
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase__          ={
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase__          ={
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}



class 		UpperCamelCase__  (	lowerCAmelCase_   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Dict     	=				["""input_ids""", """attention_mask"""]
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				RobertaTokenizer
   def __init__(self			: Dict ,    snake_case_			: int=None ,    snake_case_			: List[str]=None ,    snake_case_			: int=None ,    snake_case_			: Tuple="replace" ,    snake_case_			: Union[str, Any]="<s>" ,    snake_case_			: Dict="</s>" ,    snake_case_			: Any="</s>" ,    snake_case_			: str="<s>" ,    snake_case_			: int="<unk>" ,    snake_case_			: Optional[Any]="<pad>" ,    snake_case_			: str="<mask>" ,    snake_case_			: Any=False ,    snake_case_			: Optional[int]=True ,    **snake_case_			: Tuple ,    ):
    super().__init__(
        __SCREAMING_SNAKE_CASE ,    __SCREAMING_SNAKE_CASE ,    tokenizer_file=__SCREAMING_SNAKE_CASE ,    errors=__SCREAMING_SNAKE_CASE ,    bos_token=__SCREAMING_SNAKE_CASE ,    eos_token=__SCREAMING_SNAKE_CASE ,    sep_token=__SCREAMING_SNAKE_CASE ,    cls_token=__SCREAMING_SNAKE_CASE ,    unk_token=__SCREAMING_SNAKE_CASE ,    pad_token=__SCREAMING_SNAKE_CASE ,    mask_token=__SCREAMING_SNAKE_CASE ,    add_prefix_space=__SCREAMING_SNAKE_CASE ,    trim_offsets=__SCREAMING_SNAKE_CASE ,    **__SCREAMING_SNAKE_CASE ,    )

    __a			:  Optional[int]		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
    if pre_tok_state.get('''add_prefix_space''' ,    __SCREAMING_SNAKE_CASE       ) != add_prefix_space:
     __a			:  Dict		    =  getattr(__SCREAMING_SNAKE_CASE ,    pre_tok_state.pop('''type'''       )       )
     __a			:  int		    =  add_prefix_space
     __a			:  List[Any]		    =  pre_tok_class(**__SCREAMING_SNAKE_CASE       )

    __a			:  Optional[Any]		    =  add_prefix_space

    __a			:  Optional[int]		    =  '''post_processor'''
    __a			:  int		    =  getattr(self.backend_tokenizer ,    __SCREAMING_SNAKE_CASE ,    __SCREAMING_SNAKE_CASE       )
    if tokenizer_component_instance:
     __a			:  Dict		    =  json.loads(tokenizer_component_instance.__getstate__()       )

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
      __a			:  str		    =  tuple(state['''sep''']       )
     if "cls" in state:
      __a			:  Dict		    =  tuple(state['''cls''']       )

     __a			:  Union[str, Any]		    =  False

     if state.get('''add_prefix_space''' ,    __SCREAMING_SNAKE_CASE       ) != add_prefix_space:
      __a			:  List[str]		    =  add_prefix_space
      __a			:  Any		    =  True

     if state.get('''trim_offsets''' ,    __SCREAMING_SNAKE_CASE       ) != trim_offsets:
      __a			:  List[Any]		    =  trim_offsets
      __a			:  int		    =  True

     if changes_to_apply:
      __a			:  Optional[Any]		    =  getattr(__SCREAMING_SNAKE_CASE ,    state.pop('''type'''       )       )
      __a			:  str		    =  component_class(**__SCREAMING_SNAKE_CASE       )
      setattr(self.backend_tokenizer ,    __SCREAMING_SNAKE_CASE ,    __SCREAMING_SNAKE_CASE       )
   @property
   def 				lowerCAmelCase      (self			: Tuple       ):

    if self._mask_token is None:
     if self.verbose:
      logger.error('''Using mask_token, but it is not set yet.'''       )
     return None
    return str(self._mask_token       )
   @mask_token.setter
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[Any]       ):
    __a			:  List[Any]		    =  AddedToken(__SCREAMING_SNAKE_CASE ,    lstrip=__SCREAMING_SNAKE_CASE ,    rstrip=__SCREAMING_SNAKE_CASE       ) if isinstance(__SCREAMING_SNAKE_CASE ,    __SCREAMING_SNAKE_CASE       ) else value
    __a			:  str		    =  value
   def 				lowerCAmelCase      (self			: Optional[int] ,    *snake_case_			: List[Any] ,    **snake_case_			: int       ):
    __a			:  List[Any]		    =  kwargs.get('''is_split_into_words''' ,    __SCREAMING_SNAKE_CASE       )
    assert self.add_prefix_space or not is_split_into_words, (
        f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
        "to use it with pretokenized inputs."
    )

    return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE ,    **__SCREAMING_SNAKE_CASE       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    *snake_case_			: int ,    **snake_case_			: Tuple       ):
    __a			:  Tuple		    =  kwargs.get('''is_split_into_words''' ,    __SCREAMING_SNAKE_CASE       )

    assert self.add_prefix_space or not is_split_into_words, (
        f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
        "to use it with pretokenized inputs."
    )

    return super()._encode_plus(*__SCREAMING_SNAKE_CASE ,    **__SCREAMING_SNAKE_CASE       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: List[Any] ,    snake_case_			: Optional[int] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,    name=__SCREAMING_SNAKE_CASE       )
    return tuple(__SCREAMING_SNAKE_CASE       )
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[int] ,    snake_case_			: Optional[Any]=None       ):
    __a			:  Union[str, Any]		    =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
     return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: Tuple ,    snake_case_			: int = None       ):
    __a			:  Tuple		    =  [self.sep_token_id]
    __a			:  List[str]		    =  [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]


from typing import TYPE_CHECKING

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


lowercase__          ={
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv2ForQuestionAnswering',
          'LayoutLMv2ForSequenceClassification',
          'LayoutLMv2ForTokenClassification',
          'LayoutLMv2Layer',
          'LayoutLMv2Model',
          'LayoutLMv2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
