





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 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_							=	'''LayoutLMv3ImageProcessor'''
					lowerCAmelCase_							=	('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')




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

										_snake_case									=				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__(lowerCAmelCase_				,						lowerCAmelCase_  )




					def __call__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							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
										_snake_case									=				self.image_processor(images=lowerCAmelCase_				,						return_tensors=lowerCAmelCase_  )

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

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

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

										return encoded_inputs




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							List[str]:
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										_snake_case									=				[]
										for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]  )

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

										return images_with_overflow




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							List[Any]:
										return self.tokenizer.batch_decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Any:
										return self.tokenizer.decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					@property
					def 	lowerCAmelCase			(		self  )		->							Dict:
										return ["input_ids", "bbox", "attention_mask", "pixel_values"]




					@property
					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,						lowerCAmelCase_				,						)
										return self.image_processor_class






					@property
					def 	lowerCAmelCase			(		self  )		->							Tuple:
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,						lowerCAmelCase_				,						)
										return self.image_processor










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''vit_msn'''




					def __init__(		self				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=3072				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=1E-06				,						lowerCAmelCase_=224				,						lowerCAmelCase_=16				,						lowerCAmelCase_=3				,						lowerCAmelCase_=True				,						**lowerCAmelCase_				,						)		->							str:
										super().__init__(**lowerCAmelCase_  )

										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				initializer_range
										_snake_case									=				layer_norm_eps
										_snake_case									=				image_size
										_snake_case									=				patch_size
										_snake_case									=				num_channels
										_snake_case									=				qkv_bias



