'''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       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      return 1.0 / (1.0 + np.exp(-_outputs		))
def       __magic_name__(  __UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

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



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  False
     A_										=  ClassificationFunction.NONE



     def __init__(					self				,				**A_						) ->				str:






           """simple docstring"""
           super().__init__(**A_						)

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



     def 		UpperCamelCase_						(					self				,				A_=None				,				A_=None				,				A_=""				,				**A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				tokenizer_kwargs

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

           if isinstance(A_				,				A_						) or top_k is None:
                 _lowerCamelCase			    =				top_k
                 _lowerCamelCase			    =				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`.'''				,				A_				,				)
                 if return_all_scores:
                       _lowerCamelCase			    =				None
                 else:
                       _lowerCamelCase			    =				1

           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				ClassificationFunction[function_to_apply.upper()]

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



     def __call__(					self				,				*A_				,				**A_						) ->				Optional[Any]:






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



     def 		UpperCamelCase_						(					self				,				A_				,				**A_						) ->				Dict[str, GenericTensor]:






           """simple docstring"""
           _lowerCamelCase			    =				self.framework
           if isinstance(A_				,				A_						):
                 return self.tokenizer(**A_				,				return_tensors=A_				,				**A_						)
           elif isinstance(A_				,				A_						) and len(A_						) == 1 and isinstance(inputs[0]				,				A_						) 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=A_				,				**A_						)
           elif isinstance(A_				,				A_						):
                 # 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(A_				,				return_tensors=A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           return self.model(**A_						)





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=1				,				A_=True						) ->				List[Any]:






           """simple docstring"""
           # `_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			    =				ClassificationFunction.SIGMOID
                 elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                       _lowerCamelCase			    =				ClassificationFunction.SOFTMAX
                 elif hasattr(self.model.config				,				'''function_to_apply'''						) and function_to_apply is None:
                       _lowerCamelCase			    =				self.model.config.function_to_apply
                 else:
                       _lowerCamelCase			    =				ClassificationFunction.NONE

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

           if function_to_apply == ClassificationFunction.SIGMOID:
                 _lowerCamelCase			    =				sigmoid(A_						)
           elif function_to_apply == ClassificationFunction.SOFTMAX:
                 _lowerCamelCase			    =				softmax(A_						)
           elif function_to_apply == ClassificationFunction.NONE:
                 _lowerCamelCase			    =				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			    =				[
               {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(A_						)
           ]
           if not _legacy:
                 dict_scores.sort(key=lambda A_						: x["score"]				,				reverse=A_						)
                 if top_k is not None:
                       _lowerCamelCase			    =				dict_scores[:top_k]
           return dict_scores
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()