





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				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							"MarkupLMFeatureExtractor"
	_A       	=							("MarkupLMTokenizer", "MarkupLMTokenizerFast")
	_A       	=							True

	def __call__(	self:	Optional[int],				lowerCamelCase_:	Tuple=None,				lowerCamelCase_:	int=None,				lowerCamelCase_:	List[Any]=None,				lowerCamelCase_:	Optional[Any]=None,				lowerCamelCase_:	List[Any]=None,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	Union[bool, str, PaddingStrategy] = False,				lowerCamelCase_:	Union[bool, str, TruncationStrategy] = None,				lowerCamelCase_:	Optional[int] = None,				lowerCamelCase_:	int = 0,				lowerCamelCase_:	Optional[int] = None,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	bool = False,				lowerCamelCase_:	bool = False,				lowerCamelCase_:	bool = False,				lowerCamelCase_:	bool = False,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	Optional[Union[str, TensorType]] = None,				**lowerCamelCase_:	str,				):
			# 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`' )

					lowercase__				:			Any            =		self.feature_extractor(lowerCamelCase_ )
					lowercase__				:			Union[str, Any]            =		features['nodes']
					lowercase__				:			Union[str, Any]            =		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(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			int            =		[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

			lowercase__				:			Optional[Any]            =		self.tokenizer(
			    text=questions if questions is not None else nodes,				text_pair=nodes if questions is not None else None,				xpaths=lowerCamelCase_,				node_labels=lowerCamelCase_,				add_special_tokens=lowerCamelCase_,				padding=lowerCamelCase_,				truncation=lowerCamelCase_,				max_length=lowerCamelCase_,				stride=lowerCamelCase_,				pad_to_multiple_of=lowerCamelCase_,				return_token_type_ids=lowerCamelCase_,				return_attention_mask=lowerCamelCase_,				return_overflowing_tokens=lowerCamelCase_,				return_special_tokens_mask=lowerCamelCase_,				return_offsets_mapping=lowerCamelCase_,				return_length=lowerCamelCase_,				verbose=lowerCamelCase_,				return_tensors=lowerCamelCase_,				**lowerCamelCase_,				)

			return encoded_inputs

	def       snake_case__(	self:	Dict,				*lowerCamelCase_:	Optional[Any],				**lowerCamelCase_:	List[str] ):
			return self.tokenizer.batch_decode(*lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	str,				*lowerCamelCase_:	int,				**lowerCamelCase_:	Optional[Any] ):
			return self.tokenizer.decode(*lowerCamelCase_,				**lowerCamelCase_ )





	@property
	def       snake_case__(	self:	Tuple ):
			lowercase__				:			str            =		self.tokenizer.model_input_names
			return tokenizer_input_names









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:  Any   =   {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   ["""DetaImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[str]   =   [
			    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DetaForObjectDetection""",
			    """DetaModel""",
			    """DetaPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_deta import DetaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deta import (
						    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetaForObjectDetection,
						    DetaModel,
						    DetaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  int   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


