
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__     (	__lowercase			):
		a__       :						str        =		["""image_processor""", """tokenizer"""]
		a__       :						Tuple        =		"""LayoutLMv3ImageProcessor"""
		a__       :						Dict        =		("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=None							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
										    ''' instead.'''							,			SCREAMING_SNAKE_CASE__							,			)
										__lowerCamelCase    					=	kwargs.pop('''feature_extractor'''  )

						__lowerCamelCase    					=	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__(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def __call__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,			SCREAMING_SNAKE_CASE__		:							Union[List[List[int]], List[List[List[int]]]] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Union[List[int], List[List[int]]]] = None							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							Union[bool, str, PaddingStrategy] = False							,			SCREAMING_SNAKE_CASE__		:							Union[bool, str, TruncationStrategy] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							int = 0							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[bool] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[bool] = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							Optional[Union[str, TensorType]] = None							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]							,			)     ->							BatchEncoding:
						# verify input
						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
						__lowerCamelCase    					=	self.image_processor(images=SCREAMING_SNAKE_CASE__							,			return_tensors=SCREAMING_SNAKE_CASE__  )

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

						__lowerCamelCase    					=	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=SCREAMING_SNAKE_CASE__							,			add_special_tokens=SCREAMING_SNAKE_CASE__							,			padding=SCREAMING_SNAKE_CASE__							,			truncation=SCREAMING_SNAKE_CASE__							,			max_length=SCREAMING_SNAKE_CASE__							,			stride=SCREAMING_SNAKE_CASE__							,			pad_to_multiple_of=SCREAMING_SNAKE_CASE__							,			return_token_type_ids=SCREAMING_SNAKE_CASE__							,			return_attention_mask=SCREAMING_SNAKE_CASE__							,			return_overflowing_tokens=SCREAMING_SNAKE_CASE__							,			return_special_tokens_mask=SCREAMING_SNAKE_CASE__							,			return_offsets_mapping=SCREAMING_SNAKE_CASE__							,			return_length=SCREAMING_SNAKE_CASE__							,			verbose=SCREAMING_SNAKE_CASE__							,			return_tensors=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)

						# add pixel values
						__lowerCamelCase    					=	features.pop('''pixel_values'''  )
						if return_overflowing_tokens is True:
										__lowerCamelCase    					=	self.get_overflowing_images(SCREAMING_SNAKE_CASE__							,			encoded_inputs['''overflow_to_sample_mapping''']  )
						__lowerCamelCase    					=	images

						return encoded_inputs






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Optional[Any]:
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						__lowerCamelCase    					=	[]
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]  )

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

						return images_with_overflow






		def  __A       (		self		:							Dict							,			*SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			**SCREAMING_SNAKE_CASE__		:							Any  )     ->							List[Any]:
						return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[Any]							,			*SCREAMING_SNAKE_CASE__		:							Dict							,			**SCREAMING_SNAKE_CASE__		:							int  )     ->							Any:
						return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		@property
		def  __A       (		self		:							Optional[int]  )     ->							Tuple:
						return ["input_ids", "bbox", "attention_mask", "pixel_values"]






		@property
		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						warnings.warn(
						    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,			SCREAMING_SNAKE_CASE__							,			)
						return self.image_processor_class







		@property
		def  __A       (		self		:							Union[str, Any]  )     ->							str:
						warnings.warn(
						    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,			SCREAMING_SNAKE_CASE__							,			)
						return self.image_processor





from abc import ABC, abstractmethod
from argparse import ArgumentParser

class    lowerCAmelCase__     (	__lowercase			):






		@staticmethod
		@abstractmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							ArgumentParser  )     ->							str:
						raise NotImplementedError()







		@abstractmethod
		def  __A       (		self		:							Optional[int]  )     ->							Union[str, Any]:
						raise NotImplementedError()



