





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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   int:
 return 1.0 / (1.0 + np.exp(-_outputs   ))






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   )      ->   str:
 __lowerCAmelCase					:	Any      =       np.max(_outputs   ,   axis=-1   ,   keepdims=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       np.exp(_outputs - maxes   )
 return shifted_exp / shifted_exp.sum(axis=-1   ,   keepdims=SCREAMING_SNAKE_CASE   )






class        snake_case_    (		__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        snake_case_    (		__lowercase						):
  A_      							=   False
  A_      							=   ClassificationFunction.NONE






  def __init__(							self			:					Union[str, Any]       ,  **_snake_case			:					Any      )->Dict:






   '''simple docstring'''

   super().__init__(**_snake_case      )

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






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Any=None       ,  _snake_case			:					str=""       ,  **_snake_case			:					Any      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       tokenizer_kwargs

   __lowerCAmelCase					:	Any      =       {}
   if hasattr(self.model.config       ,  """return_all_scores"""      ) and return_all_scores is None:
    __lowerCAmelCase					:	List[Any]      =       self.model.config.return_all_scores

   if isinstance(_snake_case       ,  _snake_case      ) or top_k is None:
    __lowerCAmelCase					:	List[str]      =       top_k
    __lowerCAmelCase					:	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`."""       ,  _snake_case       ,  )
    if return_all_scores:
     __lowerCAmelCase					:	Tuple      =       None
    else:
     __lowerCAmelCase					:	Union[str, Any]      =       1

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

   if function_to_apply is not None:
    __lowerCAmelCase					:	Optional[Any]      =       function_to_apply
   return preprocess_params, {}, postprocess_params






  def __call__(							self			:					Any       ,  *_snake_case			:					Dict       ,  **_snake_case			:					Union[str, Any]      )->int:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					Optional[int]       ,  **_snake_case			:					Optional[Any]      )->Dict[str, GenericTensor]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self.framework
   if isinstance(_snake_case       ,  _snake_case      ):
    return self.tokenizer(**_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )
   elif isinstance(_snake_case       ,  _snake_case      ) and len(_snake_case      ) == 1 and isinstance(inputs[0]       ,  _snake_case      ) 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=_snake_case       ,  **_snake_case      )
   elif isinstance(_snake_case       ,  _snake_case      ):
    # 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(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					str      )->Optional[int]:






   '''simple docstring'''

   return self.model(**_snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Dict       ,  _snake_case			:					Any=None       ,  _snake_case			:					List[Any]=1       ,  _snake_case			:					Optional[int]=True      )->Tuple:






   '''simple docstring'''

   if function_to_apply is None:
    if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
     __lowerCAmelCase					:	Dict      =       ClassificationFunction.SIGMOID
    elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
     __lowerCAmelCase					:	Optional[int]      =       ClassificationFunction.SOFTMAX
    elif hasattr(self.model.config       ,  """function_to_apply"""      ) and function_to_apply is None:
     __lowerCAmelCase					:	Tuple      =       self.model.config.function_to_apply
    else:
     __lowerCAmelCase					:	int      =       ClassificationFunction.NONE

   __lowerCAmelCase					:	Tuple      =       model_outputs["""logits"""][0]
   __lowerCAmelCase					:	Optional[Any]      =       outputs.numpy()

   if function_to_apply == ClassificationFunction.SIGMOID:
    __lowerCAmelCase					:	str      =       sigmoid(_snake_case      )
   elif function_to_apply == ClassificationFunction.SOFTMAX:
    __lowerCAmelCase					:	Dict      =       softmax(_snake_case      )
   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					:	List[str]      =       [
       {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(_snake_case      )
   ]
   if not _legacy:
    dict_scores.sort(key=lambda _snake_case      : x["score"]       ,  reverse=_snake_case      )
    if top_k is not None:
     __lowerCAmelCase					:	int      =       dict_scores[:top_k]
   return dict_scores






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()