





'''simple docstring'''






from __future__ import annotations

import random

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





      """simple docstring"""


      _lowerCAmelCase					=				len([g for position, g in enumerate(lowerCAmelCase    ) if g == main_target[position]]    )
      return (item, float(lowerCAmelCase    ))
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				random.randint(0				,      len(lowerCAmelCase    ) - 1    )
      _lowerCAmelCase					=				parent_a[:random_slice] + parent_a[random_slice:]
      _lowerCAmelCase					=				parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				list(lowerCAmelCase    )
      if random.uniform(0				,      1    ) < MUTATION_PROBABILITY:
            _lowerCAmelCase					=				random.choice(lowerCAmelCase    )
      return "".join(lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      ):





      """simple docstring"""


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

            _lowerCAmelCase	,				_lowerCAmelCase					=				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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase = True    ):





      """simple docstring"""


      if N_POPULATION < N_SELECTED:
            _lowerCAmelCase					=				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.
      _lowerCAmelCase					=				sorted({c for c in target if c not in genes}    )
      if not_in_genes_list:
            _lowerCAmelCase					=				f"{not_in_genes_list} is not in genes list, evolution cannot converge"
            raise ValueError(lowerCAmelCase    )

      # Generate random starting population.
      _lowerCAmelCase					=				[]
      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.
      _lowerCAmelCase	,				_lowerCAmelCase					=				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.
            _lowerCAmelCase					=				[evaluate(lowerCAmelCase				,      lowerCAmelCase    ) for item in population]

            # Check if there is a matching evolution.
            _lowerCAmelCase					=				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.
            _lowerCAmelCase					=				population[: int(N_POPULATION / 3    )]
            population.clear()
            population.extend(lowerCAmelCase    )
            # Normalize population score to be between 0 and 1.
            _lowerCAmelCase					=				[
                (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__":
      A__							:  Any    =(
          '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
      )
      A__							:  int    =list(
          ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
          '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
      )
      A__  ,		A__  ,		A__							:  Union[str, Any]    =basic(target_str, genes_list)
      print(
          F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
      )













'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  DebertaTokenizer
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     Union[str, Any]			=  DebertaTokenizerFast




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """[UNK]"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Any    )							-> Dict:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tokenizer("""Hello"""     ,     """World"""    )
            _lowerCAmelCase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]     ,     __snake_case    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




      @slow
      def 							lowercase__       (     self     :					Any    )							-> str:
            _lowerCAmelCase					=				[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case    )
                  _lowerCAmelCase					=				[tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  _lowerCAmelCase					=				{
                      """input_ids""": [
                          [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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],
                          [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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],
                          [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                      ],
                      """token_type_ids""": [
                          [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, 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, 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, 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, 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]
                      ]
                  }
                  # fmt: on

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data     ,     __snake_case    )

                  for expected, decoded in zip(__snake_case     ,     __snake_case    ):
                        self.assertEqual(__snake_case     ,     __snake_case    )






