
from typing import TYPE_CHECKING

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


__magic_name__  							=	{'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__  							=	['''LayoutXLMTokenizer''']

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

if TYPE_CHECKING:
     from .processing_layoutxlm import LayoutXLMProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm import LayoutXLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
     import sys

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





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


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__  							=	{
    '''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'''
        ),
    },
}

__magic_name__  							=	{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}




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

          snake_case__    =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
          if pre_tok_state.get("add_prefix_space"		,						lowerCamelCase			) != add_prefix_space:
              snake_case__    =					getattr(lowerCamelCase		,						pre_tok_state.pop("type"			)			)
              snake_case__    =					add_prefix_space
              snake_case__    =					pre_tok_class(**lowerCamelCase			)

          snake_case__    =					add_prefix_space

          snake_case__    =					"post_processor"
          snake_case__    =					getattr(self.backend_tokenizer		,						lowerCamelCase		,						lowerCamelCase			)
          if tokenizer_component_instance:
              snake_case__    =					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:
                  snake_case__    =					tuple(state["sep"]			)
              if "cls" in state:
                  snake_case__    =					tuple(state["cls"]			)

              snake_case__    =					False

              if state.get("add_prefix_space"		,						lowerCamelCase			) != add_prefix_space:
                  snake_case__    =					add_prefix_space
                  snake_case__    =					True

              if state.get("trim_offsets"		,						lowerCamelCase			) != trim_offsets:
                  snake_case__    =					trim_offsets
                  snake_case__    =					True

              if changes_to_apply:
                  snake_case__    =					getattr(lowerCamelCase		,						state.pop("type"			)			)
                  snake_case__    =					component_class(**lowerCamelCase			)
                  setattr(self.backend_tokenizer		,						lowerCamelCase		,						lowerCamelCase			)
      @property
      def A_      (      self			):
          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 A_      (      self		,						lowerCamelCase			):
          snake_case__    =					AddedToken(lowerCamelCase		,						lstrip=lowerCamelCase		,						rstrip=lowerCamelCase			) if isinstance(lowerCamelCase		,						lowerCamelCase			) else value
          snake_case__    =					value
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          snake_case__    =					kwargs.get("is_split_into_words"		,						lowerCamelCase			)
          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(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          snake_case__    =					kwargs.get("is_split_into_words"		,						lowerCamelCase			)

          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(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          snake_case__    =					self._tokenizer.model.save(lowerCamelCase		,						name=lowerCamelCase			)
          return tuple(lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=None			):
          snake_case__    =					[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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase = 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]



