
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]







def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int    ):
       snake_case       :   list[list[str]]							=      [[] for _ in range(__lowerCamelCase    )]
       snake_case       :   int							=      key - 1

       if key <= 0:
              raise ValueError("Height of grid can't be 0 or negative"    )
       if key == 1 or len(__lowerCamelCase    ) <= key:
              return input_string

       for position, character in enumerate(__lowerCamelCase    ):
              snake_case       :   Any							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Optional[int]							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              temp_grid[num].append(__lowerCamelCase    )
       snake_case       :   List[str]							=      ["".join(__lowerCamelCase    ) for row in temp_grid]
       snake_case       :   Tuple							=      "".join(__lowerCamelCase    )

       return output_string


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int    ):
       snake_case       :   Dict							=      []
       snake_case       :   Union[str, Any]							=      key - 1

       if key <= 0:
              raise ValueError("Height of grid can't be 0 or negative"    )
       if key == 1:
              return input_string

       snake_case       :   list[list[str]]							=      [[] for _ in range(__lowerCamelCase    )]  # generates template
       for position in range(len(__lowerCamelCase    )    ):
              snake_case       :   List[str]							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Optional[int]							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              temp_grid[num].append("*"    )

       snake_case       :   Tuple							=      0
       for row in temp_grid:  # fills in the characters
              snake_case       :   Union[str, Any]							=      input_string[counter : counter + len(__lowerCamelCase    )]
              grid.append(list(__lowerCamelCase    )    )
              counter += len(__lowerCamelCase    )

       snake_case       :   str							=      ""  # reads as zigzag
       for position in range(len(__lowerCamelCase    )    ):
              snake_case       :   Optional[int]							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Tuple							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              output_string += grid[num][0]
              grid[num].pop(0    )
       return output_string


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Tuple							=      {}
       for key_guess in range(1				,					len(__lowerCamelCase    )    ):  # tries every key
              snake_case       :   Any							=      decrypt(__lowerCamelCase				,					__lowerCamelCase    )
       return results


if __name__ == "__main__":
   import doctest

   doctest.testmod()





