





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


_A :   List[Any]								=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			lowerCamelCase__ ):







       def 						__lowerCamelCase   (				self					:     str		, A					:     Optional[int]      )       ->List[str]:
          if isinstance(__A		, __A      ):
             lowerCamelCase__       :						str        =							[label.strip() for label in labels.split(''','''      ) if label.strip()]
          return labels







       def __call__(				self					:     Any		, A					:     Any		, A					:     Tuple		, A					:     Dict      )       ->int:
          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      ):
             lowerCamelCase__       :						int        =							[sequences]

          lowerCamelCase__       :						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        __SCREAMING_SNAKE_CASE      (			lowerCamelCase__ ):







       def __init__(				self					:     Union[str, Any]		, A					:     List[str]=ZeroShotClassificationArgumentHandler()		, *A					:     Optional[int]		, **A					:     Optional[Any]      )       ->int:
          lowerCamelCase__       :						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					:     Union[str, Any]      )       ->Tuple:
          for label, ind in self.model.config.labelaid.items():
             if label.lower().startswith('''entail'''      ):
                return ind
          return -1







       def 						__lowerCamelCase   (				self					:     str		, A					:     int		, A					:     List[Any]=True		, A					:     Any=True		, A					:     List[str]=TruncationStrategy.ONLY_FIRST		, **A					:     List[Any]      )       ->int:
          lowerCamelCase__       :						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`'''      )
             lowerCamelCase__       :						Optional[int]        =							self.tokenizer.eos_token
          try:
             lowerCamelCase__       :						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.

                lowerCamelCase__       :						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					:     str		, **A					:     int      )       ->List[str]:
          if kwargs.get('''multi_class'''		, __A      ) is not None:
             lowerCamelCase__       :						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.'''      )
          lowerCamelCase__       :						Tuple        =							{}
          if "candidate_labels" in kwargs:
             lowerCamelCase__       :						List[str]        =							self._args_parser._parse_labels(kwargs['''candidate_labels''']      )
          if "hypothesis_template" in kwargs:
             lowerCamelCase__       :						int        =							kwargs['''hypothesis_template''']

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







       def __call__(				self					:     List[Any]		, A					:     Union[str, List[str]]		, *A					:     Tuple		, **A					:     Union[str, Any]		, )       ->List[Any]:
          if len(__A      ) == 0:
             pass
          elif len(__A      ) == 1 and "candidate_labels" not in kwargs:
             lowerCamelCase__       :						int        =							args[0]
          else:
             raise ValueError(F"Unable to understand extra arguments {args}"      )

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







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Tuple		, A					:     Optional[Any]=None		, A					:     List[str]="This example is {}."      )       ->Dict:
          lowerCamelCase__       :						List[Any]        =							self._args_parser(__A		, __A		, __A      )

          for i, (candidate_label, sequence_pair) in enumerate(zip(__A		, __A      )      ):
             lowerCamelCase__       :						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					:     Dict		, A					:     Dict      )       ->List[str]:
          lowerCamelCase__       :						str        =							inputs['''candidate_label''']
          lowerCamelCase__       :						List[Any]        =							inputs['''sequence''']
          lowerCamelCase__       :						int        =							{k: inputs[k] for k in self.tokenizer.model_input_names}
          lowerCamelCase__       :						List[Any]        =							self.model(**__A      )

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







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Optional[Any]		, A					:     int=False      )       ->List[str]:
          lowerCamelCase__       :						Union[str, Any]        =							[outputs['''candidate_label'''] for outputs in model_outputs]
          lowerCamelCase__       :						Union[str, Any]        =							[outputs['''sequence'''] for outputs in model_outputs]
          lowerCamelCase__       :						Any        =							np.concatenate([output['''logits'''].numpy() for output in model_outputs]      )
          lowerCamelCase__       :						List[Any]        =							logits.shape[0]
          lowerCamelCase__       :						Union[str, Any]        =							len(__A      )
          lowerCamelCase__       :						str        =							N // n
          lowerCamelCase__       :						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
             lowerCamelCase__       :						Union[str, Any]        =							self.entailment_id
             lowerCamelCase__       :						List[str]        =							-1 if entailment_id == 0 else 0
             lowerCamelCase__       :						Tuple        =							reshaped_outputs[..., [contradiction_id, entailment_id]]
             lowerCamelCase__       :						Dict        =							np.exp(__A      ) / np.exp(__A      ).sum(-1		, keepdims=__A      )
             lowerCamelCase__       :						Dict        =							scores[..., 1]
          else:
             # softmax the "entailment" logits over all candidate labels
             lowerCamelCase__       :						List[str]        =							reshaped_outputs[..., self.entailment_id]
             lowerCamelCase__       :						str        =							np.exp(__A      ) / np.exp(__A      ).sum(-1		, keepdims=__A      )

          lowerCamelCase__       :						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(),
          }










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



