"""simple docstring"""


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 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  return 1.0 / (1.0 + np.exp(-_outputs						))






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					np.max(_outputs      ,axis=-1      ,keepdims=SCREAMING_SNAKE_CASE_						)
  _UpperCAmelCase         =					np.exp(_outputs - maxes						)
  return shifted_exp / shifted_exp.sum(axis=-1      ,keepdims=SCREAMING_SNAKE_CASE_						)


class 				a (						__snake_case      ):
    _snake_case   :     Optional[int]   							=							"""sigmoid"""
    _snake_case   :     List[Any]   							=							"""softmax"""
    _snake_case   :     Optional[Any]   							=							"""none"""



@add_end_docstrings(
    __snake_case		,							R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    '		,							)
class 				a (						__snake_case      ):
    _snake_case   :     Dict   							=							False
    _snake_case   :     List[str]   							=							ClassificationFunction.NONE


    def __init__(    self		:     Optional[Any]      , **__lowerCAmelCase		:     List[Any]    ):
      super().__init__(**__lowerCAmelCase    )

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


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     List[str]=None      , __lowerCAmelCase		:     Union[str, Any]=None      , __lowerCAmelCase		:     List[str]=""      , **__lowerCAmelCase		:     int    ):
      # Using "" as default argument because we're going to use `top_k=None` in user code to declare
      # "No top_k"
      _UpperCAmelCase         =					tokenizer_kwargs

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

      if isinstance(__lowerCAmelCase      , __lowerCAmelCase    ) or top_k is None:
        _UpperCAmelCase         =					top_k
        _UpperCAmelCase         =					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:
          _UpperCAmelCase         =					None
        else:
          _UpperCAmelCase         =					1

      if isinstance(__lowerCAmelCase      , __lowerCAmelCase    ):
        _UpperCAmelCase         =					ClassificationFunction[function_to_apply.upper()]

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


    def __call__(    self		:     str      , *__lowerCAmelCase		:     Optional[Any]      , **__lowerCAmelCase		:     Tuple    ):
      _UpperCAmelCase         =					super().__call__(*__lowerCAmelCase      , **__lowerCAmelCase    )
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      _UpperCAmelCase         =					'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    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     Optional[int]      , **__lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     Dict    ):
      return self.model(**__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     List[str]=None      , __lowerCAmelCase		:     str=1      , __lowerCAmelCase		:     Optional[int]=True    ):
      # `_legacy` is used to determine if we're running the naked pipeline and in backward
      # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
      # the more natural result containing the list.
      # Default value before `set_parameters`
      if function_to_apply is None:
        if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
          _UpperCAmelCase         =					ClassificationFunction.SIGMOID
        elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
          _UpperCAmelCase         =					ClassificationFunction.SOFTMAX
        elif hasattr(self.model.config      , """function_to_apply"""    ) and function_to_apply is None:
          _UpperCAmelCase         =					self.model.config.function_to_apply
        else:
          _UpperCAmelCase         =					ClassificationFunction.NONE

      _UpperCAmelCase         =					model_outputs['logits'][0]
      _UpperCAmelCase         =					outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
        _UpperCAmelCase         =					sigmoid(__lowerCAmelCase    )
      elif function_to_apply == ClassificationFunction.SOFTMAX:
        _UpperCAmelCase         =					softmax(__lowerCAmelCase    )
      elif function_to_apply == ClassificationFunction.NONE:
        _UpperCAmelCase         =					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()}

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



import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

