
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       """▁"""

__lowerCamelCase							=       {"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__lowerCamelCase							=       {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
__lowerCamelCase							=       ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]






class   UpperCAmelCase		(						A_ ):
   A__					:		Optional[Any]						     =     VOCAB_FILES_NAMES
   A__					:		Union[str, Any]						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		str						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		str						     =     ["input_ids", "attention_mask"]

   A__					:		List[int]						     =     []
   A__					:		List[int]						     =     []



   def __init__(self		:					List[Any]    ,						snake_case__		:					Tuple    ,						snake_case__		:					Dict=None    ,						snake_case__		:					Any=None    ,						snake_case__		:					int="</s>"    ,						snake_case__		:					List[Any]="</s>"    ,						snake_case__		:					Optional[int]="<s>"    ,						snake_case__		:					Tuple="<unk>"    ,						snake_case__		:					List[str]="<pad>"    ,						snake_case__		:					Dict="<mask>"    ,						snake_case__		:					Optional[Dict[str, Any]] = None    ,						**snake_case__		:					Tuple    ,						)		->	None:


          '''simple docstring'''


          snake_case       :   int							=      AddedToken(snake_case__    ,						lstrip=snake_case__    ,						rstrip=snake_case__ ) if isinstance(snake_case__    ,						snake_case__ ) else mask_token

          snake_case       :   Tuple							=      {} if sp_model_kwargs is None else sp_model_kwargs

          snake_case       :   Union[str, Any]							=      kwargs.get("additional_special_tokens"    ,						[] )
          kwargs["additional_special_tokens"] += [
              code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=snake_case__    ,						tgt_lang=snake_case__    ,						eos_token=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						cls_token=snake_case__    ,						pad_token=snake_case__    ,						mask_token=snake_case__    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**snake_case__    ,						)

          snake_case       :   Tuple							=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(snake_case__ ) )
          snake_case       :   Tuple							=      vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          snake_case       :   Optional[int]							=      {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          snake_case       :   List[str]							=      1

          snake_case       :   int							=      len(self.sp_model )
          snake_case       :   Any							=      {
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(snake_case__ )
          }
          snake_case       :   Union[str, Any]							=      {v: k for k, v in self.lang_code_to_id.items()}
          snake_case       :   List[Any]							=      len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
          snake_case       :   Any							=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

          snake_case       :   str							=      src_lang if src_lang is not None else "en_XX"
          snake_case       :   Dict							=      self.lang_code_to_id[self._src_lang]
          snake_case       :   int							=      tgt_lang
          self.set_src_lang_special_tokens(self._src_lang )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	str:


          '''simple docstring'''


          return self._src_lang



   @src_lang.setter
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   Dict							=      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang )



   def __getstate__(self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.__dict__.copy()
          snake_case       :   List[str]							=      None
          return state



   def __setstate__(self		:					str    ,						snake_case__		:					Dict )		->	None:


          '''simple docstring'''


          snake_case       :   List[Any]							=      d

          # for backward compatibility
          if not hasattr(self    ,						"sp_model_kwargs" ):
                 snake_case       :   Dict							=      {}

          snake_case       :   int							=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(self.vocab_file )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Dict:


          '''simple docstring'''


          snake_case       :   Dict							=      {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          return self.sp_model.encode(snake_case__    ,						out_type=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str )		->	int:


          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
          snake_case       :   List[Any]							=      self.sp_model.PieceToId(snake_case__ )

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int )		->	str:


          '''simple docstring'''


          if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      []
          snake_case       :   Union[str, Any]							=      ""
          snake_case       :   Tuple							=      False
          for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        if not prev_is_special:
                               out_string += " "
                        out_string += self.sp_model.decode(snake_case__ ) + token
                        snake_case       :   List[Any]							=      True
                        snake_case       :   List[Any]							=      []
                 else:
                        current_sub_tokens.append(snake_case__ )
                        snake_case       :   Optional[Any]							=      False
          out_string += self.sp_model.decode(snake_case__ )
          return out_string.strip()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          if not os.path.isdir(snake_case__ ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                 return
          snake_case       :   Any							=      os.path.join(
              snake_case__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
                 copyfile(self.vocab_file    ,						snake_case__ )
          elif not os.path.isfile(self.vocab_file ):
                 with open(snake_case__    ,						"wb" ) as fi:
                        snake_case       :   str							=      self.sp_model.serialized_model_proto()
                        fi.write(snake_case__ )

          return (out_vocab_file,)



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None    ,						snake_case__		:					bool = False )		->	List[int]:


          '''simple docstring'''


          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=snake_case__    ,						token_ids_a=snake_case__    ,						already_has_special_tokens=snake_case__ )

          snake_case       :   Union[str, Any]							=      [1] * len(self.prefix_tokens )
          snake_case       :   int							=      [1] * len(self.suffix_tokens )
          if token_ids_a is None:
                 return prefix_ones + ([0] * len(snake_case__ )) + suffix_ones
          return prefix_ones + ([0] * len(snake_case__ )) + ([0] * len(snake_case__ )) + suffix_ones



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str]    ,						snake_case__		:					Optional[str]    ,						**snake_case__		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          if src_lang is None or tgt_lang is None:
                 raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
          snake_case       :   str							=      src_lang
          snake_case       :   List[str]							=      self(snake_case__    ,						add_special_tokens=snake_case__    ,						return_tensors=snake_case__    ,						**snake_case__ )
          snake_case       :   Optional[int]							=      self.convert_tokens_to_ids(snake_case__ )
          snake_case       :   Optional[int]							=      tgt_lang_id
          return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					List[str]    ,						snake_case__		:					str = "en_XX"    ,						snake_case__		:					Optional[List[str]] = None    ,						snake_case__		:					str = "ro_RO"    ,						**snake_case__		:					Union[str, Any]    ,						)		->	BatchEncoding:


          '''simple docstring'''


          snake_case       :   str							=      src_lang
          snake_case       :   int							=      tgt_lang
          return super().prepare_seqaseq_batch(snake_case__    ,						snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Optional[Any]:


          '''simple docstring'''


          return self.set_src_lang_special_tokens(self.src_lang )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	str:


          '''simple docstring'''


          return self.set_tgt_lang_special_tokens(self.tgt_lang )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.lang_code_to_id[src_lang]
          snake_case       :   List[str]							=      [self.cur_lang_code_id]
          snake_case       :   Any							=      [self.eos_token_id]



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.lang_code_to_id[tgt_lang]
          snake_case       :   Union[str, Any]							=      [self.cur_lang_code_id]
          snake_case       :   Tuple							=      [self.eos_token_id]






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowercase							 =					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.
__lowercase							 =					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.
__lowercase							 =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		random.randint(0  ,			len(SCREAMING_SNAKE_CASE	) - 1	)
    __UpperCamelCase     :Tuple			  =		parent_a[:random_slice] + parent_a[random_slice:]
    __UpperCamelCase     :Union[str, Any]			  =		parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		list(SCREAMING_SNAKE_CASE	)
    if random.uniform(0  ,			1	) < MUTATION_PROBABILITY:
        __UpperCamelCase     :str			  =		random.choice(SCREAMING_SNAKE_CASE	)
    return "".join(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







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

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		crossover(parent_a[0]  ,			SCREAMING_SNAKE_CASE	)
        # Append new string to the population list.
        pop.append(mutate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
        pop.append(mutate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
    return pop



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = True	):
    '''simple docstring'''







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

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

    # Just some logs to know what the algorithms is doing.
    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		0, 0

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

        # 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     :Tuple			  =		[evaluate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	) for item in population]

        # Check if there is a matching evolution.
        __UpperCamelCase     :Tuple			  =		sorted(SCREAMING_SNAKE_CASE  ,			key=lambda SCREAMING_SNAKE_CASE	: x[1]  ,			reverse=SCREAMING_SNAKE_CASE	)
        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(SCREAMING_SNAKE_CASE	)
        # Normalize population score to be between 0 and 1.
        __UpperCamelCase     :Union[str, Any]			  =		[
            (item, score / len(SCREAMING_SNAKE_CASE	)) for item, score in population_score
        ]

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


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





