


from typing import Optional, Union

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

class 			lowerCamelCase_					(    __lowerCamelCase    ):
							_lowerCamelCase				: int       =					'MarkupLMFeatureExtractor'
							_lowerCamelCase				: Union[str, Any]       =					('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
							_lowerCamelCase				: List[str]       =					True





							def __call__(							self			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE = True			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = 0			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = True			,       _SCREAMING_SNAKE_CASE = None			,       **_SCREAMING_SNAKE_CASE			,       ):
								# first, create nodes and xpaths
								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`"""    )

									a_     			=   self.feature_extractor(UpperCamelCase_    )
									a_     			=   features["""nodes"""]
									a_     			=   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_    ):
										a_     			=   [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

								a_     			=   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   __magic_name__      (							self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ):
								return self.tokenizer.batch_decode(*UpperCamelCase_			,       **UpperCamelCase_    )





							def   __magic_name__      (							self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ):
								return self.tokenizer.decode(*UpperCamelCase_			,       **UpperCamelCase_    )





							@property
							def   __magic_name__      (							self    ):
								a_     			=   self.tokenizer.model_input_names
								return tokenizer_input_names





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_   							=      [
           '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

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

