
"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__ :       List[str]						     =			{
    "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:
					lowerCamelCase__ :       Dict						     =			["WhisperTokenizerFast"]

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

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

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       int						     =			[
					    "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

					lowerCamelCase__ :       int						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			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__ :       Optional[int]						     =			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__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= 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 __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= 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.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		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.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 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.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

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





