"""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      __lowerCAmelCase(		__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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Dict							=							tmp_path / 'cache'
	_lowercase :    List[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 :    Tuple							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							tmp_path / 'cache'
	_lowercase :    Tuple							=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	_lowercase :    List[str]							=							features.copy() if features else default_expected_features
	_lowercase :    Union[str, Any]							=							(
	    Features({feature: Value(__UpperCAmelCase			) for feature, dtype in features.items()}			) if features is not None else None
	)
	_lowercase :    Any							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							tmp_path / 'cache'
	_lowercase :    Dict							=							{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
	_lowercase :    Dict							=							features.copy() if features else default_expected_features
	_lowercase :    Optional[int]							=							(
	    Features({feature: Value(__UpperCAmelCase			) for feature, dtype in features.items()}			) if features is not None else None
	)
	_lowercase :    List[str]							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[Any]							=							{'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
	_lowercase :    List[str]							=							features.copy()
	_lowercase :    Any							=							(
	    Features({feature: Value(__UpperCAmelCase			) for feature, dtype in features.items()}			) if features is not None else None
	)
	_lowercase :    List[Any]							=							tmp_path / 'cache'
	_lowercase :    Dict							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Optional[int]							=							tmp_path / 'cache'
	_lowercase :    str							=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	_lowercase :    str							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if issubclass(__UpperCAmelCase     ,__UpperCAmelCase			):
		_lowercase :    Union[str, Any]							=							jsonl_path
	elif issubclass(__UpperCAmelCase     ,__UpperCAmelCase			):
		_lowercase :    Any							=							[jsonl_path]
	_lowercase :    str							=							tmp_path / 'cache'
	_lowercase :    Tuple							=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	_lowercase :    Any							=							JsonDatasetReader(__UpperCAmelCase     ,cache_dir=__UpperCAmelCase			).read()
	_check_json_dataset(__UpperCAmelCase     ,__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=("train",)			):






	"""simple docstring"""





	assert isinstance(__UpperCAmelCase     ,__UpperCAmelCase			)
	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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[str]							=							tmp_path / 'cache'
	_lowercase :    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():
		_lowercase :    Optional[int]							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    int							=							tmp_path / 'cache'
	_lowercase :    Optional[Any]							=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	_lowercase :    List[Any]							=							features.copy() if features else default_expected_features
	_lowercase :    List[str]							=							(
	    Features({feature: Value(__UpperCAmelCase			) for feature, dtype in features.items()}			) if features is not None else None
	)
	_lowercase :    List[str]							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if split:
		_lowercase :    Union[str, Any]							=							{split: jsonl_path}
	else:
		_lowercase :    Dict							=							'train'
		_lowercase :    Any							=							{'train': jsonl_path, 'test': jsonl_path}
	_lowercase :    str							=							tmp_path / 'cache'
	_lowercase :    Optional[Any]							=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	_lowercase :    int							=							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      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return json.load(__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





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







class  _lowerCamelCase			:







				@pytest.mark.parametrize('lines, load_json_function'			,					[(True, load_json_lines), (False, load_json)]						)
				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					with io.BytesIO() as buffer:
						JsonDatasetWriter(lowerCamelCase_			,					lowerCamelCase_			,					lines=lowerCamelCase_						).write()
						buffer.seek(0						)
						_lowercase :    Tuple							=							load_json_function(lowerCamelCase_						)
					assert isinstance(lowerCamelCase_			,					lowerCamelCase_						)
					assert isinstance(exported_content[0]			,					lowerCamelCase_						)
					assert len(lowerCamelCase_						) == 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 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






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







				@pytest.mark.parametrize('lines, load_json_function'			,					[(True, load_json_lines), (False, load_json)]						)
				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Any						):

					"""simple docstring"""






					with io.BytesIO() as buffer:
						JsonDatasetWriter(lowerCamelCase_			,					lowerCamelCase_			,					lines=lowerCamelCase_			,					num_proc=2						).write()
						buffer.seek(0						)
						_lowercase :    Optional[int]							=							load_json_function(lowerCamelCase_						)
					assert isinstance(lowerCamelCase_			,					lowerCamelCase_						)
					assert isinstance(exported_content[0]			,					lowerCamelCase_						)
					assert len(lowerCamelCase_						) == 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 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					with io.BytesIO() as buffer:
						JsonDatasetWriter(lowerCamelCase_			,					lowerCamelCase_			,					lines=lowerCamelCase_			,					orient=lowerCamelCase_			,					num_proc=2						).write()
						buffer.seek(0						)
						_lowercase :    Union[str, Any]							=							load_json(lowerCamelCase_						)
					assert isinstance(lowerCamelCase_			,					lowerCamelCase_						)
					if keys:
						if container is dict:
							assert exported_content.keys() == keys
						else:
							assert exported_content[0].keys() == keys
					else:
						assert not hasattr(lowerCamelCase_			,					'keys'						) and not hasattr(exported_content[0]			,					'keys'						)
					if len_at:
						assert len(exported_content[len_at]						) == 1_0
					else:
						assert len(lowerCamelCase_						) == 1_0







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






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







				@pytest.mark.parametrize('compression, extension'			,					[('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]						)
				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							tmp_path_factory.mktemp('data'						) / F'''test.json.{extension}'''
					_lowercase :    Dict							=							str(shared_datadir / F'''test_file.json.{extension}'''						)
					JsonDatasetWriter(lowerCamelCase_			,					lowerCamelCase_			,					compression=lowerCamelCase_						).write()

					with fsspec.open(lowerCamelCase_			,					'rb'			,					compression='infer'						) as f:
						_lowercase :    Union[str, Any]							=							f.read()
					with fsspec.open(lowerCamelCase_			,					'rb'			,					compression='infer'						) as f:
						_lowercase :    str							=							f.read()
					assert exported_content == original_content




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



