



'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
							from huggingface_hub import snapshot_download
							from pyctcdecode import BeamSearchDecoderCTC

							from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
							from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
							from transformers import WavaVecaForCTC

@require_pyctcdecode
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	str		):
									_a					:				List[str]				=							'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
									_a					:				Dict				=							dict(zip(_UpperCAmelCase     ,range(len(_UpperCAmelCase		)		)		)		)

									_a					:				Dict				=							{
									    'unk_token': '<unk>',
									    'bos_token': '<s>',
									    'eos_token': '</s>',
									}
									_a					:				Tuple				=							{
									    'feature_size': 1,
									    'padding_value': 0.0,
									    'sampling_rate': 16000,
									    'return_attention_mask': False,
									    'do_normalize': True,
									}

									_a					:				Optional[int]				=							tempfile.mkdtemp()
									_a					:				Tuple				=							os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['vocab_file']		)
									_a					:				Tuple				=							os.path.join(self.tmpdirname     ,_UpperCAmelCase		)
									with open(self.vocab_file     ,'w'     ,encoding='utf-8'		) as fp:
											fp.write(json.dumps(_UpperCAmelCase		) + '\n'		)

									with open(self.feature_extraction_file     ,'w'     ,encoding='utf-8'		) as fp:
											fp.write(json.dumps(_UpperCAmelCase		) + '\n'		)

									# load decoder from hub
									_a					:				Optional[int]				=							'hf-internal-testing/ngram-beam-search-decoder'







							def 							__lowercase      (							self :	Any     ,**_UpperCAmelCase :	Optional[int]		):
									_a					:				List[Any]				=							self.add_kwargs_tokens_map.copy()
									kwargs.update(_UpperCAmelCase		)
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]     ,**_UpperCAmelCase :	List[Any]		):
									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	int     ,**_UpperCAmelCase :	Tuple		):
									return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									shutil.rmtree(self.tmpdirname		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				Any				=							self.get_tokenizer()
									_a					:				Optional[int]				=							self.get_feature_extractor()
									_a					:				Union[str, Any]				=							self.get_decoder()

									_a					:				Any				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									processor.save_pretrained(self.tmpdirname		)
									_a					:				Dict				=							WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname		)

									# tokenizer
									self.assertEqual(processor.tokenizer.get_vocab()     ,tokenizer.get_vocab()		)
									self.assertIsInstance(processor.tokenizer     ,_UpperCAmelCase		)

									# feature extractor
									self.assertEqual(processor.feature_extractor.to_json_string()     ,feature_extractor.to_json_string()		)
									self.assertIsInstance(processor.feature_extractor     ,_UpperCAmelCase		)

									# decoder
									self.assertEqual(processor.decoder._alphabet.labels     ,decoder._alphabet.labels		)
									self.assertEqual(
									    processor.decoder.model_container[decoder._model_key]._unigram_set     ,decoder.model_container[decoder._model_key]._unigram_set     ,)
									self.assertIsInstance(processor.decoder     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Optional[Any]				=							WavaVecaProcessorWithLM(
									    tokenizer=self.get_tokenizer()     ,feature_extractor=self.get_feature_extractor()     ,decoder=self.get_decoder()		)
									processor.save_pretrained(self.tmpdirname		)

									# make sure that error is thrown when decoder alphabet doesn't match
									_a					:				List[Any]				=							WavaVecaProcessorWithLM.from_pretrained(
									    self.tmpdirname     ,alpha=5.0     ,beta=3.0     ,score_boundary=-7.0     ,unk_score_offset=3		)

									# decoder
									self.assertEqual(processor.language_model.alpha     ,5.0		)
									self.assertEqual(processor.language_model.beta     ,3.0		)
									self.assertEqual(processor.language_model.score_boundary     ,-7.0		)
									self.assertEqual(processor.language_model.unk_score_offset     ,3		)







							def 							__lowercase      (							self :	Any		):
									_a					:				int				=							self.get_tokenizer()
									# add token to trigger raise
									tokenizer.add_tokens(['xx']		)
									with self.assertRaisesRegex(_UpperCAmelCase     ,'include'		):
											WavaVecaProcessorWithLM(
											    tokenizer=_UpperCAmelCase     ,feature_extractor=self.get_feature_extractor()     ,decoder=self.get_decoder()		)







							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							self.get_feature_extractor()
									_a					:				Dict				=							self.get_tokenizer()
									_a					:				Any				=							self.get_decoder()

									_a					:				List[Any]				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									_a					:				Optional[int]				=							floats_list((3, 1000)		)

									_a					:				List[str]				=							feature_extractor(_UpperCAmelCase     ,return_tensors='np'		)
									_a					:				Dict				=							processor(_UpperCAmelCase     ,return_tensors='np'		)

									for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()     ,input_processor[key].sum()     ,delta=1E-2		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				str				=							self.get_feature_extractor()
									_a					:				Dict				=							self.get_tokenizer()
									_a					:				Any				=							self.get_decoder()

									_a					:				Any				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									_a					:				Union[str, Any]				=							'This is a test string'

									_a					:				Tuple				=							processor(text=_UpperCAmelCase		)

									_a					:				Any				=							tokenizer(_UpperCAmelCase		)

									for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]     ,encoded_processor[key]		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Optional[int]=(2, 10, 16)     ,_UpperCAmelCase :	Tuple=77		):
									np.random.seed(_UpperCAmelCase		)
									return np.random.rand(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				str				=							self.get_feature_extractor()
									_a					:				Any				=							self.get_tokenizer()
									_a					:				Tuple				=							self.get_decoder()

									_a					:				Dict				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									_a					:				Optional[Any]				=							self._get_dummy_logits(shape=(10, 16)     ,seed=13		)

									_a					:				List[Any]				=							processor.decode(_UpperCAmelCase		)

									_a					:				Optional[int]				=							decoder.decode_beams(_UpperCAmelCase		)[0]

									self.assertEqual(decoded_decoder[0]     ,decoded_processor.text		)
									self.assertEqual('</s> <s> </s>'     ,decoded_processor.text		)
									self.assertEqual(decoded_decoder[-2]     ,decoded_processor.logit_score		)
									self.assertEqual(decoded_decoder[-1]     ,decoded_processor.lm_score		)







							@parameterized.expand([[None], ['fork'], ['spawn']]		)
							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	str		):
									_a					:				str				=							self.get_feature_extractor()
									_a					:				Tuple				=							self.get_tokenizer()
									_a					:				List[Any]				=							self.get_decoder()

									_a					:				Union[str, Any]				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									_a					:				Optional[Any]				=							self._get_dummy_logits()

									# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
									#       otherwise, the LM won't be available to the pool's sub-processes.
									# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
									if pool_context is None:
											_a					:				Union[str, Any]				=							processor.batch_decode(_UpperCAmelCase		)
									else:
											with get_context(_UpperCAmelCase		).Pool() as pool:
													_a					:				Union[str, Any]				=							processor.batch_decode(_UpperCAmelCase     ,_UpperCAmelCase		)

									_a					:				int				=							list(_UpperCAmelCase		)

									with get_context('fork'		).Pool() as p:
											_a					:				Optional[int]				=							decoder.decode_beams_batch(_UpperCAmelCase     ,_UpperCAmelCase		)

									_a	, _a	, _a					:				Tuple				=							[], [], []
									for beams in decoded_beams:
											texts_decoder.append(beams[0][0]		)
											logit_scores_decoder.append(beams[0][-2]		)
											lm_scores_decoder.append(beams[0][-1]		)

									self.assertListEqual(_UpperCAmelCase     ,decoded_processor.text		)
									self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']     ,decoded_processor.text		)
									self.assertListEqual(_UpperCAmelCase     ,decoded_processor.logit_score		)
									self.assertListEqual(_UpperCAmelCase     ,decoded_processor.lm_score		)







							def 							__lowercase      (							self :	int		):
									_a					:				Any				=							self.get_feature_extractor()
									_a					:				Optional[int]				=							self.get_tokenizer()
									_a					:				Optional[int]				=							self.get_decoder()

									_a					:				str				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									_a					:				Optional[int]				=							self._get_dummy_logits()

									_a					:				List[str]				=							15
									_a					:				Tuple				=							-20.0
									_a					:				int				=							-4.0

									_a					:				int				=							processor.batch_decode(
									    _UpperCAmelCase     ,beam_width=_UpperCAmelCase     ,beam_prune_logp=_UpperCAmelCase     ,token_min_logp=_UpperCAmelCase     ,)
									_a					:				str				=							decoded_processor_out.text

									_a					:				Any				=							list(_UpperCAmelCase		)

									with get_context('fork'		).Pool() as pool:
											_a					:				Dict				=							decoder.decode_beams_batch(
											    _UpperCAmelCase     ,_UpperCAmelCase     ,beam_width=_UpperCAmelCase     ,beam_prune_logp=_UpperCAmelCase     ,token_min_logp=_UpperCAmelCase     ,)

									_a					:				Dict				=							[d[0][0] for d in decoded_decoder_out]
									_a					:				Union[str, Any]				=							[d[0][2] for d in decoded_decoder_out]
									_a					:				str				=							[d[0][3] for d in decoded_decoder_out]

									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)
									self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']     ,_UpperCAmelCase		)

									self.assertTrue(np.array_equal(_UpperCAmelCase     ,decoded_processor_out.logit_score		)		)
									self.assertTrue(np.allclose([-20.0_54, -18.4_47]     ,_UpperCAmelCase     ,atol=1E-3		)		)

									self.assertTrue(np.array_equal(_UpperCAmelCase     ,decoded_processor_out.lm_score		)		)
									self.assertTrue(np.allclose([-15.5_54, -13.94_74]     ,_UpperCAmelCase     ,atol=1E-3		)		)







							def 							__lowercase      (							self :	List[str]		):
									_a					:				int				=							self.get_feature_extractor()
									_a					:				Optional[int]				=							self.get_tokenizer()
									_a					:				Tuple				=							self.get_decoder()

									_a					:				str				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									_a					:				List[Any]				=							self._get_dummy_logits()

									_a					:				Union[str, Any]				=							2.0
									_a					:				int				=							5.0
									_a					:				List[Any]				=							-20.0
									_a					:				List[str]				=							True

									_a					:				str				=							processor.batch_decode(
									    _UpperCAmelCase     ,alpha=_UpperCAmelCase     ,beta=_UpperCAmelCase     ,unk_score_offset=_UpperCAmelCase     ,lm_score_boundary=_UpperCAmelCase     ,)
									_a					:				Dict				=							decoded_processor_out.text

									_a					:				Union[str, Any]				=							list(_UpperCAmelCase		)
									decoder.reset_params(
									    alpha=_UpperCAmelCase     ,beta=_UpperCAmelCase     ,unk_score_offset=_UpperCAmelCase     ,lm_score_boundary=_UpperCAmelCase     ,)

									with get_context('fork'		).Pool() as pool:
											_a					:				Optional[int]				=							decoder.decode_beams_batch(
											    _UpperCAmelCase     ,_UpperCAmelCase     ,)

									_a					:				Any				=							[d[0][0] for d in decoded_decoder_out]

									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)
									self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']     ,_UpperCAmelCase		)
									_a					:				List[str]				=							processor.decoder.model_container[processor.decoder._model_key]
									self.assertEqual(lm_model.alpha     ,2.0		)
									self.assertEqual(lm_model.beta     ,5.0		)
									self.assertEqual(lm_model.unk_score_offset     ,-20.0		)
									self.assertEqual(lm_model.score_boundary     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Optional[int]				=							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'		)

									_a					:				Tuple				=							processor.decoder.model_container[processor.decoder._model_key]
									_a					:				List[str]				=							Path(language_model._kenlm_model.path.decode('utf-8'		)		).parent.parent.absolute()

									_a					:				Union[str, Any]				=							os.listdir(_UpperCAmelCase		)
									_a					:				Optional[int]				=							['alphabet.json', 'language_model']

									downloaded_decoder_files.sort()
									expected_decoder_files.sort()

									# test that only decoder relevant files from
									# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
									# are downloaded and none of the rest (e.g. README.md, ...)
									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Union[str, Any]				=							snapshot_download('hf-internal-testing/processor_with_lm'		)

									_a					:				Any				=							WavaVecaProcessorWithLM.from_pretrained(_UpperCAmelCase		)

									_a					:				Optional[Any]				=							processor.decoder.model_container[processor.decoder._model_key]
									_a					:				List[Any]				=							Path(language_model._kenlm_model.path.decode('utf-8'		)		).parent.parent.absolute()

									_a					:				List[str]				=							os.listdir(_UpperCAmelCase		)
									_a					:				Any				=							os.listdir(_UpperCAmelCase		)

									local_decoder_files.sort()
									expected_decoder_files.sort()

									# test that both decoder form hub and local files in cache are the same
									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Any				=							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'		)
									_a					:				List[Any]				=							AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'		)

									_a					:				Tuple				=							floats_list((3, 1000)		)

									_a					:				Union[str, Any]				=							processor_wavaveca(_UpperCAmelCase     ,return_tensors='np'		)
									_a					:				int				=							processor_auto(_UpperCAmelCase     ,return_tensors='np'		)

									for key in input_wavaveca.keys():
											self.assertAlmostEqual(input_wavaveca[key].sum()     ,input_auto[key].sum()     ,delta=1E-2		)

									_a					:				Union[str, Any]				=							self._get_dummy_logits()

									_a					:				int				=							processor_wavaveca.batch_decode(_UpperCAmelCase		)
									_a					:				Optional[int]				=							processor_auto.batch_decode(_UpperCAmelCase		)

									self.assertListEqual(decoded_wavaveca.text     ,decoded_auto.text		)







							def 							__lowercase      (							self :	Any		):
									_a					:				List[str]				=							self.get_feature_extractor()
									_a					:				Any				=							self.get_tokenizer()
									_a					:				Any				=							self.get_decoder()

									_a					:				str				=							WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,decoder=_UpperCAmelCase		)

									self.assertListEqual(
									    processor.model_input_names     ,feature_extractor.model_input_names     ,msg='`processor` and `feature_extractor` model input names do not match'     ,)







							@staticmethod
							def 							__lowercase      (							_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Dict		):
									_a					:				str				=							[d[key] for d in offsets]
									return retrieved_list







							def 							__lowercase      (							self :	int		):
									_a					:				Optional[Any]				=							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'		)
									_a					:				List[str]				=							self._get_dummy_logits()[0]

									_a					:				Any				=							processor.decode(_UpperCAmelCase     ,output_word_offsets=_UpperCAmelCase		)
									# check Wav2Vec2CTCTokenizerOutput keys for word
									self.assertEqual(len(outputs.keys()		)     ,4		)
									self.assertTrue('text' in outputs		)
									self.assertTrue('word_offsets' in outputs		)
									self.assertTrue(isinstance(_UpperCAmelCase     ,_UpperCAmelCase		)		)

									self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets']     ,'word'		)		)     ,outputs.text		)
									self.assertListEqual(self.get_from_offsets(outputs['word_offsets']     ,'word'		)     ,['<s>', '<s>', '</s>']		)
									self.assertListEqual(self.get_from_offsets(outputs['word_offsets']     ,'start_offset'		)     ,[0, 2, 4]		)
									self.assertListEqual(self.get_from_offsets(outputs['word_offsets']     ,'end_offset'		)     ,[1, 3, 5]		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				str				=							WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'		)
									_a					:				List[Any]				=							self._get_dummy_logits()

									_a					:				List[str]				=							processor.batch_decode(_UpperCAmelCase     ,output_word_offsets=_UpperCAmelCase		)

									# check Wav2Vec2CTCTokenizerOutput keys for word
									self.assertEqual(len(outputs.keys()		)     ,4		)
									self.assertTrue('text' in outputs		)
									self.assertTrue('word_offsets' in outputs		)
									self.assertTrue(isinstance(_UpperCAmelCase     ,_UpperCAmelCase		)		)

									self.assertListEqual(
									    [' '.join(self.get_from_offsets(_UpperCAmelCase     ,'word'		)		) for o in outputs['word_offsets']]     ,outputs.text		)
									self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]     ,'word'		)     ,['<s>', '<s>', '</s>']		)
									self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]     ,'start_offset'		)     ,[0, 2, 4]		)
									self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]     ,'end_offset'		)     ,[1, 3, 5]		)







							@slow
							@require_torch
							@require_torchaudio
							def 							__lowercase      (							self :	Optional[Any]		):
									import torch

									_a					:				List[Any]				=							load_dataset('common_voice'     ,'en'     ,split='train'     ,streaming=_UpperCAmelCase		)
									_a					:				Any				=							ds.cast_column('audio'     ,datasets.Audio(sampling_rate=16000		)		)
									_a					:				Tuple				=							iter(_UpperCAmelCase		)
									_a					:				Optional[int]				=							next(_UpperCAmelCase		)

									_a					:				List[Any]				=							AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'		)
									_a					:				Any				=							WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'		)

									# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
									_a					:				str				=							processor(sample['audio']['array']     ,return_tensors='pt'		).input_values

									with torch.no_grad():
											_a					:				List[Any]				=							model(_UpperCAmelCase		).logits.cpu().numpy()

									_a					:				int				=							processor.decode(logits[0]     ,output_word_offsets=_UpperCAmelCase		)

									_a					:				Any				=							model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
									_a					:				Union[str, Any]				=							[
									    {
									        'start_time': d['start_offset'] * time_offset,
									        'end_time': d['end_offset'] * time_offset,
									        'word': d['word'],
									    }
									    for d in output['word_offsets']
									]

									_a					:				Union[str, Any]				=							'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'

									# output words
									self.assertEqual(' '.join(self.get_from_offsets(_UpperCAmelCase     ,'word'		)		)     ,_UpperCAmelCase		)
									self.assertEqual(' '.join(self.get_from_offsets(_UpperCAmelCase     ,'word'		)		)     ,output.text		)

									# output times
									_a					:				Dict				=							torch.tensor(self.get_from_offsets(_UpperCAmelCase     ,'start_time'		)		)
									_a					:				Optional[int]				=							torch.tensor(self.get_from_offsets(_UpperCAmelCase     ,'end_time'		)		)

									# fmt: off
									_a					:				Dict				=							torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]		)
									_a					:				Optional[Any]				=							torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]		)
									# fmt: on

									self.assertTrue(torch.allclose(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=0.01		)		)
									self.assertTrue(torch.allclose(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=0.01		)		)









'''simple docstring'''

from __future__ import annotations

import random

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, float]:
		_a					:				List[Any]				=							len([g for position, g in enumerate(lowerCAmelCase_  ) if g == main_target[position]]  )
		return (item, float(lowerCAmelCase_  ))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, str]:
		_a					:				Dict				=							random.randint(0						,      len(lowerCAmelCase_  ) - 1  )
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				Optional[Any]				=							list(lowerCAmelCase_  )
		if random.uniform(0						,      1  ) < MUTATION_PROBABILITY:
				_a					:				Optional[int]				=							random.choice(lowerCAmelCase_  )
		return "".join(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				list[str]:
		_a					:				List[str]				=							[]
		# Generate more children proportionally to the fitness score.
		_a					:				Tuple				=							int(parent_a[1] * 100  ) + 1
		_a					:				Tuple				=							10 if child_n >= 10 else child_n
		for _ in range(lowerCAmelCase_  ):
				_a					:				Any				=							population_score[random.randint(0						,      lowerCAmelCase_  )][0]

				_a	, _a					:				Tuple				=							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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = True  )      ->				tuple[int, int, str]:

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				_a					:				Dict				=							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.
		_a					:				Optional[int]				=							sorted({c for c in target if c not in genes}  )
		if not_in_genes_list:
				_a					:				List[Any]				=							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
				raise ValueError(lowerCAmelCase_  )

		# Generate random starting population.
		_a					:				Union[str, Any]				=							[]
		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.
		_a	, _a					:				Union[str, Any]				=							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.
				_a					:				Optional[Any]				=							[evaluate(lowerCAmelCase_						,      lowerCAmelCase_  ) for item in population]

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




