code: 
  
  
  import sys
  from typing import Tuple
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  
  from transformers.image_utils import PILImageResampling
  from utils import img_tensorize
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case=sys.maxsize					):
           snake_case					=       '''bilinear'''
           snake_case					=       max_size
           snake_case					=       short_edge_length
  
  
  
  
      def __call__( self    ,      __snake_case					):
           snake_case					=       []
           for img in imgs:
                snake_case				,						snake_case					=       img.shape[:2]
                # later: provide list and randomly choose index for resize
                snake_case					=       np.random.randint(self.short_edge_length[0]    ,      self.short_edge_length[1] + 1					)
                if size == 0:
                     return img
                snake_case					=       size * 1.0 / min(__snake_case    ,      __snake_case					)
                if h < w:
                     snake_case				,						snake_case					=       size, scale * w
                else:
                     snake_case				,						snake_case					=       scale * h, size
                if max(__snake_case    ,      __snake_case					) > self.max_size:
                     snake_case					=       self.max_size * 1.0 / max(__snake_case    ,      __snake_case					)
                     snake_case					=       newh * scale
                     snake_case					=       neww * scale
                snake_case					=       int(neww + 0.5					)
                snake_case					=       int(newh + 0.5					)
  
                if img.dtype == np.uinta:
                     snake_case					=       Image.fromarray(__snake_case					)
                     snake_case					=       pil_image.resize((neww, newh)    ,      PILImageResampling.BILINEAR					)
                     snake_case					=       np.asarray(__snake_case					)
                else:
                     snake_case					=       img.permute(2    ,      0    ,      1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
                     snake_case					=       nn.functional.interpolate(
                         __snake_case    ,      (newh, neww)    ,      mode=self.interp_method    ,      align_corners=__snake_case					).squeeze(0					)
                img_augs.append(__snake_case					)
  
           return img_augs
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,      cfg.INPUT.MAX_SIZE_TEST					)
           snake_case					=       cfg.INPUT.FORMAT
           snake_case					=       cfg.SIZE_DIVISIBILITY
           snake_case					=       cfg.PAD_VALUE
           snake_case					=       cfg.INPUT.MAX_SIZE_TEST
           snake_case					=       cfg.MODEL.DEVICE
           snake_case					=       torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)    ,      1    ,      1					)
           snake_case					=       torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)    ,      1    ,      1					)
           snake_case					=       lambda __snake_case					: (x - self.pixel_mean) / self.pixel_std
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       tuple(max(__snake_case					) for s in zip(*[img.shape for img in images]					)					)
           snake_case					=       [im.shape[-2:] for im in images]
           snake_case					=       [
               nn.functional.pad(
                   __snake_case    ,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,      value=self.pad_value    ,      )
               for size, im in zip(__snake_case    ,      __snake_case					)
           ]
  
           return torch.stack(__snake_case					), torch.tensor(__snake_case					)
  
  
  
  
      def __call__( self    ,      __snake_case    ,      __snake_case=False					):
           with torch.no_grad():
                if not isinstance(__snake_case    ,      __snake_case					):
                     snake_case					=       [images]
                if single_image:
                     assert len(__snake_case					) == 1
                for i in range(len(__snake_case					)					):
                     if isinstance(images[i]    ,      torch.Tensor					):
                          images.insert(__snake_case    ,      images.pop(__snake_case					).to(self.device					).float()					)
                     elif not isinstance(images[i]    ,      torch.Tensor					):
                          images.insert(
                              __snake_case    ,      torch.as_tensor(img_tensorize(images.pop(__snake_case					)    ,      input_format=self.input_format					)					)
                              .to(self.device					)
                              .float()    ,      )
              # resize smallest edge
                snake_case					=       torch.tensor([im.shape[:2] for im in images]					)
                snake_case					=       self.aug(__snake_case					)
                # transpose images and convert to torch tensors
                # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                # now normalize before pad to avoid useless arithmetic
                snake_case					=       [self.normalizer(__snake_case					) for x in images]
                # now pad them to do the following operations
                snake_case				,						snake_case					=       self.pad(__snake_case					)
                # Normalize
  
                if self.size_divisibility > 0:
                     raise NotImplementedError()
                # pad
                snake_case					=       torch.true_divide(__snake_case    ,      __snake_case					)
                if single_image:
                     return images[0], sizes[0], scales_yx[0]
                else:
                     return images, sizes, scales_yx
  
  
  
  
  
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       assert torch.isfinite(UpperCamelCase_       ).all(), "Box tensor contains infinite or NaN!"
       snake_case				,						snake_case					=       box_size
       tensor[:, 0].clamp_(min=0							,max=UpperCamelCase_       )
       tensor[:, 1].clamp_(min=0							,max=UpperCamelCase_       )
       tensor[:, 2].clamp_(min=0							,max=UpperCamelCase_       )
       tensor[:, 3].clamp_(min=0							,max=UpperCamelCase_       )
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  