





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      lowercase__    (							__lowerCamelCase  ):
  __UpperCamelCase	     =  ['''image_processor''', '''tokenizer''']
  __UpperCamelCase	     =  '''LayoutLMv3ImageProcessor'''
  __UpperCamelCase	     =  ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







  def __init__(	self    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase						):
       lowerCAmelCase_					:       Optional[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_    ,						)
            lowerCAmelCase_					:       Dict    =		kwargs.pop("""feature_extractor"""						)

       lowerCAmelCase_					:       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__(a_    ,						a_						)







  def __call__(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = True    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = False    ,						_lowercase = False    ,						_lowercase = False    ,						_lowercase = False    ,						_lowercase = True    ,						_lowercase = None    ,						**_lowercase    ,						):
       # 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_					:       Optional[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_						):
                 lowerCAmelCase_					:       Tuple    =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
            lowerCAmelCase_					:       Optional[Any]    =		features["words"]

       lowerCAmelCase_					:       Optional[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=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
       lowerCAmelCase_					:       Any    =		features.pop("""pixel_values"""						)
       if return_overflowing_tokens is True:
            lowerCAmelCase_					:       str    =		self.get_overflowing_images(a_    ,						encoded_inputs["""overflow_to_sample_mapping"""]						)
       lowerCAmelCase_					:       Union[str, Any]    =		images

       return encoded_inputs







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowerCAmelCase_					:       Any    =		[]
       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    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.batch_decode(*a_    ,						**a_						)







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.decode(*a_    ,						**a_						)







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







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







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """M-CLIP"""







  def __init__(	self    ,						_lowercase=1_024    ,						_lowercase=768    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		transformerDimSize
       lowerCAmelCase_					:       Optional[Any]    =		imageDimSize
       super().__init__(**_lowercase						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  MCLIPConfig







  def __init__(	self    ,						_lowercase    ,						*_lowercase    ,						**_lowercase						):
       super().__init__(_lowercase    ,						*_lowercase    ,						**_lowercase						)
       lowerCAmelCase_					:       str    =		XLMRobertaModel(_lowercase						)
       lowerCAmelCase_					:       str    =		torch.nn.Linear(
           in_features=config.transformerDimensions    ,						out_features=config.numDims						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		self.transformer(input_ids=_lowercase    ,						attention_mask=_lowercase						)[0]
       lowerCAmelCase_					:       Any    =		(embs * attention_mask.unsqueeze(2						)).sum(dim=1						) / attention_mask.sum(dim=1						)[:, None]
       return self.LinearTransformation(_lowercase						), embs
