






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase  :							List[str]			  =    logging.get_logger(__name__)




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   str						=					['input_features']






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=80		,				_SCREAMING_SNAKE_CASE=1_6000		,				_SCREAMING_SNAKE_CASE=160		,				_SCREAMING_SNAKE_CASE=30		,				_SCREAMING_SNAKE_CASE=400		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE		,				) ->				Dict:
								super().__init__(
								    feature_size=_SCREAMING_SNAKE_CASE		,				sampling_rate=_SCREAMING_SNAKE_CASE		,				padding_value=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							int						 =       n_fft
								snake_case_				:							List[Any]						 =       hop_length
								snake_case_				:							str						 =       chunk_length
								snake_case_				:							int						 =       chunk_length * sampling_rate
								snake_case_				:							Any						 =       self.n_samples // hop_length
								snake_case_				:							Any						 =       sampling_rate
								snake_case_				:							List[Any]						 =       mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2		,				num_mel_filters=_SCREAMING_SNAKE_CASE		,				min_frequency=0.0		,				max_frequency=8000.0		,				sampling_rate=_SCREAMING_SNAKE_CASE		,				norm="slaney"		,				mel_scale="slaney"		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				np.ndarray:
								snake_case_				:							List[Any]						 =       spectrogram(
								    _SCREAMING_SNAKE_CASE		,				window_function(self.n_fft		,				"hann"				)		,				frame_length=self.n_fft		,				hop_length=self.hop_length		,				power=2.0		,				mel_filters=self.mel_filters		,				log_mel="log10"		,				)
								snake_case_				:							Optional[int]						 =       log_spec[:, :-1]
								snake_case_				:							Union[str, Any]						 =       np.maximum(_SCREAMING_SNAKE_CASE		,				log_spec.max() - 8.0				)
								snake_case_				:							Dict						 =       (log_spec + 4.0) / 4.0
								return log_spec






						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def       _lowerCAmelCase       (	_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 0.0				) ->				List[np.ndarray]:
								if attention_mask is not None:
										snake_case_				:							int						 =       np.array(_SCREAMING_SNAKE_CASE		,				np.intaa				)
										snake_case_				:							List[str]						 =       []

										for vector, length in zip(_SCREAMING_SNAKE_CASE		,				attention_mask.sum(-1				)				):
												snake_case_				:							int						 =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7				)
												if length < normed_slice.shape[0]:
														snake_case_				:							Union[str, Any]						 =       padding_value

												normed_input_values.append(_SCREAMING_SNAKE_CASE				)
								else:
										snake_case_				:							Any						 =       [(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

								return normed_input_values






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = "max_length"		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchFeature:

								if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
												raise ValueError(
												    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
												    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
												    f''' was sampled with {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."				)

								snake_case_				:							Optional[Any]						 =       isinstance(_SCREAMING_SNAKE_CASE		,				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}'''				)
								snake_case_				:							Optional[Any]						 =       is_batched_numpy or (
								    isinstance(_SCREAMING_SNAKE_CASE		,				(list, tuple)				) and (isinstance(raw_speech[0]		,				(np.ndarray, tuple, list)				))
								)

								if is_batched:
										snake_case_				:							Dict						 =       [np.asarray([speech]		,				dtype=np.floataa				).T for speech in raw_speech]
								elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE		,				np.ndarray				):
										snake_case_				:							List[str]						 =       np.asarray(_SCREAMING_SNAKE_CASE		,				dtype=np.floataa				)
								elif isinstance(_SCREAMING_SNAKE_CASE		,				np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
										snake_case_				:							Dict						 =       raw_speech.astype(np.floataa				)

								# always return batch
								if not is_batched:
										snake_case_				:							Tuple						 =       [np.asarray([raw_speech]				).T]

								snake_case_				:							List[Any]						 =       BatchFeature({"input_features": raw_speech}				)

								# convert into correct format for padding

								snake_case_				:							Union[str, Any]						 =       self.pad(
								    _SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				max_length=max_length if max_length else self.n_samples		,				truncation=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_attention_mask=return_attention_mask or do_normalize		,				)

								# zero-mean and unit-variance normalization
								if do_normalize:
										snake_case_				:							Dict						 =       self.zero_mean_unit_var_norm(
										    padded_inputs["input_features"]		,				attention_mask=padded_inputs["attention_mask"]		,				padding_value=self.padding_value		,				)
										snake_case_				:							List[Any]						 =       np.stack(padded_inputs["input_features"]		,				axis=0				)

								# make sure list is in array format
								snake_case_				:							Optional[Any]						 =       padded_inputs.get("input_features"				).transpose(2		,				0		,				1				)

								snake_case_				:							Tuple						 =       [self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE				) for waveform in input_features[0]]

								if isinstance(input_features[0]		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							int						 =       [np.asarray(_SCREAMING_SNAKE_CASE		,				dtype=np.floataa				) for feature in input_features]
								else:
										snake_case_				:							Dict						 =       input_features

								if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										snake_case_				:							Optional[int]						 =       padded_inputs["attention_mask"][:, :: self.hop_length]

								if return_tensors is not None:
										snake_case_				:							Dict						 =       padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE				)

								return padded_inputs






						def       _lowerCAmelCase       (	self				) ->				Dict[str, Any]:
								snake_case_				:							List[str]						 =       copy.deepcopy(self.__dict__				)
								snake_case_				:							List[str]						 =       self.__class__.__name__
								if "mel_filters" in output:
										del output["mel_filters"]
								return output








from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase  :							Optional[Any]			  =    2_00
# 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.
lowercase  :							Optional[Any]			  =    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.
lowercase  :							Union[str, Any]			  =    0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str    ):
		snake_case_				:							Union[str, Any]						 =       len([g for position, g in enumerate(_a    ) if g == main_target[position]]    )
		return (item, float(_a    ))

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str    ):
		snake_case_				:							Tuple						 =       random.randint(0  ,      len(_a    ) - 1    )
		snake_case_				:							Any						 =       parent_a[:random_slice] + parent_a[random_slice:]
		snake_case_				:							Optional[Any]						 =       parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			list[str]    ):
		snake_case_				:							str						 =       list(_a    )
		if random.uniform(0  ,      1    ) < MUTATION_PROBABILITY:
				snake_case_				:							Optional[Any]						 =       random.choice(_a    )
		return "".join(_a    )

def      lowerCAmelCase__	(    _a       :			tuple[str, float]  ,      _a       :			list[tuple[str, float]]  ,      _a       :			list[str]  ,      ):
		snake_case_				:							Tuple						 =       []
		# Generate more children proportionally to the fitness score.
		snake_case_				:							Optional[int]						 =       int(parent_a[1] * 1_00    ) + 1
		snake_case_				:							Tuple						 =       10 if child_n >= 10 else child_n
		for _ in range(_a    ):
				snake_case_				:							int						 =       population_score[random.randint(0  ,      _a    )][0]

				snake_case_ ,       snake_case_				:							Any						 =       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      lowerCAmelCase__	(    _a       :			str  ,      _a       :			list[str]  ,      _a       :			bool = True    ):

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				snake_case_				:							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.
		snake_case_				:							Optional[Any]						 =       sorted({c for c in target if c not in genes}    )
		if not_in_genes_list:
				snake_case_				:							str						 =       F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
				raise ValueError(_a    )

		# Generate random starting population.
		snake_case_				:							Optional[int]						 =       []
		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.
		snake_case_ ,       snake_case_				:							Dict						 =       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.
				snake_case_				:							Optional[int]						 =       [evaluate(_a  ,      _a    ) for item in population]

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