

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							str				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Optional[Any]=9_9				,  _UpperCamelCase					:							Tuple=1_3				,  _UpperCamelCase					:							str=7				,  _UpperCamelCase					:							int=9				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							Optional[Any]=True				,  _UpperCamelCase					:							List[Any]=False				,  _UpperCamelCase					:							int=3_2				,  _UpperCamelCase					:							List[Any]=5				,  _UpperCamelCase					:							str=4				,  _UpperCamelCase					:							str=3_7				,  _UpperCamelCase					:							Optional[Any]=8				,  _UpperCamelCase					:							Union[str, Any]=0.1				,  _UpperCamelCase					:							Dict=0.002				,  _UpperCamelCase					:							Tuple=1				,  _UpperCamelCase					:							List[Any]=0				,  _UpperCamelCase					:							List[Any]=0				,  _UpperCamelCase					:							Tuple=None				,  _UpperCamelCase					:							Dict=None				,  ) ->Union[str, Any]:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      encoder_seq_length
									snake_case_  =      decoder_seq_length
									# For common tests
									snake_case_  =      self.decoder_seq_length
									snake_case_  =      is_training
									snake_case_  =      use_attention_mask
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      d_ff
									snake_case_  =      relative_attention_num_buckets
									snake_case_  =      dropout_rate
									snake_case_  =      initializer_factor
									snake_case_  =      eos_token_id
									snake_case_  =      pad_token_id
									snake_case_  =      decoder_start_token_id
									snake_case_  =      None
									snake_case_  =      decoder_layers







			def 							snake_case__(	self					:							Tuple				) ->int:
									return TaConfig.from_pretrained('''google/umt5-base'''				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							int=None				,  _UpperCamelCase					:							List[Any]=None				,  _UpperCamelCase					:							Optional[Any]=None				,  _UpperCamelCase					:							List[str]=None				,  _UpperCamelCase					:							Optional[Any]=None				,  ) ->Optional[Any]:
									if attention_mask is None:
															snake_case_  =      input_ids.ne(config.pad_token_id				)
									if decoder_attention_mask is None:
															snake_case_  =      decoder_input_ids.ne(config.pad_token_id				)
									if head_mask is None:
															snake_case_  =      torch.ones(config.num_hidden_layers				,  config.num_attention_heads				,  device=_UpperCamelCase				)
									if decoder_head_mask is None:
															snake_case_  =      torch.ones(config.num_decoder_layers				,  config.num_attention_heads				,  device=_UpperCamelCase				)
									if cross_attn_head_mask is None:
															snake_case_  =      torch.ones(
															    config.num_decoder_layers				,  config.num_attention_heads				,  device=_UpperCamelCase				)
									return {
									    "input_ids": input_ids,
									    "decoder_input_ids": decoder_input_ids,
									    "attention_mask": attention_mask,
									    "decoder_attention_mask": decoder_attention_mask,
									    "head_mask": head_mask,
									    "decoder_head_mask": decoder_head_mask,
									    "cross_attn_head_mask": cross_attn_head_mask,
									}







			def 							snake_case__(	self					:							Optional[Any]				) ->Optional[int]:
									snake_case_  =      ids_tensor([self.batch_size, self.encoder_seq_length]				,  self.vocab_size				)
									snake_case_  =      ids_tensor([self.batch_size, self.decoder_seq_length]				,  self.vocab_size				)

									# we need to clamp the input ids here to avoid having pad token in between
									# this is because for NllbMoe the position_ids are prepared such that
									# all pad tokens have pos id = 2 and rest are between 2..seq_length
									# and the seq_length here is seq_length - num_pad_tokens
									# but when using past, there is no way of knowing if the past input ids had
									# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
									# position_ids being off by num_pad_tokens in past input
									snake_case_  =      input_ids.clamp(self.pad_token_id + 1				)
									snake_case_  =      decoder_input_ids.clamp(self.pad_token_id + 1				)

									snake_case_  =      self.get_config()
									snake_case_  =      config.num_attention_heads
									snake_case_  =      self.prepare_inputs_dict(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)
									return config, input_dict







			def 							snake_case__(	self					:							str				) ->Optional[int]:
									snake_case_,       snake_case_  =      self.prepare_config_and_inputs()
									return config, inputs_dict







			def 							snake_case__(	self					:							Optional[int]				) ->int:
									return TaConfig(
									    vocab_size=1_6_6				,  d_model=self.hidden_size				,  d_ff=self.d_ff				,  d_kv=self.hidden_size // self.num_attention_heads				,  num_layers=self.num_hidden_layers				,  num_decoder_layers=self.decoder_layers				,  num_heads=self.num_attention_heads				,  relative_attention_num_buckets=self.relative_attention_num_buckets				,  dropout_rate=self.dropout_rate				,  initializer_factor=self.initializer_factor				,  eos_token_id=self.eos_token_id				,  bos_token_id=self.pad_token_id				,  pad_token_id=self.pad_token_id				,  decoder_start_token_id=self.decoder_start_token_id				,  )







			def 							snake_case__(	self					:							Union[str, Any]				) ->Any:
									return TaConfig(
									    vocab_size=self.vocab_size				,  d_model=self.hidden_size				,  d_ff=self.d_ff				,  d_kv=self.hidden_size // self.num_attention_heads				,  num_layers=self.num_hidden_layers				,  num_decoder_layers=self.decoder_layers				,  num_heads=self.num_attention_heads				,  relative_attention_num_buckets=self.relative_attention_num_buckets				,  dropout_rate=self.dropout_rate				,  initializer_factor=self.initializer_factor				,  eos_token_id=self.eos_token_id				,  bos_token_id=self.pad_token_id				,  pad_token_id=self.pad_token_id				,  decoder_start_token_id=self.decoder_start_token_id				,  )







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Tuple				,  ) ->Union[str, Any]:
									snake_case_  =      UMTaModel(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(
									    input_ids=_UpperCamelCase				,  decoder_input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  decoder_attention_mask=_UpperCamelCase				,  )
									snake_case_  =      model(input_ids=_UpperCamelCase				,  decoder_input_ids=_UpperCamelCase				)
									snake_case_  =      result.last_hidden_state
									snake_case_  =      result.past_key_values
									snake_case_  =      result.encoder_last_hidden_state

									self.parent.assertEqual(encoder_output.size()				,  (self.batch_size, self.encoder_seq_length, self.hidden_size)				)
									self.parent.assertEqual(decoder_output.size()				,  (self.batch_size, self.decoder_seq_length, self.hidden_size)				)
									# There should be `num_layers` key value embeddings stored in decoder_past
									self.parent.assertEqual(len(_UpperCamelCase				)				,  config.num_layers				)
									# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
									self.parent.assertEqual(len(decoder_past[0]				)				,  4				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Any				,  ) ->str:
									snake_case_  =      UMTaModel(config=_UpperCamelCase				).get_decoder().to(_UpperCamelCase				).eval()
									# first forward pass
									snake_case_  =      model(_UpperCamelCase				,  use_cache=_UpperCamelCase				)
									snake_case_  =      model(_UpperCamelCase				)
									snake_case_  =      model(_UpperCamelCase				,  use_cache=_UpperCamelCase				)

									self.parent.assertTrue(len(_UpperCamelCase				) == len(_UpperCamelCase				)				)
									self.parent.assertTrue(len(_UpperCamelCase				) == len(_UpperCamelCase				) + 1				)

									snake_case_,       snake_case_  =      outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									snake_case_  =      ids_tensor((self.batch_size, 1)				,  config.vocab_size				)

									# append to next input_ids and
									snake_case_  =      torch.cat([input_ids, next_tokens]				,  dim=-1				)

									snake_case_  =      model(_UpperCamelCase				)['''last_hidden_state''']
									snake_case_  =      model(_UpperCamelCase				,  past_key_values=_UpperCamelCase				)['''last_hidden_state''']

									# select random slice
									snake_case_  =      ids_tensor((1,)				,  output_from_past.shape[-1]				).item()
									snake_case_  =      output_from_no_past[:, -1, random_slice_idx].detach()
									snake_case_  =      output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(_UpperCamelCase				,  _UpperCamelCase				,  atol=1e-3				)				)







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Optional[Any]				,  ) ->Dict:
									snake_case_  =      UMTaModel(config=_UpperCamelCase				).to(_UpperCamelCase				).half().eval()
									snake_case_  =      model(**_UpperCamelCase				)['''last_hidden_state''']
									self.parent.assertFalse(torch.isnan(_UpperCamelCase				).any().item()				)






@require_torch
class       snake_case_  (						__A      ,			__A      ,			__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  int		     =							(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			SCREAMING_SNAKE_CASE	:  int		     =							(UMTaForConditionalGeneration,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							(
			    {
			        "conversational": UMTaForConditionalGeneration,
			        "feature-extraction": UMTaModel,
			        "summarization": UMTaForConditionalGeneration,
			        "text2text-generation": UMTaForConditionalGeneration,
			        "translation": UMTaForConditionalGeneration,
			        "question-answering": UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							True
			SCREAMING_SNAKE_CASE	:  List[str]		     =							False
			SCREAMING_SNAKE_CASE	:  str		     =							False
			SCREAMING_SNAKE_CASE	:  List[str]		     =							True
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							[0.8, 0.9]







			def 							snake_case__(	self					:							Union[str, Any]				) ->List[str]:
									snake_case_  =      UMTaModelTester(self				)







			@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''				)
			def 							snake_case__(	self					:							Dict				) ->List[Any]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									snake_case_  =      UMTaModel(config_and_inputs[0]				).to(_UpperCamelCase				)
									with tempfile.TemporaryDirectory() as tmpdirname:
															torch.onnx.export(
															    _UpperCamelCase				,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,  f'''{tmpdirname}/t5_test.onnx'''				,  export_params=_UpperCamelCase				,  opset_version=9				,  input_names=['''input_ids''', '''decoder_input_ids''']				,  )







			@unittest.skipIf(torch_device == '''cpu'''				,  '''Cant do half precision'''				)
			def 							snake_case__(	self					:							Tuple				) ->List[Any]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*_UpperCamelCase				)







			def 							snake_case__(	self					:							int				) ->str:
									snake_case_  =      ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									snake_case_  =      config_and_inputs[0]
									snake_case_  =      UMTaForConditionalGeneration(_UpperCamelCase				).eval()
									model.to(_UpperCamelCase				)

									snake_case_  =      {
									    '''head_mask''': torch.zeros(config.num_layers				,  config.num_heads				,  device=_UpperCamelCase				),
									    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers				,  config.num_heads				,  device=_UpperCamelCase				),
									    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers				,  config.num_heads				,  device=_UpperCamelCase				),
									}

									for attn_name, (name, mask) in zip(_UpperCamelCase				,  head_masking.items()				):
															snake_case_  =      {name: mask}
															# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
															if name == "head_mask":
																					snake_case_  =      torch.ones(
																					    config.num_decoder_layers				,  config.num_heads				,  device=_UpperCamelCase				)

															snake_case_  =      model.generate(
															    config_and_inputs[1]['''input_ids''']				,  num_beams=1				,  max_length=3				,  output_attentions=_UpperCamelCase				,  return_dict_in_generate=_UpperCamelCase				,  **_UpperCamelCase				,  )
															# We check the state of decoder_attentions and cross_attentions just from the last step
															snake_case_  =      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
															self.assertEqual(sum([w.sum().item() for w in attn_weights]				)				,  0.0				)







			@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''				)
			def 							snake_case__(	self					:							List[str]				) ->Optional[Any]:
									pass






@require_torch
@require_sentencepiece
@require_tokenizers
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			@slow
			@unittest.skip(
			    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''				)
			def 							snake_case__(	self					:							List[str]				) ->Optional[int]:
									snake_case_  =      UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				,  return_dict=_UpperCamelCase				).to(_UpperCamelCase				)
									snake_case_  =      AutoTokenizer.from_pretrained('''google/umt5-small'''				,  use_fast=_UpperCamelCase				,  legacy=_UpperCamelCase				)
									snake_case_  =      [
									    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
									    '''No se como puedo <extra_id_0>.''',
									    '''This is the reason why we <extra_id_0> them.''',
									    '''The <extra_id_0> walks in <extra_id_1>, seats''',
									    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
									]
									snake_case_  =      tokenizer(_UpperCamelCase				,  return_tensors='''pt'''				,  padding=_UpperCamelCase				).input_ids
									# fmt: off
									snake_case_  =      torch.tensor(
									    [
									        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
									    ]				)
									# fmt: on
									torch.testing.assert_allclose(_UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      model.generate(input_ids.to(_UpperCamelCase				)				)
									snake_case_  =      [
									    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
									    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
									    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
									    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
									    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
									]
									snake_case_  =      tokenizer.batch_decode(_UpperCamelCase				)
									self.assertEqual(_UpperCamelCase				,  _UpperCamelCase				)

"""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}"
			)
