

'''simple docstring'''




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_lowerCAmelCase         =						200
# 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.
_lowerCAmelCase         =						50
# 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.
_lowerCAmelCase         =						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))




def 						UpperCamelCase	(			a		,						a			)				->     tuple[str, float]:






     '''simple docstring'''
     __magic_name__						       =    len([g for position, g in enumerate(a			) if g == main_target[position]]			)
     return (item, float(a			))




def 						UpperCamelCase	(			a		,						a			)				->     tuple[str, str]:






     '''simple docstring'''
     __magic_name__						       =    random.randint(0		,						len(a			) - 1			)
     __magic_name__						       =    parent_a[:random_slice] + parent_a[random_slice:]
     __magic_name__						       =    parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)




def 						UpperCamelCase	(			a		,						a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    list(a			)
     if random.uniform(0		,						1			) < MUTATION_PROBABILITY:
          __magic_name__						       =    random.choice(a			)
     return "".join(a			)




def 						UpperCamelCase	(			a		,						a		,						a		,						)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     # Generate more children proportionally to the fitness score.
     __magic_name__						       =    int(parent_a[1] * 100			) + 1
     __magic_name__						       =    10 if child_n >= 10 else child_n
     for _ in range(a			):
          __magic_name__						       =    population_score[random.randint(0		,						a			)][0]

          __magic_name__       ,  __magic_name__						       =    crossover(parent_a[0]		,						a			)
          # Append new string to the population list.
          pop.append(mutate(a		,						a			)			)
          pop.append(mutate(a		,						a			)			)
     return pop




def 						UpperCamelCase	(			a		,						a		,						a = True			)				->     tuple[int, int, str]:






     '''simple docstring'''

     # Verify if N_POPULATION is bigger than N_SELECTED
     if N_POPULATION < N_SELECTED:
          __magic_name__						       =    F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
          raise ValueError(a			)
     # Verify that the target contains no genes besides the ones inside genes variable.
     __magic_name__						       =    sorted({c for c in target if c not in genes}			)
     if not_in_genes_list:
          __magic_name__						       =    F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(a			)

     # Generate random starting population.
     __magic_name__						       =    []
     for _ in range(a			):
          population.append(''''''.join([random.choice(a			) for i in range(len(a			)			)]			)			)

     # Just some logs to know what the algorithms is doing.
     __magic_name__       ,  __magic_name__						       =    0, 0

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

          # 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.
          __magic_name__						       =    [evaluate(a		,						a			) for item in population]

          # Check if there is a matching evolution.
          __magic_name__						       =    sorted(a		,						key=lambda a			: x[1]		,						reverse=a			)
          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.
          __magic_name__						       =    population[: int(N_POPULATION / 3			)]
          population.clear()
          population.extend(a			)
          # Normalize population score to be between 0 and 1.
          __magic_name__						       =    [
              (item, score / len(a			)) for item, score in population_score
          ]

          # This is selection
          for i in range(a			):
               population.extend(select(population_score[int(a			)]		,						a		,						a			)			)
               # 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(a			) > N_POPULATION:
                    break


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









'''simple docstring'''




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
      from ...feature_extraction_utils import FeatureExtractionMixin
      from ...tokenization_utils_base import PreTrainedTokenizerBase
      from ...utils import TensorType

_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
_lowerCAmelCase         =						[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_lowerCAmelCase         =						[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:str      	=					"""whisper"""
 __SCREAMING_SNAKE_CASE					:str      	=					["""past_key_values"""]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



 def __init__(   self		:   Dict			,				a__		:   Optional[int]=5_1865			,				a__		:   str=80			,				a__		:   List[str]=6			,				a__		:   List[str]=4			,				a__		:   List[Any]=6			,				a__		:   Union[str, Any]=4			,				a__		:   Tuple=1536			,				a__		:   Optional[int]=1536			,				a__		:   List[str]=0.0			,				a__		:   Union[str, Any]=0.0			,				a__		:   Union[str, Any]=5_0257			,				a__		:   Dict=True			,				a__		:   Optional[Any]=True			,				a__		:   Union[str, Any]="gelu"			,				a__		:   Tuple=256			,				a__		:   Dict=0.0			,				a__		:   str=0.0			,				a__		:   Optional[Any]=0.0			,				a__		:   int=0.02			,				a__		:   Any=False			,				a__		:   List[Any]=1500			,				a__		:   Optional[int]=448			,				a__		:   Dict=5_0256			,				a__		:   str=5_0256			,				a__		:   Tuple=5_0256			,				a__		:   List[str]=None			,				a__		:   List[Any]=[220, 5_0256]			,				a__		:   Any=False			,				a__		:   Dict=256			,				a__		:   Optional[Any]=False			,				a__		:   str=0.05			,				a__		:   List[Any]=10			,				a__		:   List[Any]=2			,				a__		:   Optional[int]=0.0			,				a__		:   List[Any]=10			,				a__		:   Union[str, Any]=0			,				a__		:   int=7			,				**a__		:   Any			,				):
      __magic_name__						       =    vocab_size
      __magic_name__						       =    num_mel_bins
      __magic_name__						       =    d_model
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    encoder_attention_heads
      __magic_name__						       =    decoder_layers
      __magic_name__						       =    decoder_attention_heads
      __magic_name__						       =    decoder_ffn_dim
      __magic_name__						       =    encoder_ffn_dim
      __magic_name__						       =    dropout
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    activation_dropout
      __magic_name__						       =    activation_function
      __magic_name__						       =    init_std
      __magic_name__						       =    encoder_layerdrop
      __magic_name__						       =    decoder_layerdrop
      __magic_name__						       =    use_cache
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    scale_embedding  # scale factor will be sqrt(d_model) if True
      __magic_name__						       =    max_source_positions
      __magic_name__						       =    max_target_positions

      # Audio Classification-specific parameters. Feel free to ignore for other classes.
      __magic_name__						       =    classifier_proj_size
      __magic_name__						       =    use_weighted_layer_sum

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __magic_name__						       =    apply_spec_augment
      __magic_name__						       =    mask_time_prob
      __magic_name__						       =    mask_time_length
      __magic_name__						       =    mask_time_min_masks
      __magic_name__						       =    mask_feature_prob
      __magic_name__						       =    mask_feature_length
      __magic_name__						       =    mask_feature_min_masks

      __magic_name__						       =    median_filter_width

      super().__init__(
          pad_token_id=a__			,				bos_token_id=a__			,				eos_token_id=a__			,				is_encoder_decoder=a__			,				decoder_start_token_id=a__			,				suppress_tokens=a__			,				begin_suppress_tokens=a__			,				**a__			,				)






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    OrderedDict(
          [
              ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
          ]      )
      if self.use_past:
           __magic_name__						       =    {0: '''batch'''}
      else:
           __magic_name__						       =    {0: '''batch''', 1: '''decoder_sequence'''}

      if self.use_past:
           self.fill_with_past_key_values_(a__			,				direction='''inputs'''      )

      return common_inputs



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,				a__		:   int = -1			,				a__		:   int = -1			,				a__		:   bool = False			,				a__		:   Optional["TensorType"] = None			,				a__		:   int = 2_2050			,				a__		:   float = 5.0			,				a__		:   int = 220			,				):
      __magic_name__						       =    OrderedDict()
      __magic_name__						       =    OnnxConfig.generate_dummy_inputs(
          self			,				preprocessor=preprocessor.feature_extractor			,				batch_size=a__			,				framework=a__			,				sampling_rate=a__			,				time_duration=a__			,				frequency=a__			,				)
      __magic_name__						       =    encoder_inputs['''input_features'''].shape[2]
      __magic_name__						       =    encoder_sequence_length // 2 if self.use_past else seq_length

      __magic_name__						       =    super().generate_dummy_inputs(
          preprocessor.tokenizer			,				a__			,				a__			,				a__			,				a__      )

      __magic_name__						       =    encoder_inputs.pop('''input_features'''      )
      __magic_name__						       =    decoder_inputs.pop('''decoder_input_ids'''      )

      if "past_key_values" in decoder_inputs:
           __magic_name__						       =    decoder_inputs.pop('''past_key_values'''      )

      return dummy_inputs



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 1E-3






