





"""simple docstring"""


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

lowerCamelCase__				  =   {
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

lowerCamelCase__				  =   {
    "abeja/gpt-neox-japanese-2.7b": 2048,
}




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str:




      """simple docstring"""

      with open(lowerCamelCase__		,"r"		,encoding="utf-8"			) as f:
            _UpperCamelCase      :	List[str]						=       json.loads(f.read()			)

      _UpperCamelCase      :	List[Any]						=       collections.OrderedDict()
      _UpperCamelCase      :	Union[str, Any]						=       collections.OrderedDict()
      _UpperCamelCase      :	Optional[Any]						=       collections.OrderedDict()
      with open(lowerCamelCase__		,"r"		,encoding="utf-8"			) as f:
            _UpperCamelCase      :	Optional[Any]						=       f.readlines()
      _UpperCamelCase      :	List[Any]						=       [[t.rstrip("\n"			)] if (t == "," or "," not in t) else t.rstrip("\n"			).split(","			) for t in token]
      for idx, b in enumerate(lowerCamelCase__			):
            _UpperCamelCase      :	Optional[int]						=       b
            _UpperCamelCase      :	Union[str, Any]						=       idx
            for wd in b:
                  _UpperCamelCase      :	int						=       idx

      return vocab, raw_vocab, ids_to_tokens, emoji



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


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

  def __init__(		self   :       Optional[Any]					,   __a   :       Tuple					,   __a   :       Optional[Any]					,   __a   :       Optional[Any]="<|endoftext|>"					,   __a   :       List[str]="<|endoftext|>"					,   __a   :       Any="<|startoftext|>"					,   __a   :       Any="<|endoftext|>"					,   __a   :       Tuple=False					,   **__a   :       List[str]					,   )		->				Optional[Any]:
        super().__init__(
            unk_token=__a					,   pad_token=__a					,   bos_token=__a					,   eos_token=__a					,   do_clean_text=__a					,   **__a					,   )
        if not os.path.isfile(__a	):
              raise ValueError(
                  F'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
                  " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
        if not os.path.isfile(__a	):
              raise ValueError(
                  F'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
                  " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
        _UpperCamelCase      :	Optional[Any]						=       do_clean_text
        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	int						=       load_vocab_and_emoji(__a					,   __a	)
        _UpperCamelCase      :	str						=       SubWordJapaneseTokenizer(
            vocab=self.vocab					,   ids_to_tokens=self.ids_to_tokens					,   emoji=self.emoji	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				List[str]:
        return len(self.raw_vocab	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				str:
        return dict(self.raw_vocab					,   **self.added_tokens_encoder	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       Tuple	)		->				Optional[Any]:
        return self.subword_tokenizer.tokenize(__a					,   clean=self.do_clean_text	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       Optional[int]	)		->				Dict:
        return self.vocab.get(__a					,   self.vocab.get(self.unk_token	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       List[Any]	)		->				Tuple:
        return self.subword_tokenizer.convert_id_to_token(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       int	)		->				Optional[Any]:
        _UpperCamelCase      :	Tuple						=       "".join(__a	).strip()
        return out_string

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       "Conversation"	)		->				List[int]:
        _UpperCamelCase      :	int						=       []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a					,   add_special_tokens=__a	) + [self.eos_token_id]	)

        if len(__a	) > self.model_max_length:
              _UpperCamelCase      :	Optional[Any]						=       input_ids[-self.model_max_length :]
        return input_ids


  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       str					,   __a   :       Optional[str] = None	)		->				Tuple[str]:
        _UpperCamelCase      :	List[Any]						=       0
        if os.path.isdir(__a	):
              _UpperCamelCase      :	int						=       os.path.join(
                  __a					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
              _UpperCamelCase      :	str						=       os.path.join(
                  __a					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]	)
        else:
              _UpperCamelCase      :	Union[str, Any]						=       (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
              )
              _UpperCamelCase      :	List[Any]						=       (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
              )
        with open(__a					,   "w"					,   encoding="utf-8"	) as writer:
              for token_index, token in self.ids_to_tokens.items():
                    if index != token_index:
                          logger.warning(
                              F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                              " Please check that the vocabulary is not corrupted!"	)
                          _UpperCamelCase      :	Dict						=       token_index
                    writer.write(",".join(__a	) + "\n"	)
                    index += 1
        with open(__a					,   "w"					,   encoding="utf-8"	) as writer:
              json.dump(self.emoji					,   __a	)
        return vocab_file, emoji_file





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  def __init__(		self   :       str					,   __a   :       Dict					,   __a   :       Optional[Any]					,   __a   :       List[Any]	)		->				int:
        _UpperCamelCase      :	Optional[int]						=       vocab  # same as swe
        _UpperCamelCase      :	int						=       ids_to_tokens  # same as bpe
        _UpperCamelCase      :	List[Any]						=       emoji
        _UpperCamelCase      :	List[Any]						=       np.max([len(__a	) for w in self.vocab.keys()]	)
        _UpperCamelCase      :	int						=       re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"	)
        _UpperCamelCase      :	Optional[Any]						=       re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"	)
        _UpperCamelCase      :	List[Any]						=       re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"	)
        _UpperCamelCase      :	int						=       re.compile(
            R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
        _UpperCamelCase      :	Tuple						=       re.compile(
            R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
        _UpperCamelCase      :	int						=       re.compile(
            R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"	)
        _UpperCamelCase      :	Union[str, Any]						=       "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
        _UpperCamelCase      :	List[Any]						=       "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
        _UpperCamelCase      :	Union[str, Any]						=       str.maketrans({k: "<BLOCK>" for k in keisen + blocks}	)

  def __len__(		self   :       List[str]	)		->				List[str]:
        return len(self.ids_to_tokens	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       Any	)		->				Optional[int]:
        _UpperCamelCase      :	Tuple						=       self.content_repattera.sub("<URL>"					,   __a	)
        _UpperCamelCase      :	Tuple						=       self.content_repattera.sub("<EMAIL>"					,   __a	)
        _UpperCamelCase      :	int						=       self.content_repattera.sub("<TEL>"					,   __a	)
        _UpperCamelCase      :	int						=       self.content_repattera.sub("<DATE>"					,   __a	)
        _UpperCamelCase      :	Dict						=       self.content_repattera.sub("<DATE>"					,   __a	)
        _UpperCamelCase      :	List[Any]						=       self.content_repattera.sub("<PRICE>"					,   __a	)
        _UpperCamelCase      :	str						=       content.translate(self.content_transa	)
        while "<BLOCK><BLOCK>" in content:
              _UpperCamelCase      :	Optional[Any]						=       content.replace("<BLOCK><BLOCK>"					,   "<BLOCK>"	)
        return content

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       int					,   __a   :       Any=False	)		->				Any:
        _UpperCamelCase      :	Optional[Any]						=       text.replace(" "					,   "<SP>"	)
        _UpperCamelCase      :	Optional[int]						=       text.replace("　"					,   "<SP>"	)
        _UpperCamelCase      :	Optional[Any]						=       text.replace("\r\n"					,   "<BR>"	)
        _UpperCamelCase      :	List[Any]						=       text.replace("\n"					,   "<BR>"	)
        _UpperCamelCase      :	Optional[int]						=       text.replace("\r"					,   "<BR>"	)
        _UpperCamelCase      :	Dict						=       text.replace("\t"					,   "<TAB>"	)
        _UpperCamelCase      :	Union[str, Any]						=       text.replace("—"					,   "ー"	)
        _UpperCamelCase      :	Union[str, Any]						=       text.replace("−"					,   "ー"	)
        for k, v in self.emoji["emoji"].items():
              if k in text:
                    _UpperCamelCase      :	List[str]						=       text.replace(__a					,   __a	)
        if clean:
              _UpperCamelCase      :	int						=       self.clean_text(__a	)

        def check_simbol(__a   :       Union[str, Any]	):
              _UpperCamelCase      :	Tuple						=       x.encode()
              if len(__a	) == 1 and len(__a	) == 2:
                    _UpperCamelCase      :	Union[str, Any]						=       (int(e[0]	) << 8) + int(e[1]	)
                    if (
                        (c >= 0Xc2a1 and c <= 0Xc2bf)
                        or (c >= 0Xc780 and c <= 0Xc783)
                        or (c >= 0Xcab9 and c <= 0Xcbbf)
                        or (c >= 0Xcc80 and c <= 0Xcda2)
                    ):
                          return True
              return False

        def checkuae(__a   :       List[Any]	):
              _UpperCamelCase      :	List[Any]						=       x.encode()
              if len(__a	) == 1 and len(__a	) == 3:
                    _UpperCamelCase      :	List[Any]						=       (int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
                    if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
                          return True
              return False

        _UpperCamelCase      :	Any						=       0
        _UpperCamelCase      :	Any						=       []
        while pos < len(__a	):
              _UpperCamelCase      :	Any						=       min(len(__a	)					,   pos + self.maxlen + 1	) if text[pos] == "<" else pos + 3
              _UpperCamelCase      :	List[str]						=       []  # (token_id, token, pos)
              for e in range(__a					,   __a					,   -1	):
                    _UpperCamelCase      :	Optional[Any]						=       text[pos:e]
                    if wd in self.vocab:
                          if wd[0] == "<" and len(__a	) > 2:
                                _UpperCamelCase      :	Any						=       [(self.vocab[wd], wd, e)]
                                break
                          else:
                                candidates.append((self.vocab[wd], wd, e)	)
              if len(__a	) > 0:
                    # the smallest token_id is adopted
                    _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       sorted(__a					,   key=lambda __a	: x[0]	)[0]
                    result.append(__a	)
                    _UpperCamelCase      :	Optional[Any]						=       e
              else:
                    _UpperCamelCase      :	Optional[Any]						=       pos + 1
                    _UpperCamelCase      :	List[Any]						=       text[pos:end]
                    if check_simbol(__a	):
                          result.append("<KIGOU>"	)
                    elif checkuae(__a	):
                          result.append("<U2000U2BFF>"	)
                    else:
                          for i in wd.encode("utf-8"	):
                                result.append("<|byte%d|>" % i	)
                    _UpperCamelCase      :	Optional[int]						=       end
        return result


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       Optional[Any]					,   __a   :       Optional[Any]="\n"	)		->				List[Any]:
        _UpperCamelCase      :	str						=       []
        _UpperCamelCase      :	int						=       []
        _UpperCamelCase      :	int						=       self.ids_to_tokens[index][0]
        if word[:6] == "<|byte" and word[-2:] == "|>":
              byte_tokens.append(int(word[6:-2]	)	)
        else:
              if len(__a	) > 0:
                    words.append(bytearray(__a	).decode("utf-8"					,   errors="replace"	)	)
                    _UpperCamelCase      :	List[str]						=       []
              if word[:7] == "<|emoji" and word[-2:] == "|>":
                    words.append(self.emoji["emoji_inv"][word]	)
              elif word == "<SP>":
                    words.append(" "	)
              elif word == "<BR>":
                    words.append(__a	)
              elif word == "<TAB>":
                    words.append("\t"	)
              elif word == "<BLOCK>":
                    words.append("▀"	)
              elif word == "<KIGOU>":
                    words.append("ǀ"	)
              elif word == "<U2000U2BFF>":
                    words.append("‖"	)
              else:
                    words.append(__a	)
        if len(__a	) > 0:
              words.append(bytearray(__a	).decode("utf-8"					,   errors="replace"	)	)
        _UpperCamelCase      :	List[Any]						=       "".join(__a	)
        return text







"""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}"""
  )
