
"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase__ :       int						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  ["""input_features""", """attention_mask"""]





	def __init__(						self    :Optional[Any]		, lowerCamelCase_    :int=80		, lowerCamelCase_    :Tuple=1_60_00		, lowerCamelCase_    :int=80		, lowerCamelCase_    :int=0.0		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :int=True		, lowerCamelCase_    :List[Any]=True		, **lowerCamelCase_    :Optional[Any]		, )							->			Tuple:







			'''simple docstring'''




			super().__init__(feature_size=lowerCamelCase_		, sampling_rate=lowerCamelCase_		, padding_value=lowerCamelCase_		, **lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= num_mel_bins
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= do_ceptral_normalize
			SCREAMING_SNAKE_CASE       :     List[Any]				= normalize_means
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= normalize_vars
			SCREAMING_SNAKE_CASE       :     int				= True





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :np.ndarray		, )							->			np.ndarray:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
			SCREAMING_SNAKE_CASE       :     str				= torch.from_numpy(lowerCamelCase_					).unsqueeze(0					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= ta_kaldi.fbank(lowerCamelCase_		, num_mel_bins=self.num_mel_bins		, sample_frequency=self.sampling_rate					)
			return features.numpy()





	@staticmethod
	def   __lowerCAmelCase (						lowerCamelCase_    :np.ndarray		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[bool] = True		, lowerCamelCase_    :Optional[bool] = True		, lowerCamelCase_    :float = 0.0		, )							->			np.ndarray:







			'''simple docstring'''




			if normalize_means:
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= x[:input_length].mean(axis=0					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= np.subtract(lowerCamelCase_		, lowerCamelCase_					)
			if normalize_vars:
					SCREAMING_SNAKE_CASE       :     Dict				= x[:input_length].std(axis=0					)
					SCREAMING_SNAKE_CASE       :     Tuple				= np.divide(lowerCamelCase_		, lowerCamelCase_					)

			if input_length < x.shape[0]:
					SCREAMING_SNAKE_CASE       :     Tuple				= padding_value

			# make sure array is in float32
			SCREAMING_SNAKE_CASE       :     int				= x.astype(np.floataa					)

			return x





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :List[np.ndarray]		, lowerCamelCase_    :Optional[np.ndarray] = None					)							->			List[np.ndarray]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
			return [
			    self.utterance_cmvn(lowerCamelCase_		, lowerCamelCase_		, self.normalize_means		, self.normalize_vars		, self.padding_value					)
			    for x, n in zip(lowerCamelCase_		, lowerCamelCase_					)
			]







	def __call__(						self    :int		, lowerCamelCase_    :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		, lowerCamelCase_    :Union[bool, str, PaddingStrategy] = False		, lowerCamelCase_    :Optional[int] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[int] = None		, lowerCamelCase_    :Optional[Union[str, TensorType]] = None		, lowerCamelCase_    :Optional[int] = None		, lowerCamelCase_    :Optional[bool] = None		, **lowerCamelCase_    :str		, )							->			BatchFeature:







			'''simple docstring'''




			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
							    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
							    f" {self.sampling_rate} and not {sampling_rate}."					)
			else:
					logger.warning(
					    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
					    '''Failing to do so can result in silent errors that might be hard to debug.'''					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= isinstance(lowerCamelCase_		, np.ndarray					) and len(raw_speech.shape					) > 1
			if is_batched_numpy and len(raw_speech.shape					) > 2:
					raise ValueError(f"Only mono-channel audio is supported for input to {self}"					)
			SCREAMING_SNAKE_CASE       :     Any				= is_batched_numpy or (
			    isinstance(lowerCamelCase_		, (list, tuple)					) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list)					))
			)

			if is_batched:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= [np.asarray(lowerCamelCase_		, dtype=np.floataa					) for speech in raw_speech]
			elif not is_batched and not isinstance(lowerCamelCase_		, np.ndarray					):
					SCREAMING_SNAKE_CASE       :     int				= np.asarray(lowerCamelCase_		, dtype=np.floataa					)
			elif isinstance(lowerCamelCase_		, np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
					SCREAMING_SNAKE_CASE       :     List[Any]				= raw_speech.astype(np.floataa					)

			# always return batch
			if not is_batched:
					SCREAMING_SNAKE_CASE       :     Any				= [raw_speech]

			# extract fbank features
			SCREAMING_SNAKE_CASE       :     str				= [self._extract_fbank_features(lowerCamelCase_					) for waveform in raw_speech]

			# convert into correct format for padding
			SCREAMING_SNAKE_CASE       :     Any				= BatchFeature({'''input_features''': features}					)

			SCREAMING_SNAKE_CASE       :     int				= self.pad(
			    lowerCamelCase_		, padding=lowerCamelCase_		, max_length=lowerCamelCase_		, truncation=lowerCamelCase_		, pad_to_multiple_of=lowerCamelCase_		, return_attention_mask=lowerCamelCase_		, **lowerCamelCase_		, )

			# make sure list is in array format
			SCREAMING_SNAKE_CASE       :     List[Any]				= padded_inputs.get('''input_features'''					)
			if isinstance(input_features[0]		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= [np.asarray(lowerCamelCase_		, dtype=np.floataa					) for feature in input_features]

			SCREAMING_SNAKE_CASE       :     List[str]				= padded_inputs.get('''attention_mask'''					)
			if attention_mask is not None:
					SCREAMING_SNAKE_CASE       :     List[Any]				= [np.asarray(lowerCamelCase_		, dtype=np.intaa					) for array in attention_mask]

			# Utterance-level cepstral mean and variance normalization
			if self.do_ceptral_normalize:
					SCREAMING_SNAKE_CASE       :     Dict				= (
					    np.array(lowerCamelCase_		, dtype=np.intaa					)
					    if self._get_padding_strategies(lowerCamelCase_		, max_length=lowerCamelCase_					) is not PaddingStrategy.DO_NOT_PAD
					    else None
					)
					SCREAMING_SNAKE_CASE       :     str				= self.normalize(
					    padded_inputs['''input_features''']		, attention_mask=lowerCamelCase_					)

			if return_tensors is not None:
					SCREAMING_SNAKE_CASE       :     List[Any]				= padded_inputs.convert_to_tensors(lowerCamelCase_					)

			return padded_inputs







"""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}'''
					)





