

"""simple docstring"""



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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 SCREAMING_SNAKE_CASE_					:	int							  =						None


SCREAMING_SNAKE_CASE_					:	str							  =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_					:	int							  =						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_					:	str							  =						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_					:	List[Any]							  =						{
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
SCREAMING_SNAKE_CASE_					:	Dict							  =						['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']



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					VOCAB_FILES_NAMES
    UpperCAmelCase						 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase						 =					PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase						 =					["input_ids", "attention_mask"]
    UpperCAmelCase						 =					MBartTokenizer

    UpperCAmelCase						 =					[]
    UpperCAmelCase						 =					[]

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






         """simple docstring"""


         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else mask_token

         super().__init__(
             vocab_file=UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						sep_token=UpperCamelCase    ,						cls_token=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						pad_token=UpperCamelCase    ,						mask_token=UpperCamelCase    ,						src_lang=UpperCamelCase    ,						tgt_lang=UpperCamelCase    ,						additional_special_tokens=UpperCamelCase    ,						**UpperCamelCase    ,						)

         A__										=  vocab_file
         A__										=  False if not self.vocab_file else True

         A__										=  FAIRSEQ_LANGUAGE_CODES.copy()

         if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              _additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

         self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}       )
         A__										=  {
             lang_code: self.convert_tokens_to_ids(UpperCamelCase       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         A__										=  src_lang if src_lang is not None else """en_XX"""
         A__										=  self.convert_tokens_to_ids(self._src_lang       )
         A__										=  tgt_lang
         self.set_src_lang_special_tokens(self._src_lang       )

    @property
    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         return self._src_lang

    @src_lang.setter
    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__										=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None       ):






         """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 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None       ):






         """simple docstring"""


         A__										=  [self.sep_token_id]
         A__										=  [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 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: int    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[str]    ,						UpperCamelCase: Optional[str]    ,						**UpperCamelCase: 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"""       )
         A__										=  src_lang
         A__										=  self(UpperCamelCase    ,						add_special_tokens=UpperCamelCase    ,						return_tensors=UpperCamelCase    ,						**UpperCamelCase       )
         A__										=  self.convert_tokens_to_ids(UpperCamelCase       )
         A__										=  tgt_lang_id
         return inputs

    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: str = "en_XX"    ,						UpperCamelCase: Optional[List[str]] = None    ,						UpperCamelCase: str = "ro_RO"    ,						**UpperCamelCase: int    ,						):






         """simple docstring"""


         A__										=  src_lang
         A__										=  tgt_lang
         return super().prepare_seqaseq_batch(UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         return self.set_src_lang_special_tokens(self.src_lang       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         return self.set_tgt_lang_special_tokens(self.tgt_lang       )

    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  self.convert_tokens_to_ids(UpperCamelCase       )
         A__										=  []
         A__										=  [self.eos_token_id, self.cur_lang_code]

         A__										=  self.convert_ids_to_tokens(self.prefix_tokens       )
         A__										=  self.convert_ids_to_tokens(self.suffix_tokens       )

         A__										=  processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,						self.prefix_tokens + self.suffix_tokens       )       )    ,						)

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__										=  self.convert_tokens_to_ids(UpperCamelCase       )
         A__										=  []
         A__										=  [self.eos_token_id, self.cur_lang_code]

         A__										=  self.convert_ids_to_tokens(self.prefix_tokens       )
         A__										=  self.convert_ids_to_tokens(self.suffix_tokens       )

         A__										=  processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,						self.prefix_tokens + self.suffix_tokens       )       )    ,						)





    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[str] = None       ):






         """simple docstring"""


         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer."""       )

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

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase       ):
              copyfile(self.vocab_file    ,						UpperCamelCase       )

         return (out_vocab_file,)









"""simple docstring"""



import math
import sys





def  _snake_case     (    UpperCAmelCase_  :					int			):
     if number != int(UpperCAmelCase_			):
          raise ValueError("""the value of input must be a natural number"""			)
     if number < 0:
          raise ValueError("""the value of input must not be a negative number"""			)
     if number == 0:
          return 1
     A__										=  [-1] * (number + 1)
     A__										=  0
     for i in range(1					,   number + 1			):
          A__										=  sys.maxsize
          A__										=  int(math.sqrt(UpperCAmelCase_			)			)
          for j in range(1					,   root + 1			):
               A__										=  1 + answers[i - (j**2)]
               A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          A__										=  answer
     return answers[number]


if __name__ == "__main__":
 import doctest

 doctest.testmod()






