

'''simple docstring'''

from typing import Dict

from .base import GenericTensor, Pipeline




class        A_ (				__lowerCamelCase							):






     '''simple docstring'''



     def    UpperCAmelCase_       (		self :     List[Any]     ,			lowercase_ :     Tuple=None     ,			lowercase_ :     Union[str, Any]=None     ,			lowercase_ :     Optional[Any]=None     ,			**lowercase_ :     str       )						->	Tuple:
      if tokenize_kwargs is None:
       UpperCAmelCase       :	List[Any]											= {}

      if truncation is not None:
       if "truncation" in tokenize_kwargs:
        raise ValueError(
            'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'       )
       UpperCAmelCase       :	Tuple											= truncation

      UpperCAmelCase       :	Any											= tokenize_kwargs

      UpperCAmelCase       :	str											= {}
      if return_tensors is not None:
       UpperCAmelCase       :	List[str]											= return_tensors

      return preprocess_params, {}, postprocess_params


     def    UpperCAmelCase_       (		self :     Union[str, Any]     ,			lowercase_ :     Any     ,			**lowercase_ :     Dict       )						->	int:
      UpperCAmelCase       :	List[str]											= self.framework
      UpperCAmelCase       :	Any											= self.tokenizer(__A     ,			return_tensors=__A     ,			**__A       )
      return model_inputs


     def    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     Tuple       )						->	List[Any]:
      UpperCAmelCase       :	Optional[int]											= self.model(**__A       )
      return model_outputs


     def    UpperCAmelCase_       (		self :     str     ,			lowercase_ :     Tuple     ,			lowercase_ :     Tuple=False       )						->	Tuple:
      # [0] is the first available tensor, logits or last_hidden_state.
      if return_tensors:
       return model_outputs[0]
      if self.framework == "pt":
       return model_outputs[0].tolist()
      elif self.framework == "tf":
       return model_outputs[0].numpy().tolist()




     def __call__(		self :     List[Any]     ,			*lowercase_ :     Dict     ,			**lowercase_ :     int       )						->	Optional[int]:
      return super().__call__(*__A     ,			**__A       )











'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
