
"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  args.log_outputs
						__lowerCAmelCase     =  """_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

						# load metric
						__lowerCAmelCase     =  load_metric("""wer"""						)
						__lowerCAmelCase     =  load_metric("""cer"""						)

						# compute metrics
						__lowerCAmelCase     =  wer.compute(references=result["""target"""] ,    predictions=result["""prediction"""]						)
						__lowerCAmelCase     =  cer.compute(references=result["""target"""] ,    predictions=result["""prediction"""]						)

						# print & log results
						__lowerCAmelCase     =  f"""WER: {wer_result}\nCER: {cer_result}"""
						print(_lowerCAmelCase						)

						with open(f"""{dataset_id}_eval_results.txt""" ,    """w"""						) as f:
												f.write(_lowerCAmelCase						)

						# log all results in text file. Possibly interesting for analysis
						if log_outputs is not None:
												__lowerCAmelCase     =  f"""log_{dataset_id}_predictions.txt"""
												__lowerCAmelCase     =  f"""log_{dataset_id}_targets.txt"""

												with open(_lowerCAmelCase ,    """w"""						) as p, open(_lowerCAmelCase ,    """w"""						) as t:
																		# mapping function to write output
																		def write_to_file(_lowerCAmelCase ,    _lowerCAmelCase						):
																								p.write(f"""{i}""" + """\n"""						)
																								p.write(batch["""prediction"""] + """\n"""						)
																								t.write(f"""{i}""" + """\n"""						)
																								t.write(batch["""target"""] + """\n"""						)

																		result.map(_lowerCAmelCase ,    with_indices=_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  """[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

						__lowerCAmelCase     =  re.sub(_lowerCAmelCase ,    """""" ,    text.lower()						)

						# In addition, we can normalize the target text, e.g. removing new lines characters etc...
						# note that order is important here!
						__lowerCAmelCase     =  ["""\n\n""", """\n""", """   """, """  """]

						for t in token_sequences_to_ignore:
												__lowerCAmelCase     =  """ """.join(text.split(_lowerCAmelCase						)						)

						return text

def  lowercase     (_lowerCAmelCase						):
						# load dataset
						__lowerCAmelCase     =  load_dataset(args.dataset ,    args.config ,    split=args.split ,    use_auth_token=_lowerCAmelCase						)

						# for testing: only process the first two examples as a test
						# dataset = dataset.select(range(10))

						# load processor
						__lowerCAmelCase     =  AutoFeatureExtractor.from_pretrained(args.model_id						)
						__lowerCAmelCase     =  feature_extractor.sampling_rate

						# resample audio
						__lowerCAmelCase     =  dataset.cast_column("""audio""" ,    Audio(sampling_rate=_lowerCAmelCase						)						)

						# load eval pipeline
						if args.device is None:
												__lowerCAmelCase     =  0 if torch.cuda.is_available() else -1
						__lowerCAmelCase     =  pipeline("""automatic-speech-recognition""" ,    model=args.model_id ,    device=args.device						)

						# map function to decode audio
						def map_to_pred(_lowerCAmelCase						):
												__lowerCAmelCase     =  asr(
												    batch["""audio"""]["""array"""] ,    chunk_length_s=args.chunk_length_s ,    stride_length_s=args.stride_length_s						)

												__lowerCAmelCase     =  prediction["""text"""]
												__lowerCAmelCase     =  normalize_text(batch["""sentence"""]						)
												return batch

						# run inference on all examples
						__lowerCAmelCase     =  dataset.map(_lowerCAmelCase ,    remove_columns=dataset.column_names						)

						# compute and log_results
						# do not change function below
						log_results(_lowerCAmelCase ,    _lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
			)
			parser.add_argument(
			    '''--dataset''',
			    type=str,
			    required=True,
			    help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
			)
			parser.add_argument(
			    '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
			)
			parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
			parser.add_argument(
			    '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
			)
			parser.add_argument(
			    '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
			)
			parser.add_argument(
			    '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
			)
			parser.add_argument(
			    '''--device''',
			    type=int,
			    default=None,
			    help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()

			main(args)


"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE_					     =	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.
SCREAMING_SNAKE_CASE_					     =	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.
SCREAMING_SNAKE_CASE_					     =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  len([g for position, g in enumerate(_lowerCAmelCase						) if g == main_target[position]]						)
						return (item, float(_lowerCAmelCase						))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  random.randint(0 ,    len(_lowerCAmelCase						) - 1						)
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  list(_lowerCAmelCase						)
						if random.uniform(0 ,    1						) < MUTATION_PROBABILITY:
												__lowerCAmelCase     =  random.choice(_lowerCAmelCase						)
						return "".join(_lowerCAmelCase						)

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True						):

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

						# Generate random starting population.
						__lowerCAmelCase     =  []
						for _ in range(_lowerCAmelCase						):
												population.append("""""".join([random.choice(_lowerCAmelCase						) for i in range(len(_lowerCAmelCase						)						)]						)						)

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

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

												# 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.
												__lowerCAmelCase     =  [evaluate(_lowerCAmelCase ,    _lowerCAmelCase						) for item in population]

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

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


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