


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


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   """▁"""

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

UpperCAmelCase      =   {
    """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"""
        ),
    }
}

UpperCAmelCase      =   {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  VOCAB_FILES_NAMES
 __snake_case												=  PRETRAINED_VOCAB_FILES_MAP
 __snake_case												=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case												=  ["""input_ids""", """attention_mask"""]



 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase="<s>"		,				_UpperCAmelCase="</s>"		,				_UpperCAmelCase="</s>"		,				_UpperCAmelCase="<s>"		,				_UpperCAmelCase="<unk>"		,				_UpperCAmelCase="<pad>"		,				_UpperCAmelCase="<mask>"		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       snake_case_	=				AddedToken(snake_case__		,				lstrip=snake_case__		,				rstrip=snake_case__ ) if isinstance(snake_case__		,				snake_case__ ) else mask_token

       snake_case_	=				{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=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_	=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(str(snake_case__ ) )
       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
       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
       snake_case_	=				1

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



 def __getstate__(     self ):
       snake_case_	=				self.__dict__.copy()
       snake_case_	=				None
       snake_case_	=				self.sp_model.serialized_model_proto()
       return state



 def __setstate__(     self		,				_UpperCAmelCase ):
       snake_case_	=				d

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

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):





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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				_UpperCAmelCase = False ):





       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__ )

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				[self.sep_token_id]
       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 						UpperCamelCase__					(     self ):
       return len(self.sp_model ) + self.fairseq_offset + 1  # Add the <mask> token



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				{self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return self.sp_model.encode(snake_case__		,				out_type=snake_case__ )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):





       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):





       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				''''''.join(snake_case__ ).replace(snake_case__		,				''' ''' ).strip()
       return out_string





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):





       if not os.path.isdir(snake_case__ ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
             return
       snake_case_	=				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_	=				self.sp_model.serialized_model_proto()
                   fi.write(snake_case__ )

       return (out_vocab_file,)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase      =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ViTMSNModel""",
       """ViTMSNForImageClassification""",
       """ViTMSNPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

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