

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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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(UpperCAmelCase__		,					cache_dir=UpperCAmelCase__		,					keep_in_memory=UpperCAmelCase__      ).read()
					_check_json_dataset(UpperCAmelCase__		,					UpperCAmelCase__      )


@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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__lowerCAmelCase					=							JsonDatasetReader(UpperCAmelCase__		,					features=UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					_check_json_dataset(UpperCAmelCase__		,					UpperCAmelCase__      )


@pytest.mark.parametrize(
    'features'		,					[
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ]		,					)
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__lowerCAmelCase					=							JsonDatasetReader(UpperCAmelCase__		,					features=UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					assert isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
					__lowerCAmelCase					=							features.copy()
					__lowerCAmelCase					=							(
					    Features({feature: Value(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							JsonDatasetReader(UpperCAmelCase__		,					features=UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					assert isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					__lowerCAmelCase					=							JsonDatasetReader(UpperCAmelCase__		,					cache_dir=UpperCAmelCase__		,					split=UpperCAmelCase__      ).read()
					_check_json_dataset(UpperCAmelCase__		,					UpperCAmelCase__      )
					assert dataset.split == split if split else "train"


@pytest.mark.parametrize('path_type'		,					[str, list]      )
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if issubclass(UpperCAmelCase__		,					UpperCAmelCase__      ):
										__lowerCAmelCase					=							jsonl_path
					elif issubclass(UpperCAmelCase__		,					UpperCAmelCase__      ):
										__lowerCAmelCase					=							[jsonl_path]
					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					__lowerCAmelCase					=							JsonDatasetReader(UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					_check_json_dataset(UpperCAmelCase__		,					UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=("train",)      ):

					"""simple docstring"""






					assert isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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=UpperCAmelCase__		,					keep_in_memory=UpperCAmelCase__      ).read()
					_check_json_datasetdict(UpperCAmelCase__		,					UpperCAmelCase__      )


@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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__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(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__lowerCAmelCase					=							JsonDatasetReader({'train': jsonl_path}		,					features=UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					_check_json_datasetdict(UpperCAmelCase__		,					UpperCAmelCase__      )


@pytest.mark.parametrize('split'		,					[None, NamedSplit('train'      ), 'train', 'test']      )
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					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(UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					_check_json_datasetdict(UpperCAmelCase__		,					UpperCAmelCase__		,					splits=list(path.keys()      )      )
					assert all(dataset[split].split == split for split in path.keys()      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					return json.load(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					return [json.loads(UpperCAmelCase__      ) for line in buffer]





class     snake_case_			:


							"""simple docstring"""





							@pytest.mark.parametrize('lines, load_json_function'					,						[(True, load_json_lines), (False, load_json)]							)
							def        A__  ( self					,						_A					,						_A					,						_A							):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(_A					,						_A					,						lines=_A							).write()
																	buffer.seek(0							)
																	__lowerCAmelCase					=							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							):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(_A					,						_A					,						lines=_A					,						orient=_A							).write()
																	buffer.seek(0							)
																	__lowerCAmelCase					=							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							):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(_A					,						_A					,						lines=_A					,						num_proc=2							).write()
																	buffer.seek(0							)
																	__lowerCAmelCase					=							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							):
												with io.BytesIO() as buffer:
																	JsonDatasetWriter(_A					,						_A					,						lines=_A					,						orient=_A					,						num_proc=2							).write()
																	buffer.seek(0							)
																	__lowerCAmelCase					=							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							):
												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							):
												__lowerCAmelCase					=							tmp_path_factory.mktemp('data'							) / F"""test.json.{extension}"""
												__lowerCAmelCase					=							str(shared_datadir / F"""test_file.json.{extension}"""							)
												JsonDatasetWriter(_A					,						_A					,						compression=_A							).write()

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









import unittest

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 ViTImageProcessor





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3					,						_A=2_2_4					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( self							):
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}





@require_torch
@require_vision
class     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =ViTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							EfficientFormerImageProcessorTester(self							)




							@property
							def        A__  ( self							):
												return self.image_proc_tester.prepare_image_processor_dict()




							def        A__  ( self							):
												__lowerCAmelCase					=							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'							)							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						Image.Image							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						numpify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						np.ndarray							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						torchify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						torch.Tensor							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)






