





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        warnings.warn(
            '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use LayoutLMv2ImageProcessor instead.'''				,							_UpperCamelCase				,							)
        super().__init__(*_UpperCamelCase				,							**_UpperCamelCase					)






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_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				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_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				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)