




import warnings
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 		_lowerCAmelCase       (							UpperCamelCase__	):
			"""simple docstring"""
			lowerCAmelCase__           =['''image_processor''', '''tokenizer''']
			lowerCAmelCase__           ='''LayoutLMv3ImageProcessor'''
			lowerCAmelCase__           =('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')
			def __init__(					self     ,		__SCREAMING_SNAKE_CASE=None     ,		__SCREAMING_SNAKE_CASE=None     ,		**__SCREAMING_SNAKE_CASE       )		->							List[Any]:





				"""simple docstring"""







				snake_case__						:     Any		=None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''     ,		__A     ,		)
					snake_case__						:     str		=kwargs.pop('''feature_extractor'''       )

				snake_case__						:     List[str]		=image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''       )
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''       )

				super().__init__(__A     ,		__A       )
			def __call__(					self     ,		__SCREAMING_SNAKE_CASE     ,		__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     ,		)		->							BatchEncoding:





				"""simple docstring"""







				if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

				if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

				# first, apply the image processor
				snake_case__						:     Union[str, Any]		=self.image_processor(images=__A     ,		return_tensors=__A       )

				# second, apply the tokenizer
				if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(__A     ,		__A       ):
						snake_case__						:     List[str]		=[text]  # add batch dimension (as the image processor always adds a batch dimension)
					snake_case__						:     str		=features['''words''']

				snake_case__						:     Tuple		=self.tokenizer(
				    text=text if text is not None else features['''words''']     ,		text_pair=text_pair if text_pair is not None else None     ,		boxes=boxes if boxes is not None else features['''boxes''']     ,		word_labels=__A     ,		add_special_tokens=__A     ,		padding=__A     ,		truncation=__A     ,		max_length=__A     ,		stride=__A     ,		pad_to_multiple_of=__A     ,		return_token_type_ids=__A     ,		return_attention_mask=__A     ,		return_overflowing_tokens=__A     ,		return_special_tokens_mask=__A     ,		return_offsets_mapping=__A     ,		return_length=__A     ,		verbose=__A     ,		return_tensors=__A     ,		**__A     ,		)

				# add pixel values
				snake_case__						:     Dict		=features.pop('''pixel_values'''       )
				if return_overflowing_tokens is True:
					snake_case__						:     Union[str, Any]		=self.get_overflowing_images(__A     ,		encoded_inputs['''overflow_to_sample_mapping''']       )
				snake_case__						:     str		=images

				return encoded_inputs
			def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE       )		->							Any:





				"""simple docstring"""







				snake_case__						:     List[str]		=[]
				for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]       )

				if len(__A       ) != len(__A       ):
					raise ValueError(
					    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
					    f''' {len(__A       )} and {len(__A       )}'''       )

				return images_with_overflow
			def 					UpperCAmelCase     (					self     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							Any:





				"""simple docstring"""







				return self.tokenizer.batch_decode(*__A     ,		**__A       )
			def 					UpperCAmelCase     (					self     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							Optional[Any]:





				"""simple docstring"""







				return self.tokenizer.decode(*__A     ,		**__A       )
			@property
			def 					UpperCAmelCase     (					self       )		->							Tuple:





				"""simple docstring"""







				return ["input_ids", "bbox", "attention_mask", "pixel_values"]
			@property
			def 					UpperCAmelCase     (					self       )		->							Optional[Any]:





				"""simple docstring"""







				warnings.warn(
				    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		__A     ,		)
				return self.image_processor_class


			@property
			def 					UpperCAmelCase     (					self       )		->							Any:





				"""simple docstring"""







				warnings.warn(
				    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		__A     ,		)
				return self.image_processor











from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "AutoImageProcessor"
							lowerCamelCase_							     =       "AutoTokenizer"


							def __init__(				self     :Optional[int]				,					__A     :Optional[Any]				,					__A     :Dict		) -> Dict:



									"""simple docstring"""



									super().__init__(__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.image_processor


							def __call__(				self     :int				,					__A     :str=None				,					__A     :int=None				,					__A     :Union[str, Any]=None				,					**__A     :str		) -> Optional[Any]:



									"""simple docstring"""



									if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none."""		)

									if text is not None:
											SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					return_tensors=__A				,					**__A		)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :List[str]		) -> List[Any]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[str]				,					*__A     :Any				,					**__A     :Any		) -> Tuple:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									return ["input_ids", "attention_mask", "pixel_values"]