




"""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:
     A   =       None


A   =       logging.get_logger(__name__)


A   =       {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

A   =       {
    """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""",
    },
}

A   =       {
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
A   =       ["""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__				(	__magic_name__   ):
   lowercase_    =	VOCAB_FILES_NAMES
   lowercase_    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase_    =	PRETRAINED_VOCAB_FILES_MAP
   lowercase_    =	["input_ids", "attention_mask"]
   lowercase_    =	MBartTokenizer

   lowercase_    =	[]
   lowercase_    =	[]


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


      """simple docstring"""




      __UpperCAmelCase	: int		=					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_      ,  )

      __UpperCAmelCase	: Optional[Any]		=					vocab_file
      __UpperCAmelCase	: Dict		=					False if not self.vocab_file else True

      __UpperCAmelCase	: Union[str, Any]		=					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})
      __UpperCAmelCase	: List[str]		=					{
          lang_code: self.convert_tokens_to_ids(UpperCamelCase_) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      __UpperCAmelCase	: Tuple		=					src_lang if src_lang is not None else "en_XX"
      __UpperCAmelCase	: List[str]		=					self.convert_tokens_to_ids(self._src_lang)
      __UpperCAmelCase	: Any		=					tgt_lang
      self.set_src_lang_special_tokens(self._src_lang)


   @property
   def       a_     (      self      :    str):


      """simple docstring"""




      return self._src_lang


   @src_lang.setter
   def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    str):


      """simple docstring"""




      __UpperCAmelCase	: List[Any]		=					new_src_lang
      self.set_src_lang_special_tokens(self._src_lang)


   def       a_     (      self      :    Dict      ,  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       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None):


      """simple docstring"""




      __UpperCAmelCase	: int		=					[self.sep_token_id]
      __UpperCAmelCase	: Optional[int]		=					[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       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[Any]      ,  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")
      __UpperCAmelCase	: Dict		=					src_lang
      __UpperCAmelCase	: int		=					self(UpperCamelCase_      ,  add_special_tokens=UpperCamelCase_      ,  return_tensors=UpperCamelCase_      ,  **UpperCamelCase_)
      __UpperCAmelCase	: Any		=					self.convert_tokens_to_ids(UpperCamelCase_)
      __UpperCAmelCase	: str		=					tgt_lang_id
      return inputs


   def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    str = "en_XX"      ,  UpperCamelCase_      :    Optional[List[str]] = None      ,  UpperCamelCase_      :    str = "ro_RO"      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


      """simple docstring"""




      __UpperCAmelCase	: Tuple		=					src_lang
      __UpperCAmelCase	: Optional[Any]		=					tgt_lang
      return super().prepare_seqaseq_batch(UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_)


   def       a_     (      self      :    str):


      """simple docstring"""




      return self.set_src_lang_special_tokens(self.src_lang)


   def       a_     (      self      :    int):


      """simple docstring"""




      return self.set_tgt_lang_special_tokens(self.tgt_lang)


   def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    Optional[int]):


      """simple docstring"""




      __UpperCAmelCase	: Optional[int]		=					self.convert_tokens_to_ids(UpperCamelCase_)
      __UpperCAmelCase	: Dict		=					[]
      __UpperCAmelCase	: Optional[Any]		=					[self.eos_token_id, self.cur_lang_code]

      __UpperCAmelCase	: Dict		=					self.convert_ids_to_tokens(self.prefix_tokens)
      __UpperCAmelCase	: List[Any]		=					self.convert_ids_to_tokens(self.suffix_tokens)

      __UpperCAmelCase	: Tuple		=					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       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    str):


      """simple docstring"""




      __UpperCAmelCase	: int		=					self.convert_tokens_to_ids(UpperCamelCase_)
      __UpperCAmelCase	: int		=					[]
      __UpperCAmelCase	: List[str]		=					[self.eos_token_id, self.cur_lang_code]

      __UpperCAmelCase	: List[Any]		=					self.convert_ids_to_tokens(self.prefix_tokens)
      __UpperCAmelCase	: int		=					self.convert_ids_to_tokens(self.suffix_tokens)

      __UpperCAmelCase	: Optional[Any]		=					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       a_     (      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
      __UpperCAmelCase	: int		=					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"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


