

from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase__  ):


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if isinstance(__A      ,    __A  ):
         _lowercase      :				str      					=       [label.strip() for label in labels.split(","  ) if label.strip()]
      return labels






   def __call__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      if len(__A  ) == 0 or len(__A  ) == 0:
         raise ValueError("You must include at least one label and at least one sequence."  )
      if hypothesis_template.format(labels[0]  ) == hypothesis_template:
         raise ValueError(
             (
                 "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
                 "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
             ).format(__A  )  )

      if isinstance(__A      ,    __A  ):
         _lowercase      :				int      					=       [sequences]

      _lowercase      :				Dict      					=       []
      for sequence in sequences:
         sequence_pairs.extend([[sequence, hypothesis_template.format(__A  )] for label in labels]  )

      return sequence_pairs, sequences




@add_end_docstrings(lowerCamelCase__  )
class        a__  (							lowerCamelCase__  ):


   def __init__(			self      ,    _UpperCamelCase=ZeroShotClassificationArgumentHandler()      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       args_parser
      super().__init__(*__A      ,    **__A  )
      if self.entailment_id == -1:
         logger.warning(
             "Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to "
             "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for label, ind in self.model.config.labelaid.items():
         if label.lower().startswith("entail"  ):
            return ind
      return -1


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=TruncationStrategy.ONLY_FIRST      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.framework
      if self.tokenizer.pad_token is None:
         # Override for tokenizers not supporting padding
         logger.error(
             "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
             " `pad_token=eos_token`"  )
         _lowercase      :				Optional[int]      					=       self.tokenizer.eos_token
      try:
         _lowercase      :				int      					=       self.tokenizer(
             __A      ,    add_special_tokens=__A      ,    return_tensors=__A      ,    padding=__A      ,    truncation=__A      ,    )
      except Exception as e:
         if "too short" in str(__A  ):
            # tokenizers might yell that we want to truncate
            # to a value that is not even reached by the input.
            # In that case we don't want to truncate.
            # It seems there's not a really better way to catch that
            # exception.

            _lowercase      :				Dict      					=       self.tokenizer(
                __A      ,    add_special_tokens=__A      ,    return_tensors=__A      ,    padding=__A      ,    truncation=TruncationStrategy.DO_NOT_TRUNCATE      ,    )
         else:
            raise e

      return inputs


   def 						_lowerCamelCase       (			self      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if kwargs.get("multi_class"      ,    __A  ) is not None:
         _lowercase      :				Any      					=       kwargs['''multi_class''']
         logger.warning(
             "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
             "`multi_class` will be removed in a future version of Transformers."  )
      _lowercase      :				Tuple      					=       {}
      if "candidate_labels" in kwargs:
         _lowercase      :				List[str]      					=       self._args_parser._parse_labels(kwargs["candidate_labels"]  )
      if "hypothesis_template" in kwargs:
         _lowercase      :				int      					=       kwargs['''hypothesis_template''']

      _lowercase      :				int      					=       {}
      if "multi_label" in kwargs:
         _lowercase      :				List[str]      					=       kwargs['''multi_label''']
      return preprocess_params, {}, postprocess_params


   def __call__(			self      ,    _UpperCamelCase      ,    *_UpperCamelCase      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if len(__A  ) == 0:
         pass
      elif len(__A  ) == 1 and "candidate_labels" not in kwargs:
         _lowercase      :				int      					=       args[0]
      else:
         raise ValueError(f'''Unable to understand extra arguments {args}'''  )

      return super().__call__(__A      ,    **__A  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="This example is {}."  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       self._args_parser(__A      ,    __A      ,    __A  )

      for i, (candidate_label, sequence_pair) in enumerate(zip(__A      ,    __A  )  ):
         _lowercase      :				Dict      					=       self._parse_and_tokenize([sequence_pair]  )

         yield {
             "candidate_label": candidate_label,
             "sequence": sequences[0],
             "is_last": i == len(__A  ) - 1,
             **model_input,
         }


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				str      					=       inputs['''candidate_label''']
      _lowercase      :				List[Any]      					=       inputs['''sequence''']
      _lowercase      :				int      					=       {k: inputs[k] for k in self.tokenizer.model_input_names}
      _lowercase      :				List[Any]      					=       self.model(**__A  )

      _lowercase      :				List[str]      					=       {
          '''candidate_label''': candidate_label,
          '''sequence''': sequence,
          '''is_last''': inputs['''is_last'''],
          **outputs,
      }
      return model_outputs






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=False  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       [outputs['''candidate_label'''] for outputs in model_outputs]
      _lowercase      :				Union[str, Any]      					=       [outputs['''sequence'''] for outputs in model_outputs]
      _lowercase      :				Any      					=       np.concatenate([output["logits"].numpy() for output in model_outputs]  )
      _lowercase      :				List[Any]      					=       logits.shape[0]
      _lowercase      :				Union[str, Any]      					=       len(__A  )
      _lowercase      :				str      					=       N // n
      _lowercase      :				Dict      					=       logits.reshape((num_sequences, n, -1)  )

      if multi_label or len(__A  ) == 1:
         # softmax over the entailment vs. contradiction dim for each label independently
         _lowercase      :				Union[str, Any]      					=       self.entailment_id
         _lowercase      :				List[str]      					=       -1 if entailment_id == 0 else 0
         _lowercase      :				Tuple      					=       reshaped_outputs[..., [contradiction_id, entailment_id]]
         _lowercase      :				Dict      					=       np.exp(__A  ) / np.exp(__A  ).sum(-1      ,    keepdims=__A  )
         _lowercase      :				Dict      					=       scores[..., 1]
      else:
         # softmax the "entailment" logits over all candidate labels
         _lowercase      :				List[str]      					=       reshaped_outputs[..., self.entailment_id]
         _lowercase      :				str      					=       np.exp(__A  ) / np.exp(__A  ).sum(-1      ,    keepdims=__A  )

      _lowercase      :				Any      					=       list(reversed(scores[0].argsort()  )  )
      return {
          "sequence": sequences[0],
          "labels": [candidate_labels[i] for i in top_inds],
          "scores": scores[0, top_inds].tolist(),
      }









'''simple docstring'''
from math import pi, sqrt, tan







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("surface_area_cube() only accepts non-negative values"					)
   return 6 * side_length**2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if length < 0 or breadth < 0 or height < 0:
      raise ValueError("surface_area_cuboid() only accepts non-negative values"					)
   return 2 * ((length * breadth) + (breadth * height) + (length * height))







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_sphere() only accepts non-negative values"					)
   return 4 * pi * radius**2







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_hemisphere() only accepts non-negative values"					)
   return 3 * pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cone() only accepts non-negative values"					)
   return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          "surface_area_conical_frustum() only accepts non-negative values"					)
   _lowercase      :				Union[str, Any]      					=       (height**2 + (radius_a - radius_a) ** 2) ** 0.5
   return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cylinder() only accepts non-negative values"					)
   return 2 * pi * radius * (height + radius)







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if torus_radius < 0 or tube_radius < 0:
      raise ValueError("surface_area_torus() only accepts non-negative values"					)
   if torus_radius < tube_radius:
      raise ValueError(
          "surface_area_torus() does not support spindle or self intersecting tori"					)
   return 4 * pow(snake_case		,							2					) * torus_radius * tube_radius







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if length < 0 or width < 0:
      raise ValueError("area_rectangle() only accepts non-negative values"					)
   return length * width







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("area_square() only accepts non-negative values"					)
   return side_length**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_triangle() only accepts non-negative values"					)
   return (base * height) / 2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if sidea < 0 or sidea < 0 or sidea < 0:
      raise ValueError("area_triangle_three_sides() only accepts non-negative values"					)
   elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
      raise ValueError("Given three sides do not form a triangle"					)
   _lowercase      :				Any      					=       (sidea + sidea + sidea) / 2
   _lowercase      :				List[str]      					=       sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)					)
   return area







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_parallelogram() only accepts non-negative values"					)
   return base * height







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if basea < 0 or basea < 0 or height < 0:
      raise ValueError("area_trapezium() only accepts non-negative values"					)
   return 1 / 2 * (basea + basea) * height







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("area_circle() only accepts non-negative values"					)
   return pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius_x < 0 or radius_y < 0:
      raise ValueError("area_ellipse() only accepts non-negative values"					)
   return pi * radius_x * radius_y







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if diagonal_a < 0 or diagonal_a < 0:
      raise ValueError("area_rhombus() only accepts non-negative values"					)
   return 1 / 2 * diagonal_a * diagonal_a







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if not isinstance(snake_case		,							snake_case					) or sides < 3:
      raise ValueError(
          "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"					)
   elif length < 0:
      raise ValueError(
          "area_reg_polygon() only accepts non-negative values as \
length of a side"					)
   return (sides * length**2) / (4 * tan(pi / sides					))
   return (sides * length**2) / (4 * tan(pi / sides					))


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

    print('[DEMO] Areas of various geometric shapes: \n')
    print(F'''Rectangle: {area_rectangle(10, 20) = }''')
    print(F'''Square: {area_square(10) = }''')
    print(F'''Triangle: {area_triangle(10, 10) = }''')
    print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
    print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
    print(F'''Rhombus: {area_rhombus(10, 20) = }''')
    print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
    print(F'''Circle: {area_circle(20) = }''')
    print(F'''Ellipse: {area_ellipse(10, 20) = }''')
    print('\nSurface Areas of various geometric shapes: \n')
    print(F'''Cube: {surface_area_cube(20) = }''')
    print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
    print(F'''Sphere: {surface_area_sphere(20) = }''')
    print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
    print(F'''Cone: {surface_area_cone(10, 20) = }''')
    print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
    print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
    print(F'''Torus: {surface_area_torus(20, 10) = }''')
    print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
    print(F'''Square: {area_reg_polygon(4, 10) = }''')
    print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')






