


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__  :Union[str, Any]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__  :Dict             =							{
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase__  :List[Any]             =							{'''bert_for_seq_generation''': 5_1_2}

class 		__a				(    UpperCAmelCase      ):
  _a     :  str           =     VOCAB_FILES_NAMES
  _a     :  Tuple           =     PRETRAINED_VOCAB_FILES_MAP
  _a     :  str           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a     :  List[int]           =     []
  _a     :  Optional[int]           =     ['input_ids', 'attention_mask']
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE="<s>"      ,   _SCREAMING_SNAKE_CASE="</s>"      ,   _SCREAMING_SNAKE_CASE="<unk>"      ,   _SCREAMING_SNAKE_CASE="<pad>"      ,   _SCREAMING_SNAKE_CASE="<::::>"      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




         """simple docstring"""





         _UpperCAmelCase					=      {} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=_SCREAMING_SNAKE_CASE      ,   eos_token=_SCREAMING_SNAKE_CASE      ,   unk_token=_SCREAMING_SNAKE_CASE      ,   pad_token=_SCREAMING_SNAKE_CASE      ,   sep_token=_SCREAMING_SNAKE_CASE      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **_SCREAMING_SNAKE_CASE      ,   )

         _UpperCAmelCase					=      vocab_file

         _UpperCAmelCase					=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
         self.sp_model.Load(_SCREAMING_SNAKE_CASE						)
  @property
  def      UpperCAmelCase__    (      self						)			->	Tuple:




         """simple docstring"""





         return self.sp_model.get_piece_size()
  def      UpperCAmelCase__    (      self						)			->	str:




         """simple docstring"""





         _UpperCAmelCase					=      {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
         vocab.update(self.added_tokens_encoder						)
         return vocab
  def __getstate__(      self						)			->	int:




         """simple docstring"""





         _UpperCAmelCase					=      self.__dict__.copy()
         _UpperCAmelCase					=      None
         return state
  def __setstate__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




         """simple docstring"""





         _UpperCAmelCase					=      d

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

         _UpperCAmelCase					=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
         self.sp_model.Load(self.vocab_file						)
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




         """simple docstring"""





         return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,   out_type=_SCREAMING_SNAKE_CASE						)
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




         """simple docstring"""





         return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE						)
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




         """simple docstring"""





         _UpperCAmelCase					=      self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE						)
         return token
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




         """simple docstring"""





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






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	Tuple[str]:




         """simple docstring"""





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

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

         return (out_vocab_file,)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Dict             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
