


import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


class   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     str							    =     ProphetNetTokenizer
       lowerCamelCase__:     Optional[Any]							    =     False

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            super().setUp()

            a_    :     str										=    [
                """[UNK]""",
                """[CLS]""",
                """[SEP]""",
                """[PAD]""",
                """[MASK]""",
                """want""",
                """##want""",
                """##ed""",
                """wa""",
                """un""",
                """runn""",
                """##ing""",
                """,""",
                """low""",
                """lowest""",
            ]
            a_    :     Optional[int]										=    os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["""vocab_file"""]    )
            with open(self.vocab_file      ,							"""w"""      ,							encoding="""utf-8"""    ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     Tuple    ):
            a_    :     List[str]										=    """UNwant\u00E9d,running"""
            a_    :     List[str]										=    """unwanted, running"""
            return input_text, output_text

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     List[Any]										=    self.tokenizer_class(self.vocab_file    )

            a_    :     str										=    tokenizer.tokenize("""UNwant\u00E9d,running"""    )
            self.assertListEqual(lowerCamelCase_      ,							["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]    )
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    )      ,							[9, 6, 7, 1_2, 1_0, 1_1]    )

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Dict										=    BasicTokenizer()

            self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""    )      ,							["""ah""", """\u535A""", """\u63A8""", """zz"""]    )

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     str										=    BasicTokenizer(do_lower_case=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )      ,							["""hello""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )      ,							["""hello"""]    )

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Any										=    BasicTokenizer(do_lower_case=lowerCamelCase_      ,							strip_accents=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )      ,							["""hällo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )      ,							["""h\u00E9llo"""]    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    BasicTokenizer(do_lower_case=lowerCamelCase_      ,							strip_accents=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )      ,							["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )      ,							["""hello"""]    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     str										=    BasicTokenizer(do_lower_case=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )      ,							["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )      ,							["""hello"""]    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[Any]										=    BasicTokenizer(do_lower_case=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )      ,							["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     List[Any]										=    BasicTokenizer(do_lower_case=lowerCamelCase_      ,							strip_accents=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )      ,							["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Optional[Any]										=    BasicTokenizer(do_lower_case=lowerCamelCase_      ,							strip_accents=lowerCamelCase_    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )      ,							["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Dict										=    BasicTokenizer(do_lower_case=lowerCamelCase_      ,							never_split=["""[UNK]"""]    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""    )      ,							["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Any										=    ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

            a_    :     Optional[int]										=    {}
            for i, token in enumerate(lowerCamelCase_    ):
                 a_    :     Union[str, Any]										=    i
            a_    :     Any										=    WordpieceTokenizer(vocab=lowerCamelCase_      ,							unk_token="""[UNK]"""    )

            self.assertListEqual(tokenizer.tokenize(""""""    )      ,							[]    )

            self.assertListEqual(tokenizer.tokenize("""unwanted running"""    )      ,							["""un""", """##want""", """##ed""", """runn""", """##ing"""]    )

            self.assertListEqual(tokenizer.tokenize("""unwantedX running"""    )      ,							["""[UNK]""", """runn""", """##ing"""]    )

       @require_torch
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Any										=    self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""    )

            a_    :     Optional[Any]										=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            a_    :     List[str]										=    [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
            a_    :     List[Any]										=    tokenizer(lowerCamelCase_      ,							padding=lowerCamelCase_      ,							return_tensors="""pt"""    )
            self.assertIsInstance(lowerCamelCase_      ,							lowerCamelCase_    )
            a_    :     Dict										=    list(batch.input_ids.numpy()[0]    )
            self.assertListEqual(lowerCamelCase_      ,							lowerCamelCase_    )

            self.assertEqual((2, 9)      ,							batch.input_ids.shape    )
            self.assertEqual((2, 9)      ,							batch.attention_mask.shape    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.assertTrue(_is_whitespace(""" """    )    )
            self.assertTrue(_is_whitespace("""\t"""    )    )
            self.assertTrue(_is_whitespace("""\r"""    )    )
            self.assertTrue(_is_whitespace("""\n"""    )    )
            self.assertTrue(_is_whitespace("""\u00A0"""    )    )

            self.assertFalse(_is_whitespace("""A"""    )    )
            self.assertFalse(_is_whitespace("""-"""    )    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            self.assertTrue(_is_control("""\u0005"""    )    )

            self.assertFalse(_is_control("""A"""    )    )
            self.assertFalse(_is_control(""" """    )    )
            self.assertFalse(_is_control("""\t"""    )    )
            self.assertFalse(_is_control("""\r"""    )    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            self.assertTrue(_is_punctuation("""-"""    )    )
            self.assertTrue(_is_punctuation("""$"""    )    )
            self.assertTrue(_is_punctuation("""`"""    )    )
            self.assertTrue(_is_punctuation("""."""    )    )

            self.assertFalse(_is_punctuation("""A"""    )    )
            self.assertFalse(_is_punctuation(""" """    )    )

       @slow
       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Any										=    self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""    )

            a_    :     List[Any]										=    tokenizer.encode("""sequence builders"""      ,							add_special_tokens=lowerCamelCase_    )
            a_    :     Optional[Any]										=    tokenizer.encode("""multi-sequence build"""      ,							add_special_tokens=lowerCamelCase_    )

            a_    :     Optional[Any]										=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase_    )
            a_    :     Union[str, Any]										=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      ,							lowerCamelCase_    )

            assert encoded_sentence == text + [1_0_2]
            assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]







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, 1000))



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    len([g for position, g in enumerate(__UpperCamelCase ) if g == main_target[position]] )
     return (item, float(__UpperCamelCase ))



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    random.randint(0  ,  len(__UpperCamelCase ) - 1 )
     a_    :     Tuple										=    parent_a[:random_slice] + parent_a[random_slice:]
     a_    :     Optional[Any]										=    parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[int]										=    list(__UpperCamelCase )
     if random.uniform(0  ,  1 ) < MUTATION_PROBABILITY:
          a_    :     int										=    random.choice(__UpperCamelCase )
     return "".join(__UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_    :     Tuple										=    []
     # Generate more children proportionally to the fitness score.
     a_    :     Any										=    int(parent_a[1] * 1_0_0 ) + 1
     a_    :     int										=    1_0 if child_n >= 1_0 else child_n
     for _ in range(__UpperCamelCase ):
          a_    :     Tuple										=    population_score[random.randint(0  ,  __UpperCamelCase )][0]

          a_			,			a_    :     List[str]										=    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  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase = True ):

     # Verify if N_POPULATION is bigger than N_SELECTED
     if N_POPULATION < N_SELECTED:
          a_    :     Union[str, Any]										=    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_    :     List[str]										=    sorted({c for c in target if c not in genes} )
     if not_in_genes_list:
          a_    :     Optional[Any]										=    F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(__UpperCamelCase )

     # Generate random starting population.
     a_    :     str										=    []
     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_    :     List[str]										=    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_    :     Tuple										=    [evaluate(__UpperCamelCase  ,  __UpperCamelCase ) for item in population]

          # Check if there is a matching evolution.
          a_    :     Optional[Any]										=    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 % 1_0 == 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_    :     List[str]										=    population[: int(N_POPULATION / 3 )]
          population.clear()
          population.extend(__UpperCamelCase )
          # Normalize population score to be between 0 and 1.
          a_    :     Optional[Any]										=    [
              (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__":
       __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}'''
       )



