



"""simple docstring"""





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_mvp import MvpTokenizer


lowerCamelCase  			=		logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase  			=		{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase  			=		{
    """RUCAIBox/mvp""": 1_024,
}
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	VOCAB_FILES_NAMES
       UpperCamelCase		=	PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase		=	['''input_ids''', '''attention_mask''']
       UpperCamelCase		=	MvpTokenizer



       def __init__(				self     :    List[str]						,							_UpperCAmelCase     :    Dict=None						,							_UpperCAmelCase     :    List[Any]=None						,							_UpperCAmelCase     :    Union[str, Any]=None						,							_UpperCAmelCase     :    Any="replace"						,							_UpperCAmelCase     :    int="<s>"						,							_UpperCAmelCase     :    Any="</s>"						,							_UpperCAmelCase     :    Tuple="</s>"						,							_UpperCAmelCase     :    List[Any]="<s>"						,							_UpperCAmelCase     :    Any="<unk>"						,							_UpperCAmelCase     :    Any="<pad>"						,							_UpperCAmelCase     :    Optional[Any]="<mask>"						,							_UpperCAmelCase     :    Tuple=False						,							_UpperCAmelCase     :    str=True						,							**_UpperCAmelCase     :    Dict						,							)					->		Tuple:


             '''simple docstring'''




             super().__init__(
                 _UpperCAmelCase						,							_UpperCAmelCase						,							tokenizer_file=_UpperCAmelCase						,							errors=_UpperCAmelCase						,							bos_token=_UpperCAmelCase						,							eos_token=_UpperCAmelCase						,							sep_token=_UpperCAmelCase						,							cls_token=_UpperCAmelCase						,							unk_token=_UpperCAmelCase						,							pad_token=_UpperCAmelCase						,							mask_token=_UpperCAmelCase						,							add_prefix_space=_UpperCAmelCase						,							trim_offsets=_UpperCAmelCase						,							**_UpperCAmelCase						,							)

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

             UpperCAmelCase_     					=				add_prefix_space

             # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
             UpperCAmelCase_     					=				"post_processor"
             UpperCAmelCase_     					=				getattr(self.backend_tokenizer						,							_UpperCAmelCase						,							_UpperCAmelCase       )
             if tokenizer_component_instance:
                   UpperCAmelCase_     					=				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:
                         UpperCAmelCase_     					=				tuple(state["sep"]       )
                   if "cls" in state:
                         UpperCAmelCase_     					=				tuple(state["cls"]       )

                   UpperCAmelCase_     					=				False

                   if state.get("add_prefix_space"						,							_UpperCAmelCase       ) != add_prefix_space:
                         UpperCAmelCase_     					=				add_prefix_space
                         UpperCAmelCase_     					=				True

                   if state.get("trim_offsets"						,							_UpperCAmelCase       ) != trim_offsets:
                         UpperCAmelCase_     					=				trim_offsets
                         UpperCAmelCase_     					=				True

                   if changes_to_apply:
                         UpperCAmelCase_     					=				getattr(_UpperCAmelCase						,							state.pop("type"       )       )
                         UpperCAmelCase_     					=				component_class(**_UpperCAmelCase       )
                         setattr(self.backend_tokenizer						,							_UpperCAmelCase						,							_UpperCAmelCase       )



       @property
       def 	lowercase__							(				self     :    Optional[Any]       )					->		str:


             '''simple docstring'''




             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 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    List[Any]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				AddedToken(_UpperCAmelCase						,							lstrip=_UpperCAmelCase						,							rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ) else value
             UpperCAmelCase_     					=				value



       def 	lowercase__							(				self     :    Optional[int]						,							*_UpperCAmelCase     :    Any						,							**_UpperCAmelCase     :    Tuple       )					->		BatchEncoding:


             '''simple docstring'''




             UpperCAmelCase_     					=				kwargs.get("is_split_into_words"						,							_UpperCAmelCase       )

             if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                       "to use it with pretokenized inputs."       )

             return super()._batch_encode_plus(*_UpperCAmelCase						,							**_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Tuple						,							*_UpperCAmelCase     :    List[str]						,							**_UpperCAmelCase     :    List[str]       )					->		BatchEncoding:


             '''simple docstring'''




             UpperCAmelCase_     					=				kwargs.get("is_split_into_words"						,							_UpperCAmelCase       )

             if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                       "to use it with pretokenized inputs."       )

             return super()._encode_plus(*_UpperCAmelCase						,							**_UpperCAmelCase       )



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[str] = None       )					->		Tuple[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self._tokenizer.model.save(_UpperCAmelCase						,							name=_UpperCAmelCase       )
             return tuple(_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Any						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    str=None       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				[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 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    List[int]						,							_UpperCAmelCase     :    Optional[List[int]] = None       )					->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[self.sep_token_id]
             UpperCAmelCase_     					=				[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]












'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__			=      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3TokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv3ForQuestionAnswering',
     'LayoutLMv3ForSequenceClassification',
     'LayoutLMv3ForTokenClassification',
     'LayoutLMv3Model',
     'LayoutLMv3PreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFLayoutLMv3ForQuestionAnswering',
     'TFLayoutLMv3ForSequenceClassification',
     'TFLayoutLMv3ForTokenClassification',
     'TFLayoutLMv3Model',
     'TFLayoutLMv3PreTrainedModel',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3FeatureExtractor']
 UpperCamelCase__			=      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 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_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

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

else:
 import sys

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




