




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       "▁"

_snake_case										=       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_snake_case										=       {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_snake_case										=       {
    "facebook/m2m100_418M": 1024,
}

# fmt: off
_snake_case										=       {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}







class 						lowercase							(   lowerCamelCase_		):
     _a							 =						VOCAB_FILES_NAMES
     _a							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a							 =						PRETRAINED_VOCAB_FILES_MAP
     _a							 =						["input_ids", "attention_mask"]

     _a							 =						[]
     _a							 =						[]

     def __init__(  self		,     _a		,     _a		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<pad>"		,     _a="<unk>"		,     _a="m2m100"		,     _a = None		,     _a=8		,     **_a		,     )						->							Dict:
           _A							:     Optional[Any]			=						{} if sp_model_kwargs is None else sp_model_kwargs

           _A							:     List[Any]			=						language_codes
           _A							:     Optional[int]			=						FAIRSEQ_LANGUAGE_CODES[language_codes]
           _A							:     List[Any]			=						{lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

           _A							:     Union[str, Any]			=						kwargs.get("""additional_special_tokens"""		,     []				)
           kwargs["additional_special_tokens"] += [
               self.get_lang_token(__SCREAMING_SNAKE_CASE				)
               for lang_code in fairseq_language_code
               if self.get_lang_token(__SCREAMING_SNAKE_CASE				) not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               src_lang=__SCREAMING_SNAKE_CASE		,     tgt_lang=__SCREAMING_SNAKE_CASE		,     bos_token=__SCREAMING_SNAKE_CASE		,     eos_token=__SCREAMING_SNAKE_CASE		,     sep_token=__SCREAMING_SNAKE_CASE		,     unk_token=__SCREAMING_SNAKE_CASE		,     pad_token=__SCREAMING_SNAKE_CASE		,     language_codes=__SCREAMING_SNAKE_CASE		,     sp_model_kwargs=self.sp_model_kwargs		,     num_madeup_words=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE		,     )

           _A							:     int			=						vocab_file
           _A							:     Any			=						load_json(__SCREAMING_SNAKE_CASE				)
           _A							:     str			=						{v: k for k, v in self.encoder.items()}
           _A							:     Optional[int]			=						spm_file
           _A							:     Any			=						load_spm(__SCREAMING_SNAKE_CASE		,     self.sp_model_kwargs				)

           _A							:     List[Any]			=						len(self.encoder				)

           _A							:     Dict			=						{
               self.get_lang_token(__SCREAMING_SNAKE_CASE				): self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE				)
           }
           _A							:     Optional[int]			=						{lang_code: self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE				)}
           _A							:     Union[str, Any]			=						{v: k for k, v in self.lang_token_to_id.items()}

           _A							:     Tuple			=						src_lang if src_lang is not None else '''en'''
           _A							:     Optional[int]			=						tgt_lang
           _A							:     Any			=						self.get_lang_id(self._src_lang				)
           self.set_src_lang_special_tokens(self._src_lang				)

           _A							:     Dict			=						num_madeup_words

     @property
     def 					a__	(  self				)						->							Union[str, Any]:
           return len(self.encoder				) + len(self.lang_token_to_id				)

     @property
     def 					a__	(  self				)						->							Tuple:
           return self._src_lang

     @src_lang.setter
     def 					a__	(  self		,     _a				)						->							str:
           _A							:     str			=						new_src_lang
           self.set_src_lang_special_tokens(self._src_lang				)

     def 					a__	(  self		,     _a				)						->							List[str]:
           return self.sp_model.encode(__SCREAMING_SNAKE_CASE		,     out_type=__SCREAMING_SNAKE_CASE				)

     def 					a__	(  self		,     _a				)						->							Union[str, Any]:





           if token in self.lang_token_to_id:
                 return self.lang_token_to_id[token]
           return self.encoder.get(__SCREAMING_SNAKE_CASE		,     self.encoder[self.unk_token]				)

     def 					a__	(  self		,     _a				)						->							Optional[Any]:





           if index in self.id_to_lang_token:
                 return self.id_to_lang_token[index]
           return self.decoder.get(__SCREAMING_SNAKE_CASE		,     self.unk_token				)

     def 					a__	(  self		,     _a				)						->							List[str]:
           _A							:     Tuple			=						[]
           _A							:     List[str]			=						''''''
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE				) + token
                       _A							:     Optional[Any]			=						[]
                 else:
                       current_sub_tokens.append(__SCREAMING_SNAKE_CASE				)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE				)
           return out_string.strip()

     def 					a__	(  self		,     _a		,     _a = None		,     _a = False				)						->							Union[str, Any]:





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

           _A							:     Optional[int]			=						[1] * len(self.prefix_tokens				)
           _A							:     Any			=						[1] * len(self.suffix_tokens				)
           if token_ids_a is None:
                 return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE				)) + suffix_ones
           return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE				)) + ([0] * len(__SCREAMING_SNAKE_CASE				)) + suffix_ones

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[str]:





           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 					a__	(  self				)						->							str:
           _A							:     Tuple			=						{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
           vocab.update(self.added_tokens_encoder				)
           return vocab

     def __getstate__(  self				)						->							str:
           _A							:     List[Any]			=						self.__dict__.copy()
           _A							:     List[Any]			=						None
           return state

     def __setstate__(  self		,     _a				)						->							Optional[Any]:
           _A							:     Dict			=						d

           # for backward compatibility
           if not hasattr(self		,     """sp_model_kwargs"""				):
                 _A							:     List[Any]			=						{}

           _A							:     Union[str, Any]			=						load_spm(self.spm_file		,     self.sp_model_kwargs				)

     def 					a__	(  self		,     _a		,     _a = None				)						->							Optional[int]:
           _A							:     Tuple			=						Path(__SCREAMING_SNAKE_CASE				)
           if not save_dir.is_dir():
                 raise OSError(F'''{save_directory} should be a directory'''				)
           _A							:     Dict			=						save_dir / (
               (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
           )
           _A							:     Dict			=						save_dir / (
               (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
           )

           save_json(self.encoder		,     __SCREAMING_SNAKE_CASE				)

           if os.path.abspath(self.spm_file				) != os.path.abspath(__SCREAMING_SNAKE_CASE				) and os.path.isfile(self.spm_file				):
                 copyfile(self.spm_file		,     __SCREAMING_SNAKE_CASE				)
           elif not os.path.isfile(self.spm_file				):
                 with open(__SCREAMING_SNAKE_CASE		,     """wb"""				) as fi:
                       _A							:     int			=						self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE				)

           return (str(__SCREAMING_SNAKE_CASE				), str(__SCREAMING_SNAKE_CASE				))

     def 					a__	(  self		,     _a		,     _a = "en"		,     _a = None		,     _a = "ro"		,     **_a		,     )						->							Union[str, Any]:
           _A							:     Optional[Any]			=						src_lang
           _A							:     List[str]			=						tgt_lang
           self.set_src_lang_special_tokens(self.src_lang				)
           return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     **_a				)						->							List[str]:





           if src_lang is None or tgt_lang is None:
                 raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
           _A							:     Tuple			=						src_lang
           _A							:     Any			=						self(__SCREAMING_SNAKE_CASE		,     add_special_tokens=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)
           _A							:     List[Any]			=						self.get_lang_id(__SCREAMING_SNAKE_CASE				)
           _A							:     Union[str, Any]			=						tgt_lang_id
           return inputs

     def 					a__	(  self				)						->							Union[str, Any]:
           self.set_src_lang_special_tokens(self.src_lang				)

     def 					a__	(  self				)						->							Optional[int]:
           self.set_tgt_lang_special_tokens(self.tgt_lang				)

     def 					a__	(  self		,     _a				)						->							Optional[Any]:
           _A							:     Any			=						self.get_lang_token(__SCREAMING_SNAKE_CASE				)
           _A							:     Dict			=						self.lang_token_to_id[lang_token]
           _A							:     Optional[Any]			=						[self.cur_lang_id]
           _A							:     Union[str, Any]			=						[self.eos_token_id]

     def 					a__	(  self		,     _a				)						->							str:
           _A							:     Any			=						self.get_lang_token(__SCREAMING_SNAKE_CASE				)
           _A							:     Any			=						self.lang_token_to_id[lang_token]
           _A							:     str			=						[self.cur_lang_id]
           _A							:     List[str]			=						[self.eos_token_id]

     def 					a__	(  self		,     _a				)						->							int:
           return self.lang_code_to_token[lang]



     def 					a__	(  self		,     _a				)						->							str:
           _A							:     List[Any]			=						self.get_lang_token(__SCREAMING_SNAKE_CASE				)
           return self.lang_token_to_id[lang_token]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[int]			=						sentencepiece.SentencePieceProcessor(**__SCREAMING_SNAKE_CASE     )
      spm.Load(str(__SCREAMING_SNAKE_CASE     )     )
      return spm







def 				lowerCAmelCase_	(     snake_case_     ):





      with open(__SCREAMING_SNAKE_CASE,"""r"""     ) as f:
            return json.load(__SCREAMING_SNAKE_CASE     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):





      with open(__SCREAMING_SNAKE_CASE,"""w"""     ) as f:
            json.dump(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,indent=2     )



'''simple docstring'''







_lowercase :  int    							=       [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       0
						while number:
												# Increased Speed Slightly by checking every 5 digits together.
												sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
												number //= 100000

						return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_lowercase :  list[bool | None]    							=       [None] * 1_0_0_0_0_0_0_0
_lowercase :  List[str]    							=       True
_lowercase :  Optional[int]    							=       False
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						if CHAINS[number - 1] is not None:
												return CHAINS[number - 1]  # type: ignore

						lowercase_	:     Tuple													=       chain(next_number(__SCREAMING_SNAKE_CASE							)							)
						lowercase_	:     Union[str, Any]													=       number_chain

						while number < 10000000:
												lowercase_	:     int													=       number_chain
												number *= 10

						return number_chain
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 10000000							):



						"""simple docstring"""





						for i in range(1      ,      __SCREAMING_SNAKE_CASE							):
												if CHAINS[i] is None:
																		chain(i + 1							)

						return CHAINS[:number].count(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"""{solution() = }""")



