



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		       =  {
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_		       =  [
      "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XCLIPModel",
      "XCLIPPreTrainedModel",
      "XCLIPTextModel",
      "XCLIPVisionModel",
  ]

if TYPE_CHECKING:
  from .configuration_x_clip import (
      XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      XCLIPConfig,
      XCLIPTextConfig,
      XCLIPVisionConfig,
  )
  from .processing_x_clip import XCLIPProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_x_clip import (
        XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        XCLIPModel,
        XCLIPPreTrainedModel,
        XCLIPTextModel,
        XCLIPVisionModel,
    )

else:
  import sys

  lowercase_		       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''





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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  "▁"

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

lowercase_		       =  {
    "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",
    },
}

lowercase_		       =  {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowercase_		       =  {
    "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 __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Dict          =       ['input_ids', 'attention_mask']

 __lowerCamelCase    :       List[int]          =       []
 __lowerCamelCase    :       List[int]          =       []



 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 ,   )    ->     None:


      """simple docstring"""



      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

      _a      =  language_codes
      _a      =  FAIRSEQ_LANGUAGE_CODES[language_codes]
      _a      =  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

      _a      =  kwargs.get('''additional_special_tokens''' ,   []       )
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(A       )
          for lang_code in fairseq_language_code
          if self.get_lang_token(A       ) not in kwargs["additional_special_tokens"]
      ]

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

      _a      =  vocab_file
      _a      =  load_json(A       )
      _a      =  {v: k for k, v in self.encoder.items()}
      _a      =  spm_file
      _a      =  load_spm(A ,   self.sp_model_kwargs       )

      _a      =  len(self.encoder       )

      _a      =  {
          self.get_lang_token(A       ): self.encoder_size + i for i, lang_code in enumerate(A       )
      }
      _a      =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(A       )}
      _a      =  {v: k for k, v in self.lang_token_to_id.items()}

      _a      =  src_lang if src_lang is not None else '''en'''
      _a      =  tgt_lang
      _a      =  self.get_lang_id(self._src_lang       )
      self.set_src_lang_special_tokens(self._src_lang       )

      _a      =  num_madeup_words



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return len(self.encoder       ) + len(self.lang_token_to_id       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang       )



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


      """simple docstring"""



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



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


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  []
      _a      =  ''''''
      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(A       ) + token
                _a      =  []
           else:
                current_sub_tokens.append(A       )
      out_string += self.sp_model.decode(A       )
      return out_string.strip()



 def 					a__  (self ,   A ,   A = None ,   A = False       )    ->     List[int]:


      """simple docstring"""



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

      _a      =  [1] * len(self.prefix_tokens       )
      _a      =  [1] * len(self.suffix_tokens       )
      if token_ids_a is None:
           return prefix_ones + ([0] * len(A       )) + suffix_ones
      return prefix_ones + ([0] * len(A       )) + ([0] * len(A       )) + suffix_ones



 def 					a__  (self ,   A ,   A = 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 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  {self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab



 def __getstate__(self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      return state



 def __setstate__(self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''       ):
           _a      =  {}

      _a      =  load_spm(self.spm_file ,   self.sp_model_kwargs       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  Path(A       )
      if not save_dir.is_dir():
           raise OSError(f'''{save_directory} should be a directory'''       )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
      )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
      )

      save_json(self.encoder ,   A       )

      if os.path.abspath(self.spm_file       ) != os.path.abspath(A       ) and os.path.isfile(self.spm_file       ):
           copyfile(self.spm_file ,   A       )
      elif not os.path.isfile(self.spm_file       ):
           with open(A ,   '''wb'''       ) as fi:
                _a      =  self.sp_model.serialized_model_proto()
                fi.write(A       )

      return (str(A       ), str(A       ))



 def 					a__  (self ,   A ,   A = "en" ,   A = None ,   A = "ro" ,   **A ,   )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  src_lang
      _a      =  tgt_lang
      self.set_src_lang_special_tokens(self.src_lang       )
      return super().prepare_seqaseq_batch(A ,   A ,   **A       )



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


      """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(A ,   add_special_tokens=A ,   **A       )
      _a      =  self.get_lang_id(A       )
      _a      =  tgt_lang_id
      return inputs



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


      """simple docstring"""



      self.set_src_lang_special_tokens(self.src_lang       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      self.set_tgt_lang_special_tokens(self.tgt_lang       )



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      return self.lang_code_to_token[lang]



 def 					a__  (self ,   A       )    ->     int:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      return self.lang_token_to_id[lang_token]





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  sentencepiece.SentencePieceProcessor(**__A)
     spm.Load(str(__A))
     return spm




def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(__A  ,	'''r''') as f:
          return json.load(__A)




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     with open(__A  ,	'''w''') as f:
          json.dump(__A  ,	__A  ,	indent=2)



