





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_							=	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_							=	('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




     def __init__(		self				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						**lowerCAmelCase_  )		->							List[Any]:
          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.'  )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'  )

          # 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[str]:
          return self.tokenizer.batch_decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )




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




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




     @property
     def 	lowerCAmelCase			(		self  )		->							Optional[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  )		->							List[str]:
          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



