





import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


_SCREAMING_SNAKE_CASE     :   Dict      	=		pytest.mark.integration


@pytest.mark.parametrize('path'   , ['paws', 'csv']   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	inspect_dataset(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE   )
	snake_case_		:			List[str]											=	path + '.py'
	assert script_name in os.listdir(_SCREAMING_SNAKE_CASE   )
	assert "__pycache__" not in os.listdir(_SCREAMING_SNAKE_CASE   )


@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'   )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'   )
@pytest.mark.parametrize('path'   , ['accuracy']   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	inspect_metric(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE   )
	snake_case_		:			List[str]											=	path + '.py'
	assert script_name in os.listdir(_SCREAMING_SNAKE_CASE   )
	assert "__pycache__" not in os.listdir(_SCREAMING_SNAKE_CASE   )


@pytest.mark.parametrize(
    'path, config_name, expected_splits'   , [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Dict											=	get_dataset_config_info(_SCREAMING_SNAKE_CASE   , config_name=_SCREAMING_SNAKE_CASE   )
	assert info.config_name == config_name
	assert list(info.splits.keys()   ) == expected_splits


@pytest.mark.parametrize(
    'path, config_name, expected_exception'   , [
        ('paws', None, ValueError),
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	with pytest.raises(_SCREAMING_SNAKE_CASE   ):
		get_dataset_config_info(_SCREAMING_SNAKE_CASE   , config_name=_SCREAMING_SNAKE_CASE   )


@pytest.mark.parametrize(
    'path, expected'   , [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			List[str]											=	get_dataset_config_names(_SCREAMING_SNAKE_CASE   )
	assert expected in config_names


@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'   , [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			int											=	get_dataset_infos(_SCREAMING_SNAKE_CASE   )
	assert list(infos.keys()   ) == expected_configs
	snake_case_		:			List[str]											=	expected_configs[0]
	assert expected_config in infos
	snake_case_		:			List[Any]											=	infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()   ) == expected_splits_in_first_config


@pytest.mark.parametrize(
    'path, expected_config, expected_splits'   , [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Any											=	get_dataset_infos(_SCREAMING_SNAKE_CASE   )
	assert expected_config in infos
	snake_case_		:			Dict											=	infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()   ) == expected_splits


@pytest.mark.parametrize(
    'path, config_name, expected_exception'   , [
        ('paws', None, ValueError),
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	with pytest.raises(_SCREAMING_SNAKE_CASE   ):
		get_dataset_split_names(_SCREAMING_SNAKE_CASE   , config_name=_SCREAMING_SNAKE_CASE   )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE      	=		{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		["""LxmertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

