

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   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      return 1.0 / (1.0 + np.exp(-_outputs   ))







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		np.max(_outputs		,				axis=-1		,				keepdims=SCREAMING_SNAKE_CASE   )
      A_							       =		np.exp(_outputs - maxes   )
      return shifted_exp / shifted_exp.sum(axis=-1		,				keepdims=SCREAMING_SNAKE_CASE   )

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Dict  					=    'sigmoid'
   _lowercase     :       Any  					=    'softmax'
   _lowercase     :       Union[str, Any]  					=    'none'



@add_end_docstrings(
    __lowerCamelCase,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    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       List[Any]  					=    False
   _lowercase     :       Any  					=    ClassificationFunction.NONE

   def __init__(				self	:    Any			,						**lowerCamelCase__	:    str       ) ->			str:
         """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	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]=None			,						lowerCamelCase__	:    Union[str, Any]=None			,						lowerCamelCase__	:    Union[str, Any]=""			,						**lowerCamelCase__	:    List[Any]       ) ->			str:
         """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	:    str			,						*lowerCamelCase__	:    Tuple			,						**lowerCamelCase__	:    Any       ) ->			Optional[int]:
         """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	:    Any			,						lowerCamelCase__	:    Optional[Any]			,						**lowerCamelCase__	:    Optional[Any]       ) ->			Dict[str, GenericTensor]:
         """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	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         return self.model(**lowerCamelCase__       )





   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Dict=None			,						lowerCamelCase__	:    Dict=1			,						lowerCamelCase__	:    Any=True       ) ->			List[Any]:
         """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 lowerCamelCase__       : x["score"]			,						reverse=lowerCamelCase__       )
               if top_k is not None:
                     A_							       =		dict_scores[:top_k]
         return dict_scores








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
      print(f'{solution() = }')





