import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    """kwargs, expected"""			,					[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10     )]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(__UpperCamelCase			,					i + 1     ) for i in range(10     )]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1     )]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0			,					4     ), range(4			,					7     ), range(7			,					10     )]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0			,					1     ), range(1			,					2     ), range(2			,					3     )]),
    ]			,					)
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	List[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				_distribute_shards(**__UpperCamelCase     )
  assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""			,					[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]			,					)
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	int     )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				_split_gen_kwargs(__UpperCamelCase			,					__UpperCamelCase     )
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected"""			,					[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]			,					)
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	List[Any]     )							-> Optional[int]:







  """simple docstring"""





  if expected is RuntimeError:
    with pytest.raises(__UpperCamelCase     ):
      _number_of_shards_in_gen_kwargs(__UpperCamelCase     )
  else:
    SCREAMING_SNAKE_CASE__      							=				_number_of_shards_in_gen_kwargs(__UpperCamelCase     )
    assert out == expected

from typing import TYPE_CHECKING

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


__lowerCamelCase      :			Optional[Any]							       =		{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Dict							       =		['''EfficientFormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFEfficientFormerForImageClassification''',
       '''TFEfficientFormerForImageClassificationWithTeacher''',
       '''TFEfficientFormerModel''',
       '''TFEfficientFormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			List[Any]							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
