




'''simple docstring'''



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

import sentencepiece as spm

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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						"▁"

lowerCamelCase_			=						{"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase_			=						{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase_			=						{
    "xlm-roberta-base": 5_12,
    "xlm-roberta-large": 5_12,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_12,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_12,
    "xlm-roberta-large-finetuned-conll03-english": 5_12,
    "xlm-roberta-large-finetuned-conll03-german": 5_12,
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						['''input_ids''', '''attention_mask''']



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				AddedToken(__lowerCamelCase  ,				lstrip=__lowerCamelCase  ,				rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ) else mask_token

      _SCREAMING_SNAKE_CASE	=				{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__lowerCamelCase  ,				eos_token=__lowerCamelCase  ,				unk_token=__lowerCamelCase  ,				sep_token=__lowerCamelCase  ,				cls_token=__lowerCamelCase  ,				pad_token=__lowerCamelCase  ,				mask_token=__lowerCamelCase  ,				sp_model_kwargs=self.sp_model_kwargs  ,				**__lowerCamelCase  ,				)

      _SCREAMING_SNAKE_CASE	=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				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
      _SCREAMING_SNAKE_CASE	=				{"<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
      _SCREAMING_SNAKE_CASE	=				1

      _SCREAMING_SNAKE_CASE	=				len(self.sp_model   ) + self.fairseq_offset
      _SCREAMING_SNAKE_CASE	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



   def __getstate__(   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.__dict__.copy()
      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				self.sp_model.serialized_model_proto()
      return state



   def __setstate__(   self :     str  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				d

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

      _SCREAMING_SNAKE_CASE	=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     Optional[List[int]] = None   ):

      """simple docstring"""

      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _SCREAMING_SNAKE_CASE	=				[self.cls_token_id]
      _SCREAMING_SNAKE_CASE	=				[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     Optional[List[int]] = None  ,				__lowerCamelCase :     bool = False   ):

      """simple docstring"""

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

      if token_ids_a is None:
         return [1] + ([0] * len(__lowerCamelCase   )) + [1]
      return [1] + ([0] * len(__lowerCamelCase   )) + [1, 1] + ([0] * len(__lowerCamelCase   )) + [1]



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[self.sep_token_id]
      _SCREAMING_SNAKE_CASE	=				[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]



   @property
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{self.convert_ids_to_tokens(__lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      return self.sp_model.encode(__lowerCamelCase  ,				out_type=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _SCREAMING_SNAKE_CASE	=				self.sp_model.PieceToId(__lowerCamelCase   )

      # 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 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Union[str, Any]   ):

      """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 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"".join(__lowerCamelCase   ).replace(__lowerCamelCase  ,				" "   ).strip()
      return out_string



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[str] = None   ):

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

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowerCamelCase   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file  ,				__lowerCamelCase   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(__lowerCamelCase  ,				"wb"   ) as fi:
            _SCREAMING_SNAKE_CASE	=				self.sp_model.serialized_model_proto()
            fi.write(__lowerCamelCase   )

      return (out_vocab_file,)







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_			=						{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure)

