


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_pytesseract_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def __init__( self			:  Tuple				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Dict=7				,				lowerCamelCase__			:  int=3				,				lowerCamelCase__			:  Dict=18				,				lowerCamelCase__			:  List[str]=30				,				lowerCamelCase__			:  int=400				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Tuple=None				,				lowerCamelCase__			:  List[Any]=True				,				):
        a__	:  Dict     =   size if size is not None else {"height": 18, "width": 18}
        a__	:  Union[str, Any]     =   parent
        a__	:  Dict     =   batch_size
        a__	:  Union[str, Any]     =   num_channels
        a__	:  Optional[int]     =   image_size
        a__	:  Tuple     =   min_resolution
        a__	:  Dict     =   max_resolution
        a__	:  Tuple     =   do_resize
        a__	:  int     =   size
        a__	:  List[str]     =   apply_ocr

  def    _UpperCamelCase( self			:  List[Any]       ):
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		LayoutLMvaImageProcessor if is_pytesseract_available() else None

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Any     =   LayoutLMvaImageProcessingTester(self       )

  @property
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        return self.image_processor_tester.prepare_image_processor_dict()

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Tuple     =   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"do_resize"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"size"       )       )
        self.assertTrue(hasattr(lowerCamelCase__				,				"apply_ocr"       )       )

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  int     =   self.image_processing_class.from_dict(self.image_processor_dict       )
        self.assertEqual(image_processor.size				,				{"height": 18, "width": 18}       )

        a__	:  Optional[Any]     =   self.image_processing_class.from_dict(self.image_processor_dict				,				size=42       )
        self.assertEqual(image_processor.size				,				{"height": 42, "width": 42}       )

  def    _UpperCamelCase( self			:  Dict       ):
        pass

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        # Initialize image_processing
        a__	:  Dict     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        a__	:  Tuple     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				Image.Image       )

        # Test not batched input
        a__	:  List[str]     =   image_processing(image_inputs[0]				,				return_tensors="pt"       )
        self.assertEqual(
            encoding.pixel_values.shape				,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

        self.assertIsInstance(encoding.words				,				lowerCamelCase__       )
        self.assertIsInstance(encoding.boxes				,				lowerCamelCase__       )

        # Test batched
        a__	:  Union[str, Any]     =   image_processing(lowerCamelCase__				,				return_tensors="pt"       ).pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

  def    _UpperCamelCase( self			:  int       ):
        # Initialize image_processing
        a__	:  Tuple     =   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        a__	:  Dict     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__				,				numpify=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				np.ndarray       )

        # Test not batched input
        a__	:  Any     =   image_processing(image_inputs[0]				,				return_tensors="pt"       ).pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

        # Test batched
        a__	:  Union[str, Any]     =   image_processing(lowerCamelCase__				,				return_tensors="pt"       ).pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        # Initialize image_processing
        a__	:  List[str]     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        a__	:  Dict     =   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=lowerCamelCase__				,				torchify=lowerCamelCase__       )
        for image in image_inputs:
              self.assertIsInstance(lowerCamelCase__				,				torch.Tensor       )

        # Test not batched input
        a__	:  List[Any]     =   image_processing(image_inputs[0]				,				return_tensors="pt"       ).pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

        # Test batched
        a__	:  int     =   image_processing(lowerCamelCase__				,				return_tensors="pt"       ).pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )				,				)

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        # with apply_OCR = True
        a__	:  Any     =   LayoutLMvaImageProcessor()

        from datasets import load_dataset

        a__	:  int     =   load_dataset("hf-internal-testing/fixtures_docvqa"				,				split="test"       )

        a__	:  List[str]     =   Image.open(ds[0]["file"]       ).convert("RGB"       )

        a__	:  Any     =   image_processing(lowerCamelCase__				,				return_tensors="pt"       )

        self.assertEqual(encoding.pixel_values.shape				,				(1, 3, 224, 224)       )
        self.assertEqual(len(encoding.words       )				,				len(encoding.boxes       )       )

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        a__	:  Tuple     =   [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
        a__	:  Union[str, Any]     =   [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
        # fmt: on

        self.assertListEqual(encoding.words				,				lowerCamelCase__       )
        self.assertListEqual(encoding.boxes				,				lowerCamelCase__       )

        # with apply_OCR = False
        a__	:  List[str]     =   LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase__       )

        a__	:  List[Any]     =   image_processing(lowerCamelCase__				,				return_tensors="pt"       )

        self.assertEqual(encoding.pixel_values.shape				,				(1, 3, 224, 224)       )




"""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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Optional[Any]:
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"					,					[False, True]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Any:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Tuple:
 lowerCAmelCase__   :				Union[str, Any] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Tuple 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				List[Any] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Tuple 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Optional[Any] 			=				{"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				List[str] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 2
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_3", "col_1", "col_2"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Tuple:
 # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				Any 			=				{"col_2": "int64", "col_3": "float64", "col_1": "string"}
 lowerCAmelCase__   :				Union[str, Any] 			=				features.copy()
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 2
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_2", "col_3", "col_1"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("split"					,					[None, NamedSplit("train"   ), "train", "test"]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					split=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )
 assert dataset.split == split if split else "train"



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



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__=("train",)   )			->						List[str]:
 assert isinstance(lowercase__					,					lowercase__   )
 for split in splits:
  lowerCAmelCase__   :				Dict 			=				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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				int 			=				tmp_path / "cache"
 lowerCAmelCase__   :				str 			=				{"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__   :				Dict 			=				JsonDatasetReader({"train": jsonl_path}					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Union[str, Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				str 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				List[Any] 			=				JsonDatasetReader({"train": jsonl_path}					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



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



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[Any]:
 return json.load(lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 return [json.loads(lowercase__   ) for line in buffer]






class 			__a					:





       """simple docstring"""


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





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 10






       @pytest.mark.parametrize(
           "orient, container, keys, len_at"			,					[
               ("records", list, {"tokens", "labels", "answers", "id"}, None),
               ("split", dict, {"columns", "data"}, "data"),
               ("index", dict, set("0123456789"   ), None),
               ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
               ("values", list, None, None),
               ("table", dict, {"schema", "data"}, "data"),
           ]			,					)
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

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






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





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					num_proc=2   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 10






       @pytest.mark.parametrize(
           "orient, container, keys, len_at"			,					[
               ("records", list, {"tokens", "labels", "answers", "id"}, None),
               ("split", dict, {"columns", "data"}, "data"),
               ("index", dict, set("0123456789"   ), None),
               ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
               ("values", list, None, None),
               ("table", dict, {"schema", "data"}, "data"),
           ]			,					)
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






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





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				tmp_path_factory.mktemp("data"   ) / F"""test.json.{extension}"""
        lowerCAmelCase__   :				str 			=				str(shared_datadir / F"""test_file.json.{extension}"""   )
        JsonDatasetWriter(snake_case			,					snake_case			,					compression=snake_case   ).write()

        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        assert exported_content == original_content






