



import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case						:Optional[Any]      		=     get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
class     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    List[Any]											=						XLMProphetNetTokenizer
       UpperCamelCase__  :    List[Any]											=						False
       UpperCamelCase__  :    Optional[Any]											=						True



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           __a						       =				XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE     ,		keep_accents=__SCREAMING_SNAKE_CASE)
           tokenizer.save_pretrained(self.tmpdirname)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				'''[PAD]'''
           __a						       =				0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]     ,		'''[PAD]''')
           self.assertEqual(vocab_keys[1]     ,		'''[CLS]''')
           self.assertEqual(vocab_keys[-1]     ,		'''j''')
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,		1_012)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size     ,		1_012)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE     ,		keep_accents=__SCREAMING_SNAKE_CASE)

           __a						       =				tokenizer.tokenize('''This is a test''')
           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)     ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,		)

           __a						       =				tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE     ,		[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''9''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''é''',
                   '''.''',
               ]     ,		)
           __a						       =				tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE     ,		[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
               ]     ,		)

           __a						       =				tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE     ,		[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''[UNK]''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''[UNK]''',
                   '''.''',
               ]     ,		)



       @cached_property
       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''')



       @slow
       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				'''Hello World!'''
           __a						       =				[35_389, 6_672, 49, 2]
           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE))





       @slow
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				{'''input_ids''': [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__SCREAMING_SNAKE_CASE     ,		model_name='''microsoft/xprophetnet-large-wiki100-cased'''     ,		revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''     ,		)










from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__snake_case						:Optional[int]      		=     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.
__snake_case						:List[str]      		=     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.
__snake_case						:List[Any]      		=     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				len([g for position, g in enumerate(_UpperCAmelCase				) if g == main_target[position]]				)
    return (item, float(_UpperCAmelCase				))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				random.randint(0   , len(_UpperCAmelCase				) - 1				)
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				list(_UpperCAmelCase				)
    if random.uniform(0   , 1				) < MUTATION_PROBABILITY:
        __a						       =				random.choice(_UpperCAmelCase				)
    return "".join(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , ):
    __a						       =				[]
    # Generate more children proportionally to the fitness score.
    __a						       =				int(parent_a[1] * 100				) + 1
    __a						       =				10 if child_n >= 10 else child_n
    for _ in range(_UpperCAmelCase				):
        __a						       =				population_score[random.randint(0   , _UpperCAmelCase				)][0]

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = True				):

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

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

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

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

        # 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						       =				[evaluate(_UpperCAmelCase   , _UpperCAmelCase				) for item in population]

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

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


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





