
"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCamelCase__ :       List[str]						     =			"platform"

					import jax
					import jax.numpy as jnp

					from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
					    FlaxBlenderbotSmallForConditionalGeneration,
					    FlaxBlenderbotSmallModel,
					    shift_tokens_right,
					)





def __A						(   a_ :							Dict		,		a_ :							List[str]		,		a_ :							Optional[Any]=None		,		a_ :							Optional[Any]=None		,		a_ :							List[Any]=None		,		a_ :							Tuple=None		,		a_ :							List[Any]=None		,		a_ :							Optional[int]=None		,		)->							Dict:




		'''simple docstring'''






		if attention_mask is None:
				SCREAMING_SNAKE_CASE       :     List[Any]				= np.where(input_ids != config.pad_token_id		,		1		,		0     )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= np.where(decoder_input_ids != config.pad_token_id		,		1		,		0     )
		if head_mask is None:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE       :     Tuple				= np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}




class 				lowercase__:





	def __init__(						self    :str		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Tuple=13		, lowerCamelCase_    :List[str]=7		, lowerCamelCase_    :int=True		, lowerCamelCase_    :int=False		, lowerCamelCase_    :List[Any]=99		, lowerCamelCase_    :Optional[int]=16		, lowerCamelCase_    :List[Any]=2		, lowerCamelCase_    :List[Any]=4		, lowerCamelCase_    :Dict=4		, lowerCamelCase_    :Optional[int]="gelu"		, lowerCamelCase_    :Dict=0.1		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :Tuple=2		, lowerCamelCase_    :List[Any]=1		, lowerCamelCase_    :List[str]=0		, lowerCamelCase_    :Any=0.0_2		, )							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= parent
			SCREAMING_SNAKE_CASE       :     Dict				= batch_size
			SCREAMING_SNAKE_CASE       :     Tuple				= seq_length
			SCREAMING_SNAKE_CASE       :     Dict				= is_training
			SCREAMING_SNAKE_CASE       :     List[str]				= use_labels
			SCREAMING_SNAKE_CASE       :     Tuple				= vocab_size
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Any				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[int]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Dict				= hidden_act
			SCREAMING_SNAKE_CASE       :     str				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Tuple				= eos_token_id
			SCREAMING_SNAKE_CASE       :     List[Any]				= pad_token_id
			SCREAMING_SNAKE_CASE       :     Dict				= bos_token_id
			SCREAMING_SNAKE_CASE       :     Dict				= initializer_range





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size					)		, 3		, self.vocab_size					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		, dtype=np.intaa					))		, -1					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= shift_tokens_right(lowerCamelCase_		, 1		, 2					)

			SCREAMING_SNAKE_CASE       :     Any				= BlenderbotSmallConfig(
			    vocab_size=self.vocab_size		, d_model=self.hidden_size		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_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		, initializer_range=self.initializer_range		, use_cache=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     int				= prepare_blenderbot_inputs_dict(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			return config, inputs_dict





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.prepare_config_and_inputs()
			return config, inputs_dict





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= 20
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model_class_name(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model.encode(inputs_dict['''input_ids''']					)

			SCREAMING_SNAKE_CASE       :     Any				= (
			    inputs_dict['''decoder_input_ids'''],
			    inputs_dict['''decoder_attention_mask'''],
			)

			SCREAMING_SNAKE_CASE       :     Dict				= model.init_cache(decoder_input_ids.shape[0]		, lowerCamelCase_		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		, dtype='''i4'''					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		, )
			SCREAMING_SNAKE_CASE       :     str				= model.decode(
			    decoder_input_ids[:, :-1]		, lowerCamelCase_		, decoder_attention_mask=lowerCamelCase_		, past_key_values=lowerCamelCase_		, decoder_position_ids=lowerCamelCase_		, )

			SCREAMING_SNAKE_CASE       :     str				= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='''i4'''					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= model.decode(
			    decoder_input_ids[:, -1:]		, lowerCamelCase_		, decoder_attention_mask=lowerCamelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_position_ids=lowerCamelCase_		, )

			SCREAMING_SNAKE_CASE       :     Dict				= model.decode(lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
			self.parent.assertTrue(diff < 1E-3		, msg=f"Max diff is {diff}"					)







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 20
			SCREAMING_SNAKE_CASE       :     Tuple				= model_class_name(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= model.encode(inputs_dict['''input_ids''']					)

			SCREAMING_SNAKE_CASE       :     str				= (
			    inputs_dict['''decoder_input_ids'''],
			    inputs_dict['''decoder_attention_mask'''],
			)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
			    ]		, axis=-1		, )

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model.init_cache(decoder_input_ids.shape[0]		, lowerCamelCase_		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		, )

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model.decode(
			    decoder_input_ids[:, :-1]		, lowerCamelCase_		, decoder_attention_mask=lowerCamelCase_		, past_key_values=lowerCamelCase_		, decoder_position_ids=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Optional[int]				= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='''i4'''					)
			SCREAMING_SNAKE_CASE       :     Dict				= model.decode(
			    decoder_input_ids[:, -1:]		, lowerCamelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_attention_mask=lowerCamelCase_		, decoder_position_ids=lowerCamelCase_		, )

			SCREAMING_SNAKE_CASE       :     int				= model.decode(lowerCamelCase_		, lowerCamelCase_		, decoder_attention_mask=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
			self.parent.assertTrue(diff < 1E-3		, msg=f"Max diff is {diff}"					)





@require_flax
class 				lowercase__(				unittest.TestCase   ):
	UpperCamelCase				=  99





	def   __lowerCAmelCase (						self    :Any					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]		, dtype=np.intaa		, )

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= input_ids.shape[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= BlenderbotSmallConfig(
			    vocab_size=self.vocab_size		, d_model=24		, encoder_layers=2		, decoder_layers=2		, encoder_attention_heads=2		, decoder_attention_heads=2		, encoder_ffn_dim=32		, decoder_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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self._get_config_and_data()
			SCREAMING_SNAKE_CASE       :     List[str]				= FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= lm_model(input_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs['''logits'''].shape		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= BlenderbotSmallConfig(
			    vocab_size=self.vocab_size		, d_model=14		, encoder_layers=2		, decoder_layers=2		, encoder_attention_heads=2		, decoder_attention_heads=2		, encoder_ffn_dim=8		, decoder_ffn_dim=8		, max_position_embeddings=48		, )
			SCREAMING_SNAKE_CASE       :     int				= FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		, dtype=np.intaa					)
			SCREAMING_SNAKE_CASE       :     str				= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		, dtype=np.intaa					)
			SCREAMING_SNAKE_CASE       :     List[str]				= lm_model(input_ids=lowerCamelCase_		, decoder_input_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= (*summary.shape, config.vocab_size)
			self.assertEqual(outputs['''logits'''].shape		, lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		, dtype=np.intaa					)
			SCREAMING_SNAKE_CASE       :     Tuple				= shift_tokens_right(lowerCamelCase_		, 1		, 2					)
			SCREAMING_SNAKE_CASE       :     List[str]				= np.equal(lowerCamelCase_		, 1					).astype(np.floataa					).sum()
			SCREAMING_SNAKE_CASE       :     Dict				= np.equal(lowerCamelCase_		, 1					).astype(np.floataa					).sum()
			self.assertEqual(shifted.shape		, input_ids.shape					)
			self.assertEqual(lowerCamelCase_		, n_pad_before - 1					)
			self.assertTrue(np.equal(shifted[:, 0]		, 2					).all()					)





@require_flax
class 				lowercase__(				_UpperCAmelCase		,						unittest.TestCase		,						_UpperCAmelCase   ):
	UpperCamelCase				=  True
	UpperCamelCase				=  (
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	UpperCamelCase				=  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= FlaxBlenderbotSmallModelTester(self					)





	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							SCREAMING_SNAKE_CASE       :     Dict				= self._prepare_for_class(lowerCamelCase_		, lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     str				= model_class(lowerCamelCase_					)

							@jax.jit
							def encode_jitted(lowerCamelCase_    :List[str]		, lowerCamelCase_    :Union[str, Any]=None		, **lowerCamelCase_    :Union[str, Any]					):
									return model.encode(input_ids=lowerCamelCase_		, attention_mask=lowerCamelCase_					)

							with self.subTest('''JIT Enabled'''					):
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= encode_jitted(**lowerCamelCase_					).to_tuple()

							with self.subTest('''JIT Disabled'''					):
									with jax.disable_jit():
											SCREAMING_SNAKE_CASE       :     Tuple				= encode_jitted(**lowerCamelCase_					).to_tuple()

							self.assertEqual(len(lowerCamelCase_					)		, len(lowerCamelCase_					)					)
							for jitted_output, output in zip(lowerCamelCase_		, lowerCamelCase_					):
									self.assertEqual(jitted_output.shape		, output.shape					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= model_class(lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Any				= model.encode(inputs_dict['''input_ids''']		, inputs_dict['''attention_mask''']					)

							SCREAMING_SNAKE_CASE       :     Dict				= {
							    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
							    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
							    '''encoder_outputs''': encoder_outputs,
							}

							@jax.jit
							def decode_jitted(lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[Any]					):
									return model.decode(
									    decoder_input_ids=lowerCamelCase_		, decoder_attention_mask=lowerCamelCase_		, encoder_outputs=lowerCamelCase_		, )

							with self.subTest('''JIT Enabled'''					):
									SCREAMING_SNAKE_CASE       :     str				= decode_jitted(**lowerCamelCase_					).to_tuple()

							with self.subTest('''JIT Disabled'''					):
									with jax.disable_jit():
											SCREAMING_SNAKE_CASE       :     List[Any]				= decode_jitted(**lowerCamelCase_					).to_tuple()

							self.assertEqual(len(lowerCamelCase_					)		, len(lowerCamelCase_					)					)
							for jitted_output, output in zip(lowerCamelCase_		, lowerCamelCase_					):
									self.assertEqual(jitted_output.shape		, output.shape					)







	@slow
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Dict				= model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''					)
					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
					SCREAMING_SNAKE_CASE       :     str				= np.ones((1, 1)					) * model.config.eos_token_id
					SCREAMING_SNAKE_CASE       :     Dict				= model(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ :       Optional[int]						     =			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





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




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

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

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"     )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(a_     ) > N_POPULATION:
								break


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





