




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      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  return 1.0 / (1.0 + np.exp(-_outputs      ))



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  Union[str, Any]		       =     np.max(_outputs	,      axis=-1	,      keepdims=UpperCamelCase      )
  lowerCAmelCase__				:  Union[str, Any]		       =     np.exp(_outputs - maxes      )
  return shifted_exp / shifted_exp.sum(axis=-1	,      keepdims=UpperCamelCase      )






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		'sigmoid'
      A__		     =		'softmax'
      A__		     =		'none'






@add_end_docstrings(
    _lowercase    ,			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  _lowerCAmelCase     (  _lowercase	):
      A__		     =		False
      A__		     =		ClassificationFunction.NONE

      def __init__(   self      ,				**__UpperCAmelCase		):
        super().__init__(**__UpperCAmelCase		)

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=""      ,				**__UpperCAmelCase		):
        # Using "" as default argument because we're going to use `top_k=None` in user code to declare
        # "No top_k"
        lowerCAmelCase__				:  Optional[int]		       =     tokenizer_kwargs

        lowerCAmelCase__				:  Any		       =     {}
        if hasattr(self.model.config      ,				'''return_all_scores'''		) and return_all_scores is None:
          lowerCAmelCase__				:  Optional[int]		       =     self.model.config.return_all_scores

        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		) or top_k is None:
          lowerCAmelCase__				:  Any		       =     top_k
          lowerCAmelCase__				:  List[str]		       =     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`.'''      ,				__UpperCAmelCase      ,				)
          if return_all_scores:
            lowerCAmelCase__				:  List[Any]		       =     None
          else:
            lowerCAmelCase__				:  int		       =     1

        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     ClassificationFunction[function_to_apply.upper()]

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

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     self.framework
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          return self.tokenizer(**__UpperCAmelCase      ,				return_tensors=__UpperCAmelCase      ,				**__UpperCAmelCase		)
        elif isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		) and len(__UpperCAmelCase		) == 1 and isinstance(inputs[0]      ,				__UpperCAmelCase		) 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=__UpperCAmelCase      ,				**__UpperCAmelCase		)
        elif isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          # 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(__UpperCAmelCase      ,				return_tensors=__UpperCAmelCase      ,				**__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return self.model(**__UpperCAmelCase		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=1      ,				__UpperCAmelCase=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:
            lowerCAmelCase__				:  List[str]		       =     ClassificationFunction.SIGMOID
          elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
            lowerCAmelCase__				:  List[str]		       =     ClassificationFunction.SOFTMAX
          elif hasattr(self.model.config      ,				'''function_to_apply'''		) and function_to_apply is None:
            lowerCAmelCase__				:  str		       =     self.model.config.function_to_apply
          else:
            lowerCAmelCase__				:  Tuple		       =     ClassificationFunction.NONE

        lowerCAmelCase__				:  List[Any]		       =     model_outputs['''logits'''][0]
        lowerCAmelCase__				:  Optional[Any]		       =     outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
          lowerCAmelCase__				:  Tuple		       =     sigmoid(__UpperCAmelCase		)
        elif function_to_apply == ClassificationFunction.SOFTMAX:
          lowerCAmelCase__				:  Any		       =     softmax(__UpperCAmelCase		)
        elif function_to_apply == ClassificationFunction.NONE:
          lowerCAmelCase__				:  Any		       =     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()}

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







from __future__ import annotations



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[int]:  # This function is recursive
  lowerCAmelCase__				:  Tuple		       =     len(UpperCamelCase      )
  # If the array contains only one element, we return it (it's the stop condition of
  # recursion)
  if array_length <= 1:
    return array
    # Else
  lowerCAmelCase__				:  Optional[int]		       =     array[0]
  lowerCAmelCase__				:  Union[str, Any]		       =     False
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  list[int]		       =     []
  while not is_found and i < array_length:
    if array[i] < pivot:
      lowerCAmelCase__				:  List[Any]		       =     True
      lowerCAmelCase__				:  Optional[int]		       =     [element for element in array[i:] if element >= array[i]]
      lowerCAmelCase__				:  Any		       =     longest_subsequence(UpperCamelCase      )
      if len(UpperCamelCase      ) > len(UpperCamelCase      ):
        lowerCAmelCase__				:  Optional[int]		       =     temp_array
    else:
      i += 1

  lowerCAmelCase__				:  Optional[Any]		       =     [element for element in array[1:] if element >= pivot]
  lowerCAmelCase__				:  List[Any]		       =     [pivot, *longest_subsequence(UpperCamelCase      )]
  if len(UpperCamelCase      ) > len(UpperCamelCase      ):
    return temp_array
  else:
    return longest_subseq


if __name__ == "__main__":
     import doctest

     doctest.testmod()

