



'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 tensorflow as tf

			from transformers import (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)

class      __magic_name__						:







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







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[Any]				=							ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size		)

									_a					:				Optional[Any]				=							None
									if self.use_input_mask:
											_a					:				Any				=							random_attention_mask([self.batch_size, self.seq_length]		)

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

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

									_a					:				List[Any]				=							DebertaVaConfig(
									    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     ,relative_attention=self.relative_attention     ,position_biased_input=self.position_biased_input     ,initializer_range=self.initializer_range     ,return_dict=_UpperCAmelCase     ,)

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







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	str		):
									_a					:				List[str]				=							TFDebertaVaModel(config=_UpperCAmelCase		)
									_a					:				Any				=							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									_a					:				Any				=							[input_ids, input_mask]
									_a					:				Dict				=							model(_UpperCAmelCase		)

									_a					:				str				=							model(_UpperCAmelCase		)

									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]		):
									_a					:				Optional[Any]				=							TFDebertaVaForMaskedLM(config=_UpperCAmelCase		)
									_a					:				List[Any]				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_a					:				Optional[int]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	List[Any]		):
									_a					:				List[Any]				=							self.num_labels
									_a					:				int				=							TFDebertaVaForSequenceClassification(config=_UpperCAmelCase		)
									_a					:				Tuple				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_a					:				Dict				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[str]		):
									_a					:				str				=							self.num_labels
									_a					:				Any				=							TFDebertaVaForTokenClassification(config=_UpperCAmelCase		)
									_a					:				List[Any]				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_a					:				List[Any]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels)		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				List[str]				=							TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase		)
									_a					:				str				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_a					:				Optional[Any]				=							model(_UpperCAmelCase		)
									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)		)







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Dict				=							self.prepare_config_and_inputs()
									(
									    _a 
									)					:				int				=							config_and_inputs
									_a					:				Optional[int]				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict

@require_tf
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   str           = (
							    (
							        TFDebertaVaModel,
							        TFDebertaVaForMaskedLM,
							        TFDebertaVaForQuestionAnswering,
							        TFDebertaVaForSequenceClassification,
							        TFDebertaVaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							lowerCAmelCase	:   str           = (
							    {
							        'feature-extraction': TFDebertaVaModel,
							        'fill-mask': TFDebertaVaForMaskedLM,
							        'question-answering': TFDebertaVaForQuestionAnswering,
							        'text-classification': TFDebertaVaForSequenceClassification,
							        'token-classification': TFDebertaVaForTokenClassification,
							        'zero-shot': TFDebertaVaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Optional[Any]           = False







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Dict				=							TFDebertaVaModelTester(self		)
									_a					:				Optional[int]				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	Dict		):
									self.config_tester.run_common_tests()







							def 							__lowercase      (							self :	int		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Any				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[str]				=							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'		)
									self.assertIsNotNone(_UpperCAmelCase		)

@require_tf
class      __magic_name__						(   unittest.TestCase       ):







							@unittest.skip(reason='Model not available yet'		)
							def 							__lowercase      (							self :	Union[str, Any]		):
									pass







							@slow
							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'		)
									_a					:				List[Any]				=							tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
									_a					:				Any				=							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									_a					:				Tuple				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)[0]

									_a					:				Dict				=							tf.constant(
									    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]		)
									tf.debugging.assert_near(output[:, 1:4, 1:4]     ,_UpperCAmelCase     ,atol=1E-4		)









'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase						      =       200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase						      =       50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase						      =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))



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



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



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



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

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



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

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

		# Generate random starting population.
		_a					:				Union[str, Any]				=							[]
		for _ in range(lowerCAmelCase_  ):
				population.append(''.join([random.choice(lowerCAmelCase_  ) for i in range(len(lowerCAmelCase_  )  )]  )  )

		# Just some logs to know what the algorithms is doing.
		_a	, _a					:				Union[str, Any]				=							0, 0

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

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

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

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

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

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

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


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




