

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 							SCREAMING_SNAKE_CASE__						(    lowerCamelCase__						):





					"""simple docstring"""






					a_     =		["""image_processor""", """tokenizer"""]
					a_     =		"""LayoutLMv3ImageProcessor"""
					a_     =		("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



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

						snake_case__			:				Optional[int]       =  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__(snake_case__							,		snake_case__	)



					def __call__( self					:  List[str]							,		__A					:  List[str]							,		__A					:  Any = None							,		__A					:  List[Any] = None							,		__A					:  int = None							,		__A					:  str = None							,		__A					:  Tuple = True							,		__A					:  Optional[Any] = False							,		__A					:  List[Any] = None							,		__A					:  Tuple = None							,		__A					:  Any = 0							,		__A					:  Union[str, Any] = None							,		__A					:  int = None							,		__A					:  List[Any] = None							,		__A					:  List[str] = False							,		__A					:  Union[str, Any] = False							,		__A					:  Any = False							,		__A					:  Optional[Any] = False							,		__A					:  Dict = True							,		__A					:  int = None							,		**__A					:  Any							,		):





						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__			:				Optional[int]       =  self.image_processor(images=snake_case__							,		return_tensors=snake_case__	)

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

						snake_case__			:				Any       =  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=snake_case__							,		add_special_tokens=snake_case__							,		padding=snake_case__							,		truncation=snake_case__							,		max_length=snake_case__							,		stride=snake_case__							,		pad_to_multiple_of=snake_case__							,		return_token_type_ids=snake_case__							,		return_attention_mask=snake_case__							,		return_overflowing_tokens=snake_case__							,		return_special_tokens_mask=snake_case__							,		return_offsets_mapping=snake_case__							,		return_length=snake_case__							,		verbose=snake_case__							,		return_tensors=snake_case__							,		**snake_case__							,		)

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

						return encoded_inputs



					def 	_lowercase					( self					:  Any							,		__A					:  Union[str, Any]							,		__A					:  Any	):
						snake_case__			:				Any       =  []
						for sample_idx in overflow_to_sample_mapping:
							images_with_overflow.append(images[sample_idx]	)

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

						return images_with_overflow



					def 	_lowercase					( self					:  Any							,		*__A					:  Optional[Any]							,		**__A					:  List[Any]	):
						return self.tokenizer.batch_decode(*snake_case__							,		**snake_case__	)



					def 	_lowercase					( self					:  int							,		*__A					:  Any							,		**__A					:  int	):
						return self.tokenizer.decode(*snake_case__							,		**snake_case__	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						return ["input_ids", "bbox", "attention_mask", "pixel_values"]



					@property
					def 	_lowercase					( self					:  List[Any]	):
						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,		snake_case__							,		)
						return self.image_processor_class






					@property
					def 	_lowercase					( self					:  Tuple	):
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,		snake_case__							,		)
						return self.image_processor











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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["image_processor", "tokenizer"]
					a_     =		"ViltImageProcessor"
					a_     =		("BertTokenizer", "BertTokenizerFast")



					def __init__( self					:  Optional[int]							,		__A					:  Optional[int]=None							,		__A					:  Optional[Any]=None							,		**__A					:  int	):
						snake_case__			:				Union[str, 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__			:				Tuple       =  kwargs.pop("feature_extractor"	)

						snake_case__			:				Any       =  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	)
						snake_case__			:				Tuple       =  self.image_processor



					def __call__( self					:  List[Any]							,		__A					:  int							,		__A					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,		__A					:  bool = True							,		__A					:  Union[bool, str, PaddingStrategy] = False							,		__A					:  Union[bool, str, TruncationStrategy] = None							,		__A					:  Optional[int] = None							,		__A					:  int = 0							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[bool] = None							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = True							,		__A					:  Optional[Union[str, TensorType]] = None							,		**__A					:  List[Any]							,		):
						snake_case__			:				Optional[int]       =  self.tokenizer(
						    text=__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 + pixel_mask
						snake_case__			:				Optional[Any]       =  self.image_processor(__A							,		return_tensors=__A	)
						encoding.update(__A	)

						return encoding



					def 	_lowercase					( self					:  Optional[Any]							,		*__A					:  List[str]							,		**__A					:  Optional[int]	):
						return self.tokenizer.batch_decode(*__A							,		**__A	)



					def 	_lowercase					( self					:  Dict							,		*__A					:  str							,		**__A					:  str	):
						return self.tokenizer.decode(*__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  self.tokenizer.model_input_names
						snake_case__			:				Any       =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						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 	_lowercase					( self					:  str	):
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,		__A							,		)
						return self.image_processor



