





"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCamelCase__				  =   logging.get_logger(__name__)


lowerCamelCase__				  =   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase__				  =   {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

lowerCamelCase__				  =   {
    "allenai/led-base-16384": 1_6384,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 					lowercase__    (					)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       (
          list(range(ord("!"			)		,ord("~"			) + 1			)			) + list(range(ord("¡"			)		,ord("¬"			) + 1			)			) + list(range(ord("®"			)		,ord("ÿ"			) + 1			)			)
      )
      _UpperCamelCase      :	List[str]						=       bs[:]
      _UpperCamelCase      :	str						=       0
      for b in range(2**8			):
            if b not in bs:
                  bs.append(lowercase_			)
                  cs.append(2**8 + n			)
                  n += 1
      _UpperCamelCase      :	str						=       [chr(lowercase_			) for n in cs]
      return dict(zip(lowercase_		,lowercase_			)			)




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       set()
      _UpperCamelCase      :	Optional[Any]						=       word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)			)
            _UpperCamelCase      :	Dict						=       char
      return pairs



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:Tuple	     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					["input_ids", "attention_mask"]

  def __init__(		self   :       Optional[Any]					,   __a   :       int					,   __a   :       Optional[int]					,   __a   :       Optional[int]="replace"					,   __a   :       Any="<s>"					,   __a   :       Dict="</s>"					,   __a   :       List[str]="</s>"					,   __a   :       int="<s>"					,   __a   :       Any="<unk>"					,   __a   :       str="<pad>"					,   __a   :       Any="<mask>"					,   __a   :       List[Any]=False					,   **__a   :       List[str]					,   )		->				str:
        _UpperCamelCase      :	Tuple						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else bos_token
        _UpperCamelCase      :	List[str]						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else eos_token
        _UpperCamelCase      :	Tuple						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else sep_token
        _UpperCamelCase      :	str						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else cls_token
        _UpperCamelCase      :	Any						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else unk_token
        _UpperCamelCase      :	List[Any]						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCamelCase      :	int						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else mask_token

        super().__init__(
            errors=__a					,   bos_token=__a					,   eos_token=__a					,   unk_token=__a					,   sep_token=__a					,   cls_token=__a					,   pad_token=__a					,   mask_token=__a					,   add_prefix_space=__a					,   **__a					,   )

        with open(__a					,   encoding="utf-8"	) as vocab_handle:
              _UpperCamelCase      :	int						=       json.load(__a	)
        _UpperCamelCase      :	str						=       {v: k for k, v in self.encoder.items()}
        _UpperCamelCase      :	int						=       errors  # how to handle errors in decoding
        _UpperCamelCase      :	str						=       bytes_to_unicode()
        _UpperCamelCase      :	Optional[Any]						=       {v: k for k, v in self.byte_encoder.items()}
        with open(__a					,   encoding="utf-8"	) as merges_handle:
              _UpperCamelCase      :	List[Any]						=       merges_handle.read().split("\n"	)[1:-1]
        _UpperCamelCase      :	Optional[int]						=       [tuple(merge.split()	) for merge in bpe_merges]
        _UpperCamelCase      :	List[Any]						=       dict(zip(__a					,   range(len(__a	)	)	)	)
        _UpperCamelCase      :	Any						=       {}
        _UpperCamelCase      :	List[Any]						=       add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _UpperCamelCase      :	Optional[Any]						=       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)

  @property
  # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
        return len(self.encoder	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Tuple:
        return dict(self.encoder					,   **self.added_tokens_encoder	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       Optional[Any]	)		->				Tuple:
        if token in self.cache:
              return self.cache[token]
        _UpperCamelCase      :	Optional[int]						=       tuple(__a	)
        _UpperCamelCase      :	List[Any]						=       get_pairs(__a	)

        if not pairs:
              return token

        while True:
              _UpperCamelCase      :	List[str]						=       min(__a					,   key=lambda __a	: self.bpe_ranks.get(__a					,   float("inf"	)	)	)
              if bigram not in self.bpe_ranks:
                    break
              _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       bigram
              _UpperCamelCase      :	Dict						=       []
              _UpperCamelCase      :	Optional[Any]						=       0
              while i < len(__a	):
                    try:
                          _UpperCamelCase      :	Dict						=       word.index(__a					,   __a	)
                    except ValueError:
                          new_word.extend(word[i:]	)
                          break
                    else:
                          new_word.extend(word[i:j]	)
                          _UpperCamelCase      :	str						=       j

                    if word[i] == first and i < len(__a	) - 1 and word[i + 1] == second:
                          new_word.append(first + second	)
                          i += 2
                    else:
                          new_word.append(word[i]	)
                          i += 1
              _UpperCamelCase      :	str						=       tuple(__a	)
              _UpperCamelCase      :	Optional[int]						=       new_word
              if len(__a	) == 1:
                    break
              else:
                    _UpperCamelCase      :	Any						=       get_pairs(__a	)
        _UpperCamelCase      :	str						=       " ".join(__a	)
        _UpperCamelCase      :	Dict						=       word
        return word

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[str]	)		->				Tuple:
        _UpperCamelCase      :	str						=       []
        for token in re.findall(self.pat					,   __a	):
              _UpperCamelCase      :	List[str]						=       "".join(
                  self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__a	).split(" "	)	)
        return bpe_tokens

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       int	)		->				Tuple:
        return self.encoder.get(__a					,   self.encoder.get(self.unk_token	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       Dict	)		->				Union[str, Any]:
        return self.decoder.get(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       Tuple	)		->				str:
        _UpperCamelCase      :	Tuple						=       "".join(__a	)
        _UpperCamelCase      :	List[Any]						=       bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"					,   errors=self.errors	)
        return text

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       str					,   __a   :       Optional[str] = None	)		->				Tuple[str]:
        if not os.path.isdir(__a	):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
              return
        _UpperCamelCase      :	Tuple						=       os.path.join(
            __a					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
        _UpperCamelCase      :	Dict						=       os.path.join(
            __a					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

        with open(__a					,   "w"					,   encoding="utf-8"	) as f:
              f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=__a					,   ensure_ascii=__a	) + "\n"	)

        _UpperCamelCase      :	str						=       0
        with open(__a					,   "w"					,   encoding="utf-8"	) as writer:
              writer.write("#version: 0.2\n"	)
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda __a	: kv[1]	):
                    if index != token_index:
                          logger.warning(
                              F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                              " Please check that the tokenizer is not corrupted!"	)
                          _UpperCamelCase      :	Union[str, Any]						=       token_index
                    writer.write(" ".join(__a	) + "\n"	)
                    index += 1

        return vocab_file, merge_file

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:
        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCamelCase      :	Tuple						=       [self.cls_token_id]
        _UpperCamelCase      :	List[Any]						=       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None					,   __a   :       bool = False	)		->				List[int]:
        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__a					,   token_ids_a=__a					,   already_has_special_tokens=__a	)

        if token_ids_a is None:
              return [1] + ([0] * len(__a	)) + [1]
        return [1] + ([0] * len(__a	)) + [1, 1] + ([0] * len(__a	)) + [1]

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:
        _UpperCamelCase      :	Optional[int]						=       [self.sep_token_id]
        _UpperCamelCase      :	List[Any]						=       [self.cls_token_id]

        if token_ids_a is None:
              return len(cls + token_ids_a + sep	) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       str					,   __a   :       int=False					,   **__a   :       List[Any]	)		->				List[Any]:
        _UpperCamelCase      :	Optional[Any]						=       kwargs.pop("add_prefix_space"					,   self.add_prefix_space	)
        if (is_split_into_words or add_prefix_space) and (len(__a	) > 0 and not text[0].isspace()):
              _UpperCamelCase      :	List[Any]						=       " " + text
        return (text, kwargs)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       Union[Dict[str, EncodedInput], BatchEncoding]					,   __a   :       Optional[int] = None					,   __a   :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,   __a   :       Optional[int] = None					,   __a   :       Optional[bool] = None					,   )		->				dict:
        _UpperCamelCase      :	Any						=       super()._pad(
            encoded_inputs=__a					,   max_length=__a					,   padding_strategy=__a					,   pad_to_multiple_of=__a					,   return_attention_mask=__a					,   )

        # Load from model defaults
        if return_attention_mask is None:
              _UpperCamelCase      :	str						=       "attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
              _UpperCamelCase      :	Dict						=       encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              _UpperCamelCase      :	str						=       len(encoded_inputs["global_attention_mask"]	) != len(__a	)

              if needs_to_be_padded:
                    _UpperCamelCase      :	List[str]						=       len(__a	) - len(encoded_inputs["global_attention_mask"]	)

                    if self.padding_side == "right":
                          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                          _UpperCamelCase      :	str						=       (
                              encoded_inputs["global_attention_mask"] + [-1] * difference
                          )
                    elif self.padding_side == "left":
                          _UpperCamelCase      :	List[Any]						=       [-1] * difference + encoded_inputs[
                              "global_attention_mask"
                          ]
                    else:
                          raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

        return encoded_inputs







"""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, 1000))




def 					lowercase__    (					lowercase_		,lowercase_			)					->		tuple[str, float]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       len([g for position, g in enumerate(lowercase_			) if g == main_target[position]]			)
      return (item, float(lowercase_			))




def 					lowercase__    (					lowercase_		,lowercase_			)					->		tuple[str, str]:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       random.randint(0		,len(lowercase_			) - 1			)
      _UpperCamelCase      :	Dict						=       parent_a[:random_slice] + parent_a[random_slice:]
      _UpperCamelCase      :	Tuple						=       parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	int						=       list(lowercase_			)
      if random.uniform(0		,1			) < MUTATION_PROBABILITY:
            _UpperCamelCase      :	int						=       random.choice(lowercase_			)
      return "".join(lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       []
      # Generate more children proportionally to the fitness score.
      _UpperCamelCase      :	List[str]						=       int(parent_a[1] * 100			) + 1
      _UpperCamelCase      :	Union[str, Any]						=       10 if child_n >= 10 else child_n
      for _ in range(lowercase_			):
            _UpperCamelCase      :	Dict						=       population_score[random.randint(0		,lowercase_			)][0]

            _UpperCamelCase,  _UpperCamelCase      :	Dict						=       crossover(parent_a[0]		,lowercase_			)
            # Append new string to the population list.
            pop.append(mutate(lowercase_		,lowercase_			)			)
            pop.append(mutate(lowercase_		,lowercase_			)			)
      return pop




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_ = True			)					->		tuple[int, int, str]:




      """simple docstring"""

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

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

      # Just some logs to know what the algorithms is doing.
      _UpperCamelCase,  _UpperCamelCase      :	str						=       0, 0

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

            # 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      :	int						=       [evaluate(lowercase_		,lowercase_			) for item in population]

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

            # This is selection
            for i in range(lowercase_			):
                  population.extend(select(population_score[int(lowercase_			)]		,lowercase_		,lowercase_			)			)
                  # 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(lowercase_			) > 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}"""
  )
