

'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     Tuple   , __magic_name__						:     int=None   , __magic_name__						:     Dict=None						)				->  List[Any]:
						if attention_mask is None:
												lowercase :							Tuple    		=tf.cast(tf.math.not_equal(__magic_name__   , config.pad_token_id						)   , tf.inta						)
						return {"input_ids": input_ids, "attention_mask": attention_mask}





@require_tf
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    OPTConfig
				lowerCamelCase_		       =    {}
				lowerCamelCase_		       =    'gelu'







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		List[str]=13		,					UpperCAmelCase__     :		int=7		,					UpperCAmelCase__     :		Any=True		,					UpperCAmelCase__     :		Tuple=False		,					UpperCAmelCase__     :		str=99		,					UpperCAmelCase__     :		Tuple=16		,					UpperCAmelCase__     :		Any=2		,					UpperCAmelCase__     :		Union[str, Any]=4		,					UpperCAmelCase__     :		List[Any]=4		,					UpperCAmelCase__     :		Optional[Any]="gelu"		,					UpperCAmelCase__     :		str=0.1		,					UpperCAmelCase__     :		Any=0.1		,					UpperCAmelCase__     :		Any=20		,					UpperCAmelCase__     :		Optional[Any]=2		,					UpperCAmelCase__     :		int=1		,					UpperCAmelCase__     :		Any=0		,					UpperCAmelCase__     :		Dict=16		,					UpperCAmelCase__     :		List[str]=16		,					):
										'''simple docstring'''
										lowercase :							Optional[int]    		=parent
										lowercase :							Tuple    		=batch_size
										lowercase :							Optional[int]    		=seq_length
										lowercase :							List[Any]    		=is_training
										lowercase :							Tuple    		=use_labels
										lowercase :							Any    		=vocab_size
										lowercase :							Any    		=hidden_size
										lowercase :							List[str]    		=num_hidden_layers
										lowercase :							Dict    		=num_attention_heads
										lowercase :							Dict    		=intermediate_size
										lowercase :							Tuple    		=hidden_act
										lowercase :							List[str]    		=hidden_dropout_prob
										lowercase :							Tuple    		=attention_probs_dropout_prob
										lowercase :							Optional[int]    		=max_position_embeddings
										lowercase :							List[Any]    		=eos_token_id
										lowercase :							Optional[int]    		=pad_token_id
										lowercase :							Dict    		=bos_token_id
										lowercase :							Dict    		=embed_dim
										lowercase :							int    		=word_embed_proj_dim
										lowercase :							Dict    		=False







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		=ids_tensor([self.batch_size, self.seq_length - 1]		,					self.vocab_size   )
										lowercase :							Union[str, Any]    		=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )		,					1   )
										lowercase :							Dict    		=tf.concat([input_ids, eos_tensor]		,					axis=1   )

										lowercase :							Any    		=self.config_cls(
										    vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					ffn_dim=self.intermediate_size		,					dropout=self.hidden_dropout_prob		,					attention_dropout=self.attention_probs_dropout_prob		,					max_position_embeddings=self.max_position_embeddings		,					eos_token_id=self.eos_token_id		,					bos_token_id=self.bos_token_id		,					pad_token_id=self.pad_token_id		,					embed_dim=self.embed_dim		,					word_embed_proj_dim=self.word_embed_proj_dim		,					is_encoder_decoder=UpperCAmelCase__		,					**self.config_updates		,					)
										lowercase :							Optional[int]    		=prepare_opt_inputs_dict(UpperCAmelCase__		,					UpperCAmelCase__   )
										return config, inputs_dict







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										lowercase :							Tuple    		=TFOPTModel(config=UpperCAmelCase__   )
										lowercase :							int    		=inputs_dict['''input_ids''']

										lowercase :							List[str]    		=input_ids[:1, :]
										lowercase :							Dict    		=inputs_dict['''attention_mask'''][:1, :]
										lowercase :							Union[str, Any]    		=1

										# first forward pass
										lowercase :							Optional[Any]    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					use_cache=UpperCAmelCase__   )

										lowercase		,	lowercase :							str    		=outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										lowercase :							Any    		=ids_tensor((self.batch_size, 3)		,					config.vocab_size   )
										lowercase :							List[Any]    		=tf.cast(ids_tensor((self.batch_size, 3)		,					2   )		,					tf.inta   )

										# append to next input_ids and
										lowercase :							Optional[int]    		=tf.concat([input_ids, next_tokens]		,					axis=-1   )
										lowercase :							List[Any]    		=tf.concat([attention_mask, next_attn_mask]		,					axis=-1   )

										lowercase :							int    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__   )[0]
										lowercase :							Optional[Any]    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					past_key_values=UpperCAmelCase__   )[0]

										self.parent.assertEqual(next_tokens.shape[1]		,					output_from_past.shape[1]   )

										# select random slice
										lowercase :							Optional[Any]    		=int(ids_tensor((1,)		,					output_from_past.shape[-1]   )   )
										lowercase :							Optional[int]    		=output_from_no_past[:, -3:, random_slice_idx]
										lowercase :							Optional[int]    		=output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(UpperCAmelCase__		,					UpperCAmelCase__		,					rtol=1E-3   )





@require_tf
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
				lowerCamelCase_		       =    (TFOPTForCausalLM,) if is_tf_available() else ()
				lowerCamelCase_		       =    (
				    {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
				)
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    10







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Any    		=TFOPTModelTester(self   )
										lowercase :							Tuple    		=ConfigTester(self		,					config_class=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Any    		=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs_for_common()

										def _get_word_embedding_weight(UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Optional[int]   ):
																if hasattr(UpperCAmelCase__		,					'''weight'''   ):
																						return embedding_layer.weight
																else:
																						# Here we build the word embeddings weights if not exists.
																						# And then we retry to get the attribute once built.
																						model.build()
																						if hasattr(UpperCAmelCase__		,					'''weight'''   ):
																												return embedding_layer.weight
																						else:
																												return None

										for model_class in self.all_model_classes:
																for size in [config.vocab_size - 10, config.vocab_size + 10]:
																						# build the embeddings
																						lowercase :							int    		=model_class(config=UpperCAmelCase__   )
																						lowercase :							str    		=_get_word_embedding_weight(UpperCAmelCase__		,					model.get_input_embeddings()   )
																						lowercase :							List[str]    		=_get_word_embedding_weight(UpperCAmelCase__		,					model.get_output_embeddings()   )

																						# reshape the embeddings
																						model.resize_token_embeddings(UpperCAmelCase__   )
																						lowercase :							Tuple    		=_get_word_embedding_weight(UpperCAmelCase__		,					model.get_input_embeddings()   )
																						lowercase :							Optional[Any]    		=_get_word_embedding_weight(UpperCAmelCase__		,					model.get_output_embeddings()   )

																						# check that the resized embeddings size matches the desired size.
																						lowercase :							Any    		=size if size is not None else config.vocab_size

																						self.assertEqual(new_input_embeddings.shape[0]		,					UpperCAmelCase__   )

																						# check that weights remain the same after resizing
																						lowercase :							int    		=True
																						for pa, pa in zip(old_input_embeddings.value()		,					new_input_embeddings.value()   ):
																												if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																																		lowercase :							Optional[Any]    		=False
																						self.assertTrue(UpperCAmelCase__   )

																						if old_output_embeddings is not None and new_output_embeddings is not None:
																												self.assertEqual(new_output_embeddings.shape[0]		,					UpperCAmelCase__   )

																												lowercase :							Optional[int]    		=True
																												for pa, pa in zip(old_output_embeddings.value()		,					new_output_embeddings.value()   ):
																																		if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																																								lowercase :							Any    		=False
																												self.assertTrue(UpperCAmelCase__   )






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Tuple:
						return tf.constant(__magic_name__   , dtype=tf.intaa						)





@require_tf
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):
				lowerCamelCase_		       =    99







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=tf.ones((4, 1)		,					dtype=tf.intaa   ) * 2
										lowercase :							List[Any]    		=tf.concat([ids_tensor((4, 6)		,					self.vocab_size - 3   ) + 3, eos_column_vector]		,					axis=1   )
										lowercase :							List[Any]    		=input_ids.shape[0]
										lowercase :							str    		=OPTConfig(
										    vocab_size=self.vocab_size		,					hidden_size=24		,					num_hidden_layers=2		,					num_attention_heads=2		,					ffn_dim=32		,					max_position_embeddings=48		,					eos_token_id=2		,					pad_token_id=1		,					bos_token_id=0		,					)
										return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Tuple    		=TFOPTModel.from_pretrained('''facebook/opt-350m'''   )
										lowercase :							Optional[int]    		=_long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   )
										lowercase :							Any    		=tf.not_equal(UpperCAmelCase__		,					model.config.pad_token_id   )
										with tf.GradientTape():
																lowercase :							List[Any]    		=model(input_ids=UpperCAmelCase__		,					attention_mask=UpperCAmelCase__   ).last_hidden_state
										lowercase :							Tuple    		=(1, 11, 512)
										self.assertEqual(output.shape		,					UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=tf.constant(
										    [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]   )
										self.assertTrue(np.allclose(output[:, :3, :3]		,					UpperCAmelCase__		,					atol=4E-3   )   )

										lowercase :							str    		=tf.function(UpperCAmelCase__		,					jit_compile=UpperCAmelCase__   )
										lowercase :							Tuple    		=xla_generate(UpperCAmelCase__		,					UpperCAmelCase__   )[0]
										self.assertTrue(np.allclose(output[:, :3, :3]		,					UpperCAmelCase__		,					atol=4E-2   )   )





@require_tf
@slow
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										super().setUp()
										lowercase :							int    		='''facebook/opt-350m'''







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Any    		=TFOPTForCausalLM.from_pretrained(self.path_model   )
										lowercase :							Dict    		=GPTaTokenizer.from_pretrained(self.path_model   )

										lowercase :							int    		=[
										    '''Today is a beautiful day and I want to''',
										    '''In the city of''',
										    '''Paris is the capital of France and''',
										    '''Computers and mobile phones have taken''',
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										lowercase :							List[Any]    		=tokenizer(UpperCAmelCase__		,					return_tensors='''tf'''		,					padding=UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
										lowercase :							List[Any]    		=tf.math.reduce_mean(model(inputs.input_ids		,					attention_mask=inputs.attention_mask   )[0]		,					axis=-1   )
										lowercase :							List[Any]    		=tf.constant(
										    [
										        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
										        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
										        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
										        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
										    ]   )
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__		,					atol=1E-4   )   )

										lowercase :							Optional[int]    		=tf.function(UpperCAmelCase__		,					jit_compile=UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=tf.math.reduce_mean(xla_generate(inputs.input_ids		,					attention_mask=inputs.attention_mask   )[0]		,					axis=-1   )
										self.assertTrue(np.allclose(UpperCAmelCase__		,					UpperCAmelCase__		,					atol=1E-4   )   )





@require_tf
@slow
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@property
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							int    		='''facebook/opt-125m'''

										lowercase :							Tuple    		=[
										    '''Today is a beautiful day and I want to''',
										    '''In the city of New York, the city''',
										    '''Paris is the capital of France and the capital''',
										    '''Computers and mobile phones have taken over the''',
										]

										lowercase :							Dict    		=[]
										lowercase :							List[Any]    		=GPTaTokenizer.from_pretrained(UpperCAmelCase__   )
										lowercase :							str    		=TFOPTForCausalLM.from_pretrained(UpperCAmelCase__   )

										for prompt in self.prompts:
																lowercase :							Optional[int]    		=tokenizer(UpperCAmelCase__		,					return_tensors='''tf'''   ).input_ids

																lowercase :							Optional[Any]    		=model.generate(UpperCAmelCase__		,					max_length=10   )

																lowercase :							Union[str, Any]    		=tokenizer.batch_decode(UpperCAmelCase__		,					skip_special_tokens=UpperCAmelCase__   )
																predicted_outputs += generated_string

										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''facebook/opt-350m'''

										lowercase :							str    		=GPTaTokenizer.from_pretrained(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=TFOPTForCausalLM.from_pretrained(UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		='''left'''

										# use different length sentences to test batching
										lowercase :							Tuple    		=[
										    '''Hello, my dog is a little''',
										    '''Today, I''',
										]

										lowercase :							Union[str, Any]    		=tokenizer(UpperCAmelCase__		,					return_tensors='''tf'''		,					padding=UpperCAmelCase__   )
										lowercase :							int    		=inputs['''input_ids''']

										lowercase :							Dict    		=model.generate(input_ids=UpperCAmelCase__		,					attention_mask=inputs['''attention_mask''']   )

										lowercase :							Any    		=tokenizer(sentences[0]		,					return_tensors='''tf'''   ).input_ids
										lowercase :							Dict    		=model.generate(input_ids=UpperCAmelCase__   )

										lowercase :							str    		=inputs_non_padded.shape[-1] - tf.math.reduce_sum(
										    tf.cast(inputs['''attention_mask'''][-1]		,					tf.intaa   )   )
										lowercase :							int    		=tokenizer(sentences[1]		,					return_tensors='''tf'''   ).input_ids
										lowercase :							Union[str, Any]    		=model.generate(input_ids=UpperCAmelCase__		,					max_length=model.config.max_length - num_paddings   )

										lowercase :							Union[str, Any]    		=tokenizer.batch_decode(UpperCAmelCase__		,					skip_special_tokens=UpperCAmelCase__   )
										lowercase :							Any    		=tokenizer.decode(output_non_padded[0]		,					skip_special_tokens=UpperCAmelCase__   )
										lowercase :							Dict    		=tokenizer.decode(output_padded[0]		,					skip_special_tokens=UpperCAmelCase__   )

										lowercase :							Dict    		=[
										    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
										    '''Today, I was in the middle of a conversation with a friend about the''',
										]
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					[non_padded_sentence, padded_sentence]   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''facebook/opt-350m'''

										lowercase :							Any    		=[
										    '''Today is a beautiful day and I want to''',
										    '''In the city of San Francisco, the city''',
										    '''Paris is the capital of France and the capital''',
										    '''Computers and mobile phones have taken over the''',
										]

										lowercase :							List[Any]    		=[]
										lowercase :							Tuple    		=GPTaTokenizer.from_pretrained(UpperCAmelCase__   )
										lowercase :							List[Any]    		=TFOPTForCausalLM.from_pretrained(UpperCAmelCase__   )

										for prompt in self.prompts:
																lowercase :							Optional[Any]    		=tokenizer(UpperCAmelCase__		,					return_tensors='''tf'''   ).input_ids

																lowercase :							int    		=model.generate(UpperCAmelCase__		,					max_length=10   )

																lowercase :							Any    		=tokenizer.batch_decode(UpperCAmelCase__		,					skip_special_tokens=UpperCAmelCase__   )
																predicted_outputs += generated_string

										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )








'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCamelCase_    						=       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.
UpperCamelCase_    						=       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.
UpperCamelCase_    						=       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, float]:
						lowercase :							int    		=len([g for position, g in enumerate(__magic_name__						) if g == main_target[position]]						)
						return (item, float(__magic_name__						))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, str]:
						lowercase :							Any    		=random.randint(0   , len(__magic_name__						) - 1						)
						lowercase :							Tuple    		=parent_a[:random_slice] + parent_a[random_slice:]
						lowercase :							List[str]    		=parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]						)				->  str:
						lowercase :							Union[str, Any]    		=list(__magic_name__						)
						if random.uniform(0   , 1						) < MUTATION_PROBABILITY:
												lowercase :							Dict    		=random.choice(__magic_name__						)
						return "".join(__magic_name__						)






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

												lowercase		,	lowercase :							Dict    		=crossover(parent_a[0]   , __magic_name__						)
												# Append new string to the population list.
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
						return pop






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]   , __magic_name__						:     bool = True						)				->  tuple[int, int, str]:

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												lowercase :							List[str]    		=f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
												raise ValueError(__magic_name__						)
						# Verify that the target contains no genes besides the ones inside genes variable.
						lowercase :							Optional[int]    		=sorted({c for c in target if c not in genes}						)
						if not_in_genes_list:
												lowercase :							Dict    		=f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
												raise ValueError(__magic_name__						)

						# Generate random starting population.
						lowercase :							int    		=[]
						for _ in range(__magic_name__						):
												population.append(''''''.join([random.choice(__magic_name__						) for i in range(len(__magic_name__						)						)]						)						)

						# Just some logs to know what the algorithms is doing.
						lowercase		,	lowercase :							Optional[int]    		=0, 0

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

												# 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.
												lowercase :							List[str]    		=[evaluate(__magic_name__   , __magic_name__						) for item in population]

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

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


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





