

"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_lowercase   :	str												=						2_00
# 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.
_lowercase   :	Any												=						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.
_lowercase   :	int												=						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))





def       lowercase__						(       snake_case_   :str			,		snake_case_   :str    ):
	__UpperCAmelCase      		= len([g for position, g in enumerate(snake_case_    ) if g == main_target[position]]    )
	return (item, float(snake_case_    ))





def       lowercase__						(       snake_case_   :str			,		snake_case_   :str    ):
	__UpperCAmelCase      		= random.randint(0			,		len(snake_case_    ) - 1    )
	__UpperCAmelCase      		= parent_a[:random_slice] + parent_a[random_slice:]
	__UpperCAmelCase      		= parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)





def       lowercase__						(       snake_case_   :str			,		snake_case_   :list[str]    ):
	__UpperCAmelCase      		= list(snake_case_    )
	if random.uniform(0			,		1    ) < MUTATION_PROBABILITY:
		__UpperCAmelCase      		= random.choice(snake_case_    )
	return "".join(snake_case_    )





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

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





def       lowercase__						(       snake_case_   :str			,		snake_case_   :list[str]			,		snake_case_   :bool = True    ):

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

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

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

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

		# 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.
		__UpperCAmelCase      		= [evaluate(snake_case_			,		snake_case_    ) for item in population]

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

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


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


from __future__ import annotations

import unittest

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

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


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)




class   __lowercase						:






	def __init__(self				: Dict      ,	snake_case				: Dict      ,	snake_case				: List[Any]=13      ,	snake_case				: int=7      ,	snake_case				: List[str]=True      ,	snake_case				: List[Any]=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Dict=True      ,	snake_case				: Optional[Any]=99      ,	snake_case				: str=32      ,	snake_case				: Any=2      ,	snake_case				: Optional[Any]=4      ,	snake_case				: int=37      ,	snake_case				: Dict="gelu"      ,	snake_case				: Dict=0.1      ,	snake_case				: Dict=0.1      ,	snake_case				: Optional[int]=512      ,	snake_case				: Dict=16      ,	snake_case				: Union[str, Any]=2      ,	snake_case				: Dict=0.02      ,	snake_case				: str=3      ,	snake_case				: str=4      ,	snake_case				: Optional[Any]=None      ,	snake_case				: str=0      ,	) ->			Optional[Any]:
							_lowercase       :	int           =   parent
							_lowercase       :	Tuple           =   batch_size
							_lowercase       :	str           =   seq_length
							_lowercase       :	Optional[Any]           =   is_training
							_lowercase       :	List[Any]           =   use_input_mask
							_lowercase       :	Tuple           =   use_token_type_ids
							_lowercase       :	List[Any]           =   use_labels
							_lowercase       :	List[str]           =   vocab_size
							_lowercase       :	Dict           =   hidden_size
							_lowercase       :	str           =   num_hidden_layers
							_lowercase       :	Tuple           =   num_attention_heads
							_lowercase       :	Dict           =   intermediate_size
							_lowercase       :	List[Any]           =   hidden_act
							_lowercase       :	Optional[Any]           =   hidden_dropout_prob
							_lowercase       :	Dict           =   attention_probs_dropout_prob
							_lowercase       :	int           =   max_position_embeddings
							_lowercase       :	Tuple           =   type_vocab_size
							_lowercase       :	List[str]           =   type_sequence_label_size
							_lowercase       :	str           =   initializer_range
							_lowercase       :	Tuple           =   num_labels
							_lowercase       :	List[Any]           =   num_choices
							_lowercase       :	Dict           =   scope
							_lowercase       :	Optional[Any]           =   projection_dim






	def       _a(self				: Tuple    ) ->			List[str]:
							_lowercase       :	Optional[int]           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size    )

							_lowercase       :	Optional[int]           =   None
							if self.use_input_mask:
													# follow test_modeling_tf_ctrl.py
													_lowercase       :	List[Any]           =   random_attention_mask([self.batch_size, self.seq_length]    )

							_lowercase       :	List[Any]           =   None
							if self.use_token_type_ids:
													_lowercase       :	List[Any]           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size    )

							_lowercase       :	Tuple           =   None
							_lowercase       :	Optional[Any]           =   None
							_lowercase       :	Optional[Any]           =   None
							if self.use_labels:
													_lowercase       :	Optional[Any]           =   ids_tensor([self.batch_size]      ,	self.type_sequence_label_size    )
													_lowercase       :	Union[str, Any]           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.num_labels    )
													_lowercase       :	List[str]           =   ids_tensor([self.batch_size]      ,	self.num_choices    )

							_lowercase       :	Union[str, Any]           =   BertConfig(
							    vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	is_decoder=snake_case      ,	initializer_range=self.initializer_range      ,	)
							_lowercase       :	Any           =   DPRConfig(projection_dim=self.projection_dim      ,	**config.to_dict()    )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






	def       _a(self				: Union[str, Any]      ,	snake_case				: Any      ,	snake_case				: Tuple      ,	snake_case				: str      ,	snake_case				: str      ,	snake_case				: Union[str, Any]      ,	snake_case				: Tuple      ,	snake_case				: str    ) ->			List[str]:
							_lowercase       :	List[Any]           =   TFDPRContextEncoder(config=snake_case    )
							_lowercase       :	Dict           =   model(snake_case      ,	attention_mask=snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	Optional[int]           =   model(snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	Union[str, Any]           =   model(snake_case    )
							self.parent.assertEqual(result.pooler_output.shape      ,	(self.batch_size, self.projection_dim or self.hidden_size)    )






	def       _a(self				: List[Any]      ,	snake_case				: List[str]      ,	snake_case				: Tuple      ,	snake_case				: Any      ,	snake_case				: int      ,	snake_case				: Optional[int]      ,	snake_case				: Any      ,	snake_case				: Union[str, Any]    ) ->			int:
							_lowercase       :	Optional[Any]           =   TFDPRQuestionEncoder(config=snake_case    )
							_lowercase       :	int           =   model(snake_case      ,	attention_mask=snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	str           =   model(snake_case      ,	token_type_ids=snake_case    )
							_lowercase       :	str           =   model(snake_case    )
							self.parent.assertEqual(result.pooler_output.shape      ,	(self.batch_size, self.projection_dim or self.hidden_size)    )






	def       _a(self				: Union[str, Any]      ,	snake_case				: Any      ,	snake_case				: int      ,	snake_case				: List[str]      ,	snake_case				: Union[str, Any]      ,	snake_case				: Tuple      ,	snake_case				: Optional[Any]      ,	snake_case				: Union[str, Any]    ) ->			Any:
							_lowercase       :	Any           =   TFDPRReader(config=snake_case    )
							_lowercase       :	Optional[Any]           =   model(snake_case      ,	attention_mask=snake_case    )

							self.parent.assertEqual(result.start_logits.shape      ,	(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.end_logits.shape      ,	(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.relevance_logits.shape      ,	(self.batch_size,)    )






	def       _a(self				: int    ) ->			Optional[Any]:
							_lowercase       :	Union[str, Any]           =   self.prepare_config_and_inputs()
							(
							    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    (
							    _lowercase
							)      ,    
							)       :	int           =   config_and_inputs
							_lowercase       :	int           =   {"input_ids": input_ids}
							return config, inputs_dict




@require_tf
class   __lowercase						(						__snake_case   , __snake_case   , unittest.TestCase       ):
	_A       =   (
	    (
	        TFDPRContextEncoder,
	        TFDPRQuestionEncoder,
	        TFDPRReader,
	    )
	    if is_tf_available()
	    else ()
	)
	_A       =   {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False






	def       _a(self				: str    ) ->			List[str]:
							_lowercase       :	List[Any]           =   TFDPRModelTester(self    )
							_lowercase       :	Any           =   ConfigTester(self      ,	config_class=snake_case      ,	hidden_size=37    )






	def       _a(self				: str    ) ->			Union[str, Any]:
							self.config_tester.run_common_tests()






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	List[str]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*snake_case    )






	def       _a(self				: Dict    ) ->			Optional[Any]:
							_lowercase       :	str           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*snake_case    )






	def       _a(self				: List[Any]    ) ->			Any:
							_lowercase       :	Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*snake_case    )






	@slow
	def       _a(self				: int    ) ->			str:
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	str           =   TFDPRContextEncoder.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )

							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	List[str]           =   TFDPRContextEncoder.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )

							for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	int           =   TFDPRQuestionEncoder.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )

							for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	Any           =   TFDPRReader.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )




@require_tf
class   __lowercase						(						unittest.TestCase       ):






	@slow
	def       _a(self				: Dict    ) ->			Any:
							_lowercase       :	Any           =   TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"    )

							_lowercase       :	Optional[int]           =   tf.constant(
							    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]    )  # [CLS] hello, is my dog cute? [SEP]
							_lowercase       :	List[Any]           =   model(snake_case    )[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							_lowercase       :	Optional[Any]           =   tf.constant(
							    [
							        [
							            0.03_23_62_53,
							            0.12_75_33_35,
							            0.16_81_85_09,
							            0.00_27_97_86,
							            0.3_89_69_33,
							            0.24_26_49_45,
							            0.2_17_89_71,
							            -0.02_33_52_27,
							            -0.08_48_19_59,
							            -0.14_32_41_17,
							        ]
							    ]    )
							self.assertTrue(numpy.allclose(output[:, :10].numpy()      ,	expected_slice.numpy()      ,	atol=1e-4    )    )




