
"""simple docstring"""






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowerCamelCase__ :       Optional[Any]						     =			logging.get_logger(__name__)




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

	def __init__(						self    :Optional[int]		, **lowerCamelCase_    :str					)							->			Optional[int]:







			'''simple docstring'''




			super().__init__(**lowerCamelCase_					)

			if self.framework != "pt":
					raise ValueError(f"The {self.__class__} is only available in PyTorch."					)
			# No specific FOR_XXX available yet





	def __call__(						self    :str		, lowerCamelCase_    :Union[np.ndarray, bytes, str]		, **lowerCamelCase_    :List[str]					)							->			Dict:







			'''simple docstring'''




			return super().__call__(lowerCamelCase_		, **lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict		, **lowerCamelCase_    :Tuple					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= {}
			if "candidate_labels" in kwargs:
					SCREAMING_SNAKE_CASE       :     Any				= kwargs['''candidate_labels''']
			if "hypothesis_template" in kwargs:
					SCREAMING_SNAKE_CASE       :     Any				= kwargs['''hypothesis_template''']

			return preprocess_params, {}, {}





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :Any="This is a sound of {}."					)							->			Union[str, Any]:







			'''simple docstring'''




			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					if audio.startswith('''http://'''					) or audio.startswith('''https://'''					):
							# We need to actually check for a real protocol, otherwise it's impossible to use a local file
							# like http_huggingface_co.png
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= requests.get(lowerCamelCase_					).content
					else:
							with open(lowerCamelCase_		, '''rb'''					) as f:
									SCREAMING_SNAKE_CASE       :     Any				= f.read()

			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     str				= ffmpeg_read(lowerCamelCase_		, self.feature_extractor.sampling_rate					)

			if not isinstance(lowerCamelCase_		, np.ndarray					):
					raise ValueError('''We expect a numpy ndarray as input'''					)
			if len(audio.shape					) != 1:
					raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline'''					)

			SCREAMING_SNAKE_CASE       :     Any				= self.feature_extractor(
			    [audio]		, sampling_rate=self.feature_extractor.sampling_rate		, return_tensors='''pt'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= candidate_labels
			SCREAMING_SNAKE_CASE       :     Any				= [hypothesis_template.format(lowerCamelCase_					) for x in candidate_labels]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.tokenizer(lowerCamelCase_		, return_tensors=self.framework		, padding=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= [text_inputs]
			return inputs





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Dict					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_inputs.pop('''candidate_labels'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_inputs.pop('''text_inputs'''					)
			if isinstance(text_inputs[0]		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= text_inputs[0]
			else:
					# Batching case.
					SCREAMING_SNAKE_CASE       :     List[str]				= text_inputs[0][0]

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.model(**lowerCamelCase_		, **lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {
			    '''candidate_labels''': candidate_labels,
			    '''logits''': outputs.logits_per_audio,
			}
			return model_outputs







	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Optional[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= model_outputs.pop('''candidate_labels'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= model_outputs['''logits'''][0]

			if self.framework == "pt":
					SCREAMING_SNAKE_CASE       :     Dict				= logits.softmax(dim=0					)
					SCREAMING_SNAKE_CASE       :     Dict				= probs.tolist()
			else:
					raise ValueError('''`tf` framework not supported.'''					)

			SCREAMING_SNAKE_CASE       :     Dict				= [
			    {'''score''': score, '''label''': candidate_label}
			    for score, candidate_label in sorted(zip(lowerCamelCase_		, lowerCamelCase_					)		, key=lambda lowerCamelCase_					: -x[0]					)
			]
			return result







"""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}'''
					)





