

'''simple docstring'''

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				):
				snake_case_							     = '''MarkupLMFeatureExtractor'''
				snake_case_							     = ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
				snake_case_							     = True

				def __call__(       self,						lowercase_=None,						lowercase_=None,						lowercase_=None,						lowercase_=None,						lowercase_=None,						lowercase_ = True,						lowercase_ = False,						lowercase_ = None,						lowercase_ = None,						lowercase_ = 0,						lowercase_ = None,						lowercase_ = None,						lowercase_ = None,						lowercase_ = False,						lowercase_ = False,						lowercase_ = False,						lowercase_ = False,						lowercase_ = True,						lowercase_ = None,						**lowercase_,						)    ->							BatchEncoding:
					# 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`' )

						snake_case							=					self.feature_extractor(lowercase_ )
						snake_case							=					features['nodes']
						snake_case							=					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(lowercase_,						lowercase_ ):
							snake_case							=					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

					return encoded_inputs

				def 					_lowerCamelCase    (       self,						*lowercase_,						**lowercase_ )    ->							Optional[int]:
					return self.tokenizer.batch_decode(*lowercase_,						**lowercase_ )

				def 					_lowerCamelCase    (       self,						*lowercase_,						**lowercase_ )    ->							Tuple:
					return self.tokenizer.decode(*lowercase_,						**lowercase_ )






				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.tokenizer.model_input_names
					return tokenizer_input_names













"""simple docstring"""



from typing import TYPE_CHECKING

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


A_     =				{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				['''DeformableDetrFeatureExtractor''']
						A_     =				['''DeformableDetrImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				[
						    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DeformableDetrForObjectDetection''',
						    '''DeformableDetrModel''',
						    '''DeformableDetrPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
												from .image_processing_deformable_detr import DeformableDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deformable_detr import (
												    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeformableDetrForObjectDetection,
												    DeformableDetrModel,
												    DeformableDetrPreTrainedModel,
												)

else:
						import sys

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






