'''simple docstring'''




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__UpperCAmelCase												=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.
__UpperCAmelCase												=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.
__UpperCAmelCase												=0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   tuple[str, float]:
    __lowerCamelCase          =  len([g for position, g in enumerate(UpperCamelCase__    ) if g == main_target[position]]    )
    return (item, float(UpperCamelCase__    ))






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   tuple[str, str]:
    __lowerCamelCase          =  random.randint(0    ,  len(UpperCamelCase__    ) - 1    )
    __lowerCamelCase          =  parent_a[:random_slice] + parent_a[random_slice:]
    __lowerCamelCase          =  parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  list(UpperCamelCase__    )
    if random.uniform(0    ,  1    ) < MUTATION_PROBABILITY:
        __lowerCamelCase          =  random.choice(UpperCamelCase__    )
    return "".join(UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  )     ->   list[str]:
    __lowerCamelCase          =  []
    # Generate more children proportionally to the fitness score.
    __lowerCamelCase          =  int(parent_a[1] * 1_00    ) + 1
    __lowerCamelCase          =  10 if child_n >= 10 else child_n
    for _ in range(UpperCamelCase__    ):
        __lowerCamelCase          =  population_score[random.randint(0    ,  UpperCamelCase__    )][0]

        __lowerCamelCase     ,  __lowerCamelCase          =  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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = True    )     ->   tuple[int, int, str]:

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        __lowerCamelCase          =  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.
    __lowerCamelCase          =  sorted({c for c in target if c not in genes}    )
    if not_in_genes_list:
        __lowerCamelCase          =  f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
        raise ValueError(UpperCamelCase__    )

    # Generate random starting population.
    __lowerCamelCase          =  []
    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.
    __lowerCamelCase     ,  __lowerCamelCase          =  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.
        __lowerCamelCase          =  [evaluate(UpperCamelCase__    ,  UpperCamelCase__    ) for item in population]

        # Check if there is a matching evolution.
        __lowerCamelCase          =  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.
        __lowerCamelCase          =  population[: int(N_POPULATION / 3    )]
        population.clear()
        population.extend(UpperCamelCase__    )
        # Normalize population score to be between 0 and 1.
        __lowerCamelCase          =  [
            (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__":
  __UpperCAmelCase												=(
      "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
  )
  __UpperCAmelCase												=list(
      " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
      "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
  )
  __UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase												=basic(target_str, genes_list)
  print(
      f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
  )

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase												={
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=["WhisperTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WhisperForConditionalGeneration",
      "WhisperModel",
      "WhisperPreTrainedModel",
      "WhisperForAudioClassification",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFWhisperForConditionalGeneration",
      "TFWhisperModel",
      "TFWhisperPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "FlaxWhisperForConditionalGeneration",
      "FlaxWhisperModel",
      "FlaxWhisperPreTrainedModel",
      "FlaxWhisperForAudioClassification",
  ]


if TYPE_CHECKING:
  from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
  from .feature_extraction_whisper import WhisperFeatureExtractor
  from .processing_whisper import WhisperProcessor
  from .tokenization_whisper import WhisperTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_whisper_fast import WhisperTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_whisper import (
        WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
        WhisperForAudioClassification,
        WhisperForConditionalGeneration,
        WhisperModel,
        WhisperPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_whisper import (
        TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFWhisperForConditionalGeneration,
        TFWhisperModel,
        TFWhisperPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_whisper import (
        FlaxWhisperForAudioClassification,
        FlaxWhisperForConditionalGeneration,
        FlaxWhisperModel,
        FlaxWhisperPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase												=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
