






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__					: Optional[Any]=	logging.get_logger(__name__)
class    __lowerCamelCase    (  _a							,	_a  ):
   a      :				Optional[Any] 						="""maskformer-swin"""

   a      :				int 						={
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }

   def __init__(					self ,				snake_case_=224 ,				snake_case_=4 ,				snake_case_=3 ,				snake_case_=96 ,				snake_case_=[2, 2, 6, 2] ,				snake_case_=[3, 6, 12, 24] ,				snake_case_=7 ,				snake_case_=4.0 ,				snake_case_=True ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.1 ,				snake_case_="gelu" ,				snake_case_=False ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=None ,				snake_case_=None ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(**snake_case_      )

       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    embed_dim
       UpperCamelCase__         =    depths
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    num_heads
       UpperCamelCase__         =    window_size
       UpperCamelCase__         =    mlp_ratio
       UpperCamelCase__         =    qkv_bias
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    drop_path_rate
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    use_absolute_embeddings
       UpperCamelCase__         =    layer_norm_eps
       UpperCamelCase__         =    initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       UpperCamelCase__         =    int(embed_dim * 2 ** (len(snake_case_      ) - 1)      )
       UpperCamelCase__         =    ['stem'] + [F'stage{idx}' for idx in range(1 ,				len(snake_case_      ) + 1      )]
       UpperCamelCase__       ,						UpperCamelCase__         =    get_aligned_output_features_output_indices(
           out_features=snake_case_ ,				out_indices=snake_case_ ,				stage_names=self.stage_names      )











"""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




