code: 
  
  
  from typing import List
  
  import PIL
  import torch
  from PIL import Image
  
  from ...configuration_utils import ConfigMixin
  from ...models.modeling_utils import ModelMixin
  from ...utils import PIL_INTERPOLATION
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       __lowerCamelCase  ,					__lowerCamelCase):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self)    ->       Optional[int]:
              super().__init__()
  
              self.register_buffer("watermark_image"				,		torch.zeros((6_2, 6_2, 4)))
              _A   : List[Any]       					=				None
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Tuple:
              # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287
  
              _A   : Union[str, Any]       					=				images[0].height
              _A   : Optional[Any]       					=				images[0].width
  
              _A   : int       					=				sample_size or h
  
              _A   : str       					=				min(h / sample_size				,		w / sample_size)
              _A   : Union[str, Any]       					=				(int(h / coef), int(w / coef)) if coef < 1 else (h, w)
  
              _A   : Union[str, Any]       					=				1_0_2_4**2, img_w * img_h
              _A   : int       					=				(Sa / Sa) ** 0.5
              _A   : Optional[Any]       					=				int(K * 6_2), img_w - int(1_4 * K), img_h - int(1_4 * K)
  
              if self.watermark_image_as_pil is None:
                     _A   : Optional[Any]       					=				self.watermark_image.to(torch.uinta).cpu().numpy()
                     _A   : str       					=				Image.fromarray(_lowerCAmelCase				,		mode="RGBA")
                     _A   : List[Any]       					=				watermark_image
  
              _A   : Tuple       					=				self.watermark_image_as_pil.resize(
                  (wm_size, wm_size)				,		PIL_INTERPOLATION["bicubic"]				,		reducing_gap=_lowerCAmelCase)
  
              for pil_img in images:
                     pil_img.paste(_lowerCAmelCase				,		box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,		mask=wm_img.split()[-1])
  
              return images
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetrFeatureExtractor']
      lowerCAmelCase__   	=				['DetrImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetrForObjectDetection',
          'DetrForSegmentation',
          'DetrModel',
          'DetrPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_detr import DetrFeatureExtractor
          from .image_processing_detr import DetrImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_detr import (
              DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetrForObjectDetection,
              DetrForSegmentation,
              DetrModel,
              DetrPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  