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,
)


lowercase_     :   Optional[Any] 				=				pytest.mark.integration







@pytest.mark.parametrize('''path'''       ,				['''paws''', '''csv''']      )
def 			A__      (	snake_case_ :		Dict       ,				snake_case_ :		str      ):
 inspect_dataset(snake_case_       ,				snake_case_      )
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      path + '''.py'''
 assert script_name in os.listdir(snake_case_      )
 assert "__pycache__" not in os.listdir(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 			A__      (	snake_case_ :		int       ,				snake_case_ :		int      ):
 inspect_metric(snake_case_       ,				snake_case_      )
 SCREAMING_SNAKE_CASE__:   List[str]=      path + '''.py'''
 assert script_name in os.listdir(snake_case_      )
 assert "__pycache__" not in os.listdir(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 			A__      (	snake_case_ :		str       ,				snake_case_ :		List[str]       ,				snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   str=      get_dataset_config_info(snake_case_       ,				config_name=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 			A__      (	snake_case_ :		Optional[Any]       ,				snake_case_ :		Optional[int]       ,				snake_case_ :		Optional[Any]      ):
 with pytest.raises(snake_case_      ):
  get_dataset_config_info(snake_case_       ,				config_name=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 			A__      (	snake_case_ :		Optional[int]       ,				snake_case_ :		str      ):
 SCREAMING_SNAKE_CASE__:   int=      get_dataset_config_names(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 			A__      (	snake_case_ :		Optional[int]       ,				snake_case_ :		Tuple       ,				snake_case_ :		List[str]      ):
 SCREAMING_SNAKE_CASE__:   List[str]=      get_dataset_infos(snake_case_      )
 assert list(infos.keys()      ) == expected_configs
 SCREAMING_SNAKE_CASE__:   int=      expected_configs[0]
 assert expected_config in infos
 SCREAMING_SNAKE_CASE__:   Optional[int]=      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 			A__      (	snake_case_ :		Any       ,				snake_case_ :		Optional[int]       ,				snake_case_ :		List[str]      ):
 SCREAMING_SNAKE_CASE__:   Tuple=      get_dataset_infos(snake_case_      )
 assert expected_config in infos
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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 			A__      (	snake_case_ :		Optional[int]       ,				snake_case_ :		Optional[int]       ,				snake_case_ :		List[Any]      ):
 with pytest.raises(snake_case_      ):
  get_dataset_split_names(snake_case_       ,				config_name=snake_case_      )


from typing import TYPE_CHECKING

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


lowercase_     :   List[str] 				=				{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Optional[Any] 				=				['MaskFormerFeatureExtractor']
      lowercase_     :   Dict 				=				['MaskFormerImageProcessor']


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MaskFormerForInstanceSegmentation',
          'MaskFormerModel',
          'MaskFormerPreTrainedModel',
      ]
      lowercase_     :   Optional[Any] 				=				[
          'MaskFormerSwinBackbone',
          'MaskFormerSwinModel',
          'MaskFormerSwinPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
      from .configuration_maskformer_swin import MaskFormerSwinConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_maskformer import MaskFormerFeatureExtractor
            from .image_processing_maskformer import MaskFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_maskformer import (
                MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaskFormerForInstanceSegmentation,
                MaskFormerModel,
                MaskFormerPreTrainedModel,
            )
            from .modeling_maskformer_swin import (
                MaskFormerSwinBackbone,
                MaskFormerSwinModel,
                MaskFormerSwinPreTrainedModel,
            )


else:
      import sys

      lowercase_     :   Any 				=				_LazyModule(__name__, globals()['__file__'], _import_structure)

