





'''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      )						->					Optional[int]:

   '''simple docstring'''







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

def 						__magic_name__       (							__UpperCAmelCase      )						->					Any:

   '''simple docstring'''







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







class __a (   _snake_case			):
   __UpperCamelCase   : Any	      =	'sigmoid'
   __UpperCamelCase   : Tuple	      =	'softmax'
   __UpperCamelCase   : 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			):
   __UpperCamelCase   : Optional[int]	      =	False
   __UpperCamelCase   : List[str]	      =	ClassificationFunction.NONE





   def __init__(					self				:			str				,**lowerCamelCase				:			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				:			List[Any]=None				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Any=""				,**lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       tokenizer_kwargs

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

      if isinstance(lowerCamelCase				,lowerCamelCase    ) or top_k is None:
         __SCREAMING_SNAKE_CASE      							=       top_k
         __SCREAMING_SNAKE_CASE      							=       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:
            __SCREAMING_SNAKE_CASE      							=       None
         else:
            __SCREAMING_SNAKE_CASE      							=       1

      if isinstance(lowerCamelCase				,lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       ClassificationFunction[function_to_apply.upper()]

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





   def __call__(					self				:			List[str]				,*lowerCamelCase				:			Union[str, Any]				,**lowerCamelCase				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       super().__call__(*lowerCamelCase				,**lowerCamelCase    )
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      __SCREAMING_SNAKE_CASE      							=       """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				:			Union[str, Any]				,lowerCamelCase				:			str				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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				:			List[Any]				,lowerCamelCase				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.model(**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Dict				,lowerCamelCase				:			List[str]=None				,lowerCamelCase				:			str=1				,lowerCamelCase				:			str=True    ):

      '''simple docstring'''

      if function_to_apply is None:
         if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
            __SCREAMING_SNAKE_CASE      							=       ClassificationFunction.SIGMOID
         elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
            __SCREAMING_SNAKE_CASE      							=       ClassificationFunction.SOFTMAX
         elif hasattr(self.model.config				,"""function_to_apply"""    ) and function_to_apply is None:
            __SCREAMING_SNAKE_CASE      							=       self.model.config.function_to_apply
         else:
            __SCREAMING_SNAKE_CASE      							=       ClassificationFunction.NONE

      __SCREAMING_SNAKE_CASE      							=       model_outputs["""logits"""][0]
      __SCREAMING_SNAKE_CASE      							=       outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
         __SCREAMING_SNAKE_CASE      							=       sigmoid(lowerCamelCase    )
      elif function_to_apply == ClassificationFunction.SOFTMAX:
         __SCREAMING_SNAKE_CASE      							=       softmax(lowerCamelCase    )
      elif function_to_apply == ClassificationFunction.NONE:
         __SCREAMING_SNAKE_CASE      							=       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()}

      __SCREAMING_SNAKE_CASE      							=       [
          {"""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:
            __SCREAMING_SNAKE_CASE      							=       dict_scores[:top_k]
      return dict_scores












'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable

a        =						list[list[float | int]]

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Matrix:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       [[0 for _ in range(size + 1      )] for _ in range(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for row in range(__UpperCAmelCase      ):
      for col in range(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       matrix[row][col]

      __SCREAMING_SNAKE_CASE      							=       vector[row][0]

   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       0
   while row < size and col < size:
      # pivoting
      __SCREAMING_SNAKE_CASE      							=       max((abs(augmented[rowa][col]      ), rowa) for rowa in range(__UpperCAmelCase							,	__UpperCAmelCase      )      )[
          1
      ]
      if augmented[pivot_row][col] == 0:
         col += 1
         continue
      else:
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       augmented[pivot_row], augmented[row]

      for rowa in range(row + 1							,	__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       augmented[rowa][col] / augmented[row][col]
         __SCREAMING_SNAKE_CASE      							=       0
         for cola in range(col + 1							,	size + 1      ):
            augmented[rowa][cola] -= augmented[row][cola] * ratio

      row += 1
      col += 1

   # back substitution
   for col in range(1							,	__UpperCAmelCase      ):
      for row in range(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       augmented[row][col] / augmented[col][col]
         for cola in range(__UpperCAmelCase							,	size + 1      ):
            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
   return [
       [round(augmented[row][size] / augmented[row][row]							,	10      )] for row in range(__UpperCAmelCase      )
   ]

def 						__magic_name__       (							__UpperCAmelCase      )						->					Callable[[int], int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       [[0 for _ in range(__UpperCAmelCase      )] for _ in range(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       [[0] for _ in range(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for x_val, y_val in enumerate(__UpperCAmelCase      ):
      for col in range(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       (x_val + 1) ** (size - col - 1)
      __SCREAMING_SNAKE_CASE      							=       y_val

   __SCREAMING_SNAKE_CASE      							=       solve(__UpperCAmelCase							,	__UpperCAmelCase      )

   def interpolated_func(__UpperCAmelCase      ) -> int:
      return sum(
          round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
          for x_val in range(__UpperCAmelCase      )      )

   return interpolated_func

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return (
       1
       - variable
       + variable**2
       - variable**3
       + variable**4
       - variable**5
       + variable**6
       - variable**7
       + variable**8
       - variable**9
       + variable**10
   )

def 						__magic_name__       (							__UpperCAmelCase = question_function							,	__UpperCAmelCase = 10      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       [func(__UpperCAmelCase      ) for x_val in range(1							,	order + 1      )]

   __SCREAMING_SNAKE_CASE      							=       [
       interpolate(data_points[:max_coeff]      ) for max_coeff in range(1							,	order + 1      )
   ]

   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for poly in polynomials:
      __SCREAMING_SNAKE_CASE      							=       1
      while func(__UpperCAmelCase      ) == poly(__UpperCAmelCase      ):
         x_val += 1

      ret += poly(__UpperCAmelCase      )

   return ret


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





