

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				(PNDMScheduler,)
			__lowerCamelCase       				=				(('num_inference_steps', 50),)


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  Tuple:
							'''simple docstring'''






							A__						=	{
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**lowercase				)
							return config


			def    UpperCamelCase						(			self					,					lowercase=0					,					**lowercase				)     ->  Any:
							'''simple docstring'''






							A__						=	dict(self.forward_default_kwargs				)
							A__						=	kwargs.pop("num_inference_steps"					,					lowercase				)
							A__						=	self.dummy_sample
							A__						=	0.1 * sample
							A__						=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
											A__						=	self.get_scheduler_config(**lowercase				)
											A__						=	scheduler_class(**lowercase				)
											scheduler.set_timesteps(lowercase				)
											# copy over dummy past residuals
											A__						=	dummy_past_residuals[:]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(lowercase				)
															A__						=	scheduler_class.from_pretrained(lowercase				)
															new_scheduler.set_timesteps(lowercase				)
															# copy over dummy past residuals
															A__						=	dummy_past_residuals[:]

											A__						=	scheduler.step_prk(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample
											A__						=	new_scheduler.step_prk(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

											A__						=	scheduler.step_plms(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample
											A__						=	new_scheduler.step_plms(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self					,					lowercase=0					,					**lowercase				)     ->  Tuple:
							'''simple docstring'''






							A__						=	dict(self.forward_default_kwargs				)
							A__						=	kwargs.pop("num_inference_steps"					,					lowercase				)
							A__						=	self.dummy_sample
							A__						=	0.1 * sample
							A__						=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
											A__						=	self.get_scheduler_config()
											A__						=	scheduler_class(**lowercase				)
											scheduler.set_timesteps(lowercase				)

											# copy over dummy past residuals (must be after setting timesteps)
											A__						=	dummy_past_residuals[:]

											with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(lowercase				)
															A__						=	scheduler_class.from_pretrained(lowercase				)
															# copy over dummy past residuals
															new_scheduler.set_timesteps(lowercase				)

															# copy over dummy past residual (must be after setting timesteps)
															A__						=	dummy_past_residuals[:]

											A__						=	scheduler.step_prk(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample
											A__						=	new_scheduler.step_prk(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

											A__						=	scheduler.step_plms(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample
											A__						=	new_scheduler.step_plms(lowercase					,					lowercase					,					lowercase					,					**lowercase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.scheduler_classes[0]
							A__						=	self.get_scheduler_config(**lowercase				)
							A__						=	scheduler_class(**lowercase				)

							A__						=	10
							A__						=	self.dummy_model()
							A__						=	self.dummy_sample_deter
							scheduler.set_timesteps(lowercase				)

							for i, t in enumerate(scheduler.prk_timesteps				):
											A__						=	model(lowercase					,					lowercase				)
											A__						=	scheduler.step_prk(lowercase					,					lowercase					,					lowercase				).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps				):
											A__						=	model(lowercase					,					lowercase				)
											A__						=	scheduler.step_plms(lowercase					,					lowercase					,					lowercase				).prev_sample

							return sample


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	dict(self.forward_default_kwargs				)

							A__						=	kwargs.pop("num_inference_steps"					,					lowercase				)

							for scheduler_class in self.scheduler_classes:
											A__						=	self.get_scheduler_config()
											A__						=	scheduler_class(**lowercase				)

											A__						=	self.dummy_sample
											A__						=	0.1 * sample

											if num_inference_steps is not None and hasattr(lowercase					,					"set_timesteps"				):
															scheduler.set_timesteps(lowercase				)
											elif num_inference_steps is not None and not hasattr(lowercase					,					"set_timesteps"				):
															A__						=	num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											A__						=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
											A__						=	dummy_past_residuals[:]

											A__						=	scheduler.step_prk(lowercase					,					0					,					lowercase					,					**lowercase				).prev_sample
											A__						=	scheduler.step_prk(lowercase					,					1					,					lowercase					,					**lowercase				).prev_sample

											self.assertEqual(output_a.shape					,					sample.shape				)
											self.assertEqual(output_a.shape					,					output_a.shape				)

											A__						=	scheduler.step_plms(lowercase					,					0					,					lowercase					,					**lowercase				).prev_sample
											A__						=	scheduler.step_plms(lowercase					,					1					,					lowercase					,					**lowercase				).prev_sample

											self.assertEqual(output_a.shape					,					sample.shape				)
											self.assertEqual(output_a.shape					,					output_a.shape				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=lowercase				)

							A__						=	self.scheduler_classes[0]
							A__						=	self.get_scheduler_config(steps_offset=1				)
							A__						=	scheduler_class(**lowercase				)
							scheduler.set_timesteps(10				)
							assert torch.equal(
							    scheduler.timesteps					,					torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)					,					)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0001, 0.001]					,					[0.002, 0.02]				):
											self.check_over_configs(beta_start=lowercase					,					beta_end=lowercase				)


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							for t in [1, 5, 10]:
											self.check_over_forward(time_step=lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							for t, num_inference_steps in zip([1, 5, 10]					,					[10, 50, 100]				):
											self.check_over_forward(num_inference_steps=lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	27

							for scheduler_class in self.scheduler_classes:
											A__						=	self.dummy_sample
											A__						=	0.1 * sample

											A__						=	self.get_scheduler_config()
											A__						=	scheduler_class(**lowercase				)

											scheduler.set_timesteps(lowercase				)

											# before power of 3 fix, would error on first step, so we only need to do two
											for i, t in enumerate(scheduler.prk_timesteps[:2]				):
															A__						=	scheduler.step_prk(lowercase					,					lowercase					,					lowercase				).prev_sample


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							with self.assertRaises(lowercase				):
											A__						=	self.scheduler_classes[0]
											A__						=	self.get_scheduler_config()
											A__						=	scheduler_class(**lowercase				)

											scheduler.step_plms(self.dummy_sample					,					1					,					self.dummy_sample				).prev_sample


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.full_loop()
							A__						=	torch.sum(torch.abs(lowercase				)				)
							A__						=	torch.mean(torch.abs(lowercase				)				)

							assert abs(result_sum.item() - 198.1318				) < 1e-2
							assert abs(result_mean.item() - 0.2580				) < 1e-3


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.full_loop(prediction_type="v_prediction"				)
							A__						=	torch.sum(torch.abs(lowercase				)				)
							A__						=	torch.mean(torch.abs(lowercase				)				)

							assert abs(result_sum.item() - 67.3986				) < 1e-2
							assert abs(result_mean.item() - 0.0878				) < 1e-3


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.full_loop(set_alpha_to_one=lowercase					,					beta_start=0.01				)
							A__						=	torch.sum(torch.abs(lowercase				)				)
							A__						=	torch.mean(torch.abs(lowercase				)				)

							assert abs(result_sum.item() - 230.0399				) < 1e-2
							assert abs(result_mean.item() - 0.2995				) < 1e-3


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.full_loop(set_alpha_to_one=lowercase					,					beta_start=0.01				)
							A__						=	torch.sum(torch.abs(lowercase				)				)
							A__						=	torch.mean(torch.abs(lowercase				)				)

							assert abs(result_sum.item() - 186.9482				) < 1e-2
							assert abs(result_mean.item() - 0.2434				) < 1e-3


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_				(			_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       int):
    assert isinstance(_lowerCamelCase		,	_lowerCamelCase)
    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_				(			_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Any		,	_lowerCamelCase							:       str):
    lowercase__ :   Optional[int]    					=				tmp_path / "cache"
    lowercase__ :   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():
        lowercase__ :   Union[str, Any]    					=				JsonDatasetReader(_lowerCamelCase		,	cache_dir=_lowerCamelCase		,	keep_in_memory=_lowerCamelCase).read()
    _check_json_dataset(_lowerCamelCase		,	_lowerCamelCase)

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

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

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

def 			lowercase_				(			_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       Optional[int]=("train",)):
    assert isinstance(_lowerCamelCase		,	_lowerCamelCase)
    for split in splits:
        lowercase__ :   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 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       str):
    lowercase__ :   List[str]    					=				tmp_path / "cache"
    lowercase__ :   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():
        lowercase__ :   Optional[Any]    					=				JsonDatasetReader({"train": jsonl_path}		,	cache_dir=_lowerCamelCase		,	keep_in_memory=_lowerCamelCase).read()
    _check_json_datasetdict(_lowerCamelCase		,	_lowerCamelCase)

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

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

def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]):
    return json.load(_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    return [json.loads(_lowerCamelCase) for line in buffer]



class   snake_case_       :






   @pytest.mark.parametrize("lines, load_json_function"	,						[(True, load_json_lines), (False, load_json)]  )
   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Any	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Dict  )					->						Optional[Any]:
       with io.BytesIO() as buffer:
           JsonDatasetWriter(lowercase_	,						lowercase_	,						lines=lowercase_  ).write()
           buffer.seek(0  )
           lowercase__ :   Optional[int]    					=				load_json_function(lowercase_  )
       assert isinstance(lowercase_	,						lowercase_  )
       assert isinstance(exported_content[0]	,						lowercase_  )
       assert len(lowercase_  ) == 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       __UpperCamelCase     (      self				:     str	,						lowercase_				:     int	,						lowercase_				:     str	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Dict	,						lowercase_				:     Tuple  )					->						List[str]:
       with io.BytesIO() as buffer:
           JsonDatasetWriter(lowercase_	,						lowercase_	,						lines=lowercase_	,						orient=lowercase_  ).write()
           buffer.seek(0  )
           lowercase__ :   str    					=				load_json(lowercase_  )
       assert isinstance(lowercase_	,						lowercase_  )
       if keys:
           if container is dict:
               assert exported_content.keys() == keys
           else:
               assert exported_content[0].keys() == keys
       else:
           assert not hasattr(lowercase_	,						"keys"  ) and not hasattr(exported_content[0]	,						"keys"  )
       if len_at:
           assert len(exported_content[len_at]  ) == 10
       else:
           assert len(lowercase_  ) == 10






   @pytest.mark.parametrize("lines, load_json_function"	,						[(True, load_json_lines), (False, load_json)]  )
   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     int	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Dict  )					->						Optional[int]:
       with io.BytesIO() as buffer:
           JsonDatasetWriter(lowercase_	,						lowercase_	,						lines=lowercase_	,						num_proc=2  ).write()
           buffer.seek(0  )
           lowercase__ :   str    					=				load_json_function(lowercase_  )
       assert isinstance(lowercase_	,						lowercase_  )
       assert isinstance(exported_content[0]	,						lowercase_  )
       assert len(lowercase_  ) == 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       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     Dict	,						lowercase_				:     Dict	,						lowercase_				:     Optional[int]	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Dict  )					->						Any:
       with io.BytesIO() as buffer:
           JsonDatasetWriter(lowercase_	,						lowercase_	,						lines=lowercase_	,						orient=lowercase_	,						num_proc=2  ).write()
           buffer.seek(0  )
           lowercase__ :   Optional[Any]    					=				load_json(lowercase_  )
       assert isinstance(lowercase_	,						lowercase_  )
       if keys:
           if container is dict:
               assert exported_content.keys() == keys
           else:
               assert exported_content[0].keys() == keys
       else:
           assert not hasattr(lowercase_	,						"keys"  ) and not hasattr(exported_content[0]	,						"keys"  )
       if len_at:
           assert len(exported_content[len_at]  ) == 10
       else:
           assert len(lowercase_  ) == 10






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     List[str]  )					->						str:
       with pytest.raises(lowercase_  ):
           with io.BytesIO() as buffer:
               JsonDatasetWriter(lowercase_	,						lowercase_	,						num_proc=0  )







   @pytest.mark.parametrize("compression, extension"	,						[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]  )
   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     List[str]	,						lowercase_				:     Optional[Any]	,						lowercase_				:     str	,						lowercase_				:     List[Any]  )					->						Any:
       lowercase__ :   Dict    					=				tmp_path_factory.mktemp("data"  ) / F'''test.json.{extension}'''
       lowercase__ :   Optional[int]    					=				str(shared_datadir / F'''test_file.json.{extension}'''  )
       JsonDatasetWriter(lowercase_	,						lowercase_	,						compression=lowercase_  ).write()

       with fsspec.open(lowercase_	,						"rb"	,						compression="infer"  ) as f:
           lowercase__ :   List[Any]    					=				f.read()
       with fsspec.open(lowercase_	,						"rb"	,						compression="infer"  ) as f:
           lowercase__ :   str    					=				f.read()
       assert exported_content == original_content



