

import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def      A__					(			__A  :						Dict			)					->Tuple:
      return 1.0 / (1.0 + np.exp(-_outputs			))





def      A__					(			__A  :						int			)					->Optional[Any]:
      __A    =np.max(_outputs     ,				axis=-1     ,				keepdims=snake_case__			)
      __A    =np.exp(_outputs - maxes			)
      return shifted_exp / shifted_exp.sum(axis=-1     ,				keepdims=snake_case__			)





class        lowerCAmelCase__  (					_SCREAMING_SNAKE_CASE			):



       '''simple docstring'''







       lowercase_						=    """sigmoid"""
       lowercase_						=    """softmax"""
       lowercase_						=    """none"""





@add_end_docstrings(
    _SCREAMING_SNAKE_CASE				,							r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """				,							)
class        lowerCAmelCase__  (					_SCREAMING_SNAKE_CASE			):



       '''simple docstring'''







       lowercase_						=    False
       lowercase_						=    ClassificationFunction.NONE

       def __init__(							self ,   **lowercase__     ):







             '''simple docstring'''



             super().__init__(**lowerCAmelCase_     )

             self.check_model_type(
                 TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
                 if self.framework == '''tf'''
                 else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING     )

       def     __UpperCamelCase     (							self ,   lowercase__=None ,   lowercase__=None ,   lowercase__="" ,   **lowercase__     ):







             '''simple docstring'''



             __A    =tokenizer_kwargs

             __A    ={}
             if hasattr(self.model.config ,   '''return_all_scores'''     ) and return_all_scores is None:
                   __A    =self.model.config.return_all_scores

             if isinstance(lowerCAmelCase_ ,   lowerCAmelCase_     ) or top_k is None:
                   __A    =top_k
                   __A    =False
             elif return_all_scores is not None:
                   warnings.warn(
                       '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                       ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' ,   lowerCAmelCase_ ,   )
                   if return_all_scores:
                         __A    =None
                   else:
                         __A    =1

             if isinstance(lowerCAmelCase_ ,   lowerCAmelCase_     ):
                   __A    =ClassificationFunction[function_to_apply.upper()]

             if function_to_apply is not None:
                   __A    =function_to_apply
             return preprocess_params, {}, postprocess_params

       def __call__(							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =super().__call__(*lowerCAmelCase_ ,   **lowerCAmelCase_     )
             # TODO try and retrieve it in a nicer way from _sanitize_parameters.
             __A    ="""top_k""" not in kwargs
             if isinstance(args[0] ,   lowerCAmelCase_     ) and _legacy:
                   # This pipeline is odd, and return a list when single item is run
                   return [result]
             else:
                   return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =self.framework
             if isinstance(lowerCAmelCase_ ,   lowerCAmelCase_     ):
                   return self.tokenizer(**lowerCAmelCase_ ,   return_tensors=lowerCAmelCase_ ,   **lowerCAmelCase_     )
             elif isinstance(lowerCAmelCase_ ,   lowerCAmelCase_     ) and len(lowerCAmelCase_     ) == 1 and isinstance(inputs[0] ,   lowerCAmelCase_     ) and len(inputs[0]     ) == 2:
                   # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                   return self.tokenizer(
                       text=inputs[0][0] ,   text_pair=inputs[0][1] ,   return_tensors=lowerCAmelCase_ ,   **lowerCAmelCase_     )
             elif isinstance(lowerCAmelCase_ ,   lowerCAmelCase_     ):
                   # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                   raise ValueError(
                       '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                       ''' dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.'''     )
             return self.tokenizer(lowerCAmelCase_ ,   return_tensors=lowerCAmelCase_ ,   **lowerCAmelCase_     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return self.model(**lowerCAmelCase_     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=None ,   lowercase__=1 ,   lowercase__=True     ):







             '''simple docstring'''



             if function_to_apply is None:
                   if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                         __A    =ClassificationFunction.SIGMOID
                   elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                         __A    =ClassificationFunction.SOFTMAX
                   elif hasattr(self.model.config ,   '''function_to_apply'''     ) and function_to_apply is None:
                         __A    =self.model.config.function_to_apply
                   else:
                         __A    =ClassificationFunction.NONE

             __A    =model_outputs["""logits"""][0]
             __A    =outputs.numpy()

             if function_to_apply == ClassificationFunction.SIGMOID:
                   __A    =sigmoid(lowerCAmelCase_     )
             elif function_to_apply == ClassificationFunction.SOFTMAX:
                   __A    =softmax(lowerCAmelCase_     )
             elif function_to_apply == ClassificationFunction.NONE:
                   __A    =outputs
             else:
                   raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''     )

             if top_k == 1 and _legacy:
                   return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

             __A    =[
                 {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(lowerCAmelCase_     )
             ]
             if not _legacy:
                   dict_scores.sort(key=lambda lowercase__     : x["score"] ,   reverse=lowerCAmelCase_     )
                   if top_k is not None:
                         __A    =dict_scores[:top_k]
             return dict_scores










"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase       =				{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase       =				[
							    "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

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

