



"""simple docstring"""
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:     Optional[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Optional[Any]			=			[label.strip() for label in labels.split(""","""		) if label.strip()]
    return labels




   def __call__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    if len(_SCREAMING_SNAKE_CASE		) == 0 or len(_SCREAMING_SNAKE_CASE		) == 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(_SCREAMING_SNAKE_CASE		)		)

    if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Dict			=			[sequences]

    UpperCAmelCase			:      List[Any]			=			[]
    for sequence in sequences:
     sequence_pairs.extend([[sequence, hypothesis_template.format(_SCREAMING_SNAKE_CASE		)] for label in labels]		)

    return sequence_pairs, sequences



@add_end_docstrings(UpperCAmelCase__				)
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=ZeroShotClassificationArgumentHandler()   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			args_parser
    super().__init__(*_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=TruncationStrategy.ONLY_FIRST   ,  **_SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			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`"""		)
     UpperCAmelCase			:      str			=			self.tokenizer.eos_token
    try:
     UpperCAmelCase			:      Optional[Any]			=			self.tokenizer(
         _SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  truncation=_SCREAMING_SNAKE_CASE   ,  )
    except Exception as e:
     if "too short" in str(_SCREAMING_SNAKE_CASE		):
      # 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.

      UpperCAmelCase			:      str			=			self.tokenizer(
          _SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  truncation=TruncationStrategy.DO_NOT_TRUNCATE   ,  )
     else:
      raise e

    return inputs

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    if kwargs.get("""multi_class"""   ,  _SCREAMING_SNAKE_CASE		) is not None:
     UpperCAmelCase			:      List[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."""		)
    UpperCAmelCase			:      List[Any]			=			{}
    if "candidate_labels" in kwargs:
     UpperCAmelCase			:      Tuple			=			self._args_parser._parse_labels(kwargs["""candidate_labels"""]		)
    if "hypothesis_template" in kwargs:
     UpperCAmelCase			:      Any			=			kwargs["""hypothesis_template"""]

    UpperCAmelCase			:      Tuple			=			{}
    if "multi_label" in kwargs:
     UpperCAmelCase			:      Optional[Any]			=			kwargs["""multi_label"""]
    return preprocess_params, {}, postprocess_params

   def __call__(     self   ,  _SCREAMING_SNAKE_CASE   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Dict:





    '''simple docstring'''




    if len(_SCREAMING_SNAKE_CASE		) == 0:
     pass
    elif len(_SCREAMING_SNAKE_CASE		) == 1 and "candidate_labels" not in kwargs:
     UpperCAmelCase			:      List[str]			=			args[0]
    else:
     raise ValueError(F"Unable to understand extra arguments {args}"		)

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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="This example is {}."		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      int			=			self._args_parser(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

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

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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			inputs["""candidate_label"""]
    UpperCAmelCase			:      Any			=			inputs["""sequence"""]
    UpperCAmelCase			:      List[Any]			=			{k: inputs[k] for k in self.tokenizer.model_input_names}
    UpperCAmelCase			:      Optional[Any]			=			self.model(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			{
        """candidate_label""": candidate_label,
        """sequence""": sequence,
        """is_last""": inputs["""is_last"""],
        **outputs,
    }
    return model_outputs




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=False		)						->   Dict:





    '''simple docstring'''




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

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

    UpperCAmelCase			:      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 __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:     Union[str, Any]										=	2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
A:     int										=	5_0
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
A:     Tuple										=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Tuple			=			len([g for position, g in enumerate(UpperCamelCase		) if g == main_target[position]]		)
 return (item, float(UpperCamelCase		))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			random.randint(0    ,	len(UpperCamelCase		) - 1		)
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]		):
 UpperCAmelCase			:      str			=			list(UpperCamelCase		)
 if random.uniform(0    ,	1		) < MUTATION_PROBABILITY:
  UpperCAmelCase			:      int			=			random.choice(UpperCamelCase		)
 return "".join(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	tuple[str, float]    ,	UpperCamelCase		:	list[tuple[str, float]]    ,	UpperCamelCase		:	list[str]    ,	):
 UpperCAmelCase			:      Optional[Any]			=			[]
 # Generate more children proportionally to the fitness score.
 UpperCAmelCase			:      Optional[Any]			=			int(parent_a[1] * 100		) + 1
 UpperCAmelCase			:      List[str]			=			10 if child_n >= 10 else child_n
 for _ in range(UpperCamelCase		):
  UpperCAmelCase			:      List[str]			=			population_score[random.randint(0    ,	UpperCamelCase		)][0]

  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			crossover(parent_a[0]    ,	UpperCamelCase		)
  # Append new string to the population list.
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
 return pop

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]    ,	UpperCamelCase		:	bool = True		):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  UpperCAmelCase			:      Dict			=			F"{N_POPULATION} must be bigger than {N_SELECTED}"
  raise ValueError(UpperCamelCase		)
 # Verify that the target contains no genes besides the ones inside genes variable.
 UpperCAmelCase			:      str			=			sorted({c for c in target if c not in genes}		)
 if not_in_genes_list:
  UpperCAmelCase			:      Optional[Any]			=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(UpperCamelCase		)

 # Generate random starting population.
 UpperCAmelCase			:      Optional[int]			=			[]
 for _ in range(UpperCamelCase		):
  population.append("""""".join([random.choice(UpperCamelCase		) for i in range(len(UpperCamelCase		)		)]		)		)

 # Just some logs to know what the algorithms is doing.
 UpperCAmelCase			,	UpperCAmelCase			:      Any			=			0, 0

 # This loop will end when we find a perfect match for our target.
 while True:
  generation += 1
  total_population += len(UpperCamelCase		)

  # Random population created. Now it's time to evaluate.

  # Adding a bit of concurrency can make everything faster,
  #
  # import concurrent.futures
  # population_score: list[tuple[str, float]] = []
  # with concurrent.futures.ThreadPoolExecutor(
  #                                   max_workers=NUM_WORKERS) as executor:
  #     futures = {executor.submit(evaluate, item) for item in population}
  #     concurrent.futures.wait(futures)
  #     population_score = [item.result() for item in futures]
  #
  # but with a simple algorithm like this, it will probably be slower.
  # We just need to call evaluate for every item inside the population.
  UpperCAmelCase			:      str			=			[evaluate(UpperCamelCase    ,	UpperCamelCase		) for item in population]

  # Check if there is a matching evolution.
  UpperCAmelCase			:      Union[str, Any]			=			sorted(UpperCamelCase    ,	key=lambda UpperCamelCase		: x[1]    ,	reverse=UpperCamelCase		)
  if population_score[0][0] == target:
   return (generation, total_population, population_score[0][0])

  # Print the best result every 10 generation.
  # Just to know that the algorithm is working.
  if debug and generation % 10 == 0:
   print(
       F"\nGeneration: {generation}"
       F"\nTotal Population:{total_population}"
       F"\nBest score: {population_score[0][1]}"
       F"\nBest string: {population_score[0][0]}"		)

  # Flush the old population, keeping some of the best evolutions.
  # Keeping this avoid regression of evolution.
  UpperCAmelCase			:      Tuple			=			population[: int(N_POPULATION / 3		)]
  population.clear()
  population.extend(UpperCamelCase		)
  # Normalize population score to be between 0 and 1.
  UpperCAmelCase			:      List[str]			=			[
      (item, score / len(UpperCamelCase		)) for item, score in population_score
  ]

  # This is selection
  for i in range(UpperCamelCase		):
   population.extend(select(population_score[int(UpperCamelCase		)]    ,	UpperCamelCase    ,	UpperCamelCase		)		)
   # Check if the population has already reached the maximum value and if so,
   # break the cycle.  If this check is disabled, the algorithm will take
   # forever to compute large strings, but will also calculate small strings in
   # a far fewer generations.
   if len(UpperCamelCase		) > N_POPULATION:
    break


if __name__ == "__main__":
      A:     Union[str, Any]										=	(
          "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
      )
      A:     Dict										=	list(
          " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
          "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
      )
      A							,   A							,   A:     List[Any]										=	basic(target_str, genes_list)
      print(
          f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
      )
