
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Optional[int]    =     """MarkupLMFeatureExtractor"""
 A__    :					int    =     ("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
 A__    :					int    =     True

 def __call__(   self  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase = True  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = 0  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = True  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        if self.parse_html:
               if html_strings is None:
                      raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""  )

               if nodes is not None or xpaths is not None or node_labels is not None:
                      raise ValueError(
                          """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""  )

               UpperCamelCase_											=							self.feature_extractor(__UpperCamelCase  )
               UpperCamelCase_											=							features["""nodes"""]
               UpperCamelCase_											=							features["""xpaths"""]
        else:
               if html_strings is not None:
                      raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""  )
               if nodes is None or xpaths is None:
                      raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""  )

        # # second, apply the tokenizer
        if questions is not None and self.parse_html:
               if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ):
                      UpperCamelCase_											=							[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        UpperCamelCase_											=							self.tokenizer(
            text=questions if questions is not None else nodes  ,						text_pair=nodes if questions is not None else None  ,						xpaths=__UpperCamelCase  ,						node_labels=__UpperCamelCase  ,						add_special_tokens=__UpperCamelCase  ,						padding=__UpperCamelCase  ,						truncation=__UpperCamelCase  ,						max_length=__UpperCamelCase  ,						stride=__UpperCamelCase  ,						pad_to_multiple_of=__UpperCamelCase  ,						return_token_type_ids=__UpperCamelCase  ,						return_attention_mask=__UpperCamelCase  ,						return_overflowing_tokens=__UpperCamelCase  ,						return_special_tokens_mask=__UpperCamelCase  ,						return_offsets_mapping=__UpperCamelCase  ,						return_length=__UpperCamelCase  ,						verbose=__UpperCamelCase  ,						return_tensors=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

        return encoded_inputs

 def lowerCamelCase_					(   self  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        return self.tokenizer.batch_decode(*__UpperCamelCase  ,						**__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        return self.tokenizer.decode(*__UpperCamelCase  ,						**__UpperCamelCase  )

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.tokenizer.model_input_names
        return tokenizer_input_names





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _A							=       [
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

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



