    )
									assert isinstance(exported_content[0]						,   _A    )
									assert len(_A    ) == 1_0







			@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						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   orient=_A						,   num_proc=2    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		Dict				   =			load_json(_A    )
									assert isinstance(_A						,   _A    )
									if keys:
															if container is dict:
																					assert exported_content.keys() == keys
															else:
																					assert exported_content[0].keys() == keys
									else:
															assert not hasattr(_A						,   """keys"""    ) and not hasattr(exported_content[0]						,   """keys"""    )
									if len_at:
															assert len(exported_content[len_at]    ) == 1_0
									else:
															assert len(_A    ) == 1_0







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







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







			@pytest.mark.parametrize("""compression, extension"""						,   [("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]    )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			tmp_path_factory.mktemp("""data"""    ) / f"""test.json.{extension}"""
									UpperCamelCase				:		Tuple				   =			str(shared_datadir / f"""test_file.json.{extension}"""    )
									JsonDatasetWriter(_A						,   _A						,   compression=_A    ).write()

									with fsspec.open(_A						,   """rb"""						,   compression="""infer"""    ) as f:
															UpperCamelCase				:		Dict				   =			f.read()
									with fsspec.open(_A						,   """rb"""						,   compression="""infer"""    ) as f:
															UpperCamelCase				:		Tuple				   =			f.read()
									assert exported_content == original_content



