






"""simple docstring"""





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


A__					: List[Any]=	logging.get_logger(__name__)

A__					: Any=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class    __lowerCamelCase    (  _a  ):
   a      :				int 						="""glpn"""

   def __init__(					self ,				snake_case_=3 ,				snake_case_=4 ,				snake_case_=[2, 2, 2, 2] ,				snake_case_=[8, 4, 2, 1] ,				snake_case_=[32, 64, 160, 256] ,				snake_case_=[7, 3, 3, 3] ,				snake_case_=[4, 2, 2, 2] ,				snake_case_=[1, 2, 5, 8] ,				snake_case_=[4, 4, 4, 4] ,				snake_case_="gelu" ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.02 ,				snake_case_=0.1 ,				snake_case_=1E-6 ,				snake_case_=64 ,				snake_case_=10 ,				snake_case_=-1 ,				**snake_case_ ,				)		->       List[str]:
       super().__init__(**snake_case_      )

       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    num_encoder_blocks
       UpperCamelCase__         =    depths
       UpperCamelCase__         =    sr_ratios
       UpperCamelCase__         =    hidden_sizes
       UpperCamelCase__         =    patch_sizes
       UpperCamelCase__         =    strides
       UpperCamelCase__         =    mlp_ratios
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    initializer_range
       UpperCamelCase__         =    drop_path_rate
       UpperCamelCase__         =    layer_norm_eps
       UpperCamelCase__         =    decoder_hidden_size
       UpperCamelCase__         =    max_depth
       UpperCamelCase__         =    head_in_index











"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




