


"""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"):
       UpperCAmelCase__  							={
           "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:
       UpperCAmelCase__  							={
           "linear": PIL.Image.LINEAR,
           "bilinear": PIL.Image.BILINEAR,
           "bicubic": PIL.Image.BICUBIC,
           "lanczos": PIL.Image.LANCZOS,
           "nearest": PIL.Image.NEAREST,
       }


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: int ):






 """simple docstring"""







 if images.ndim == 3:
  __lowercase									=	images[None, ...]
 __lowercase									=	(images * 255).round().astype("""uint8""" )
 if images.shape[-1] == 1:
  # special case for grayscale (single channel) images
  __lowercase									=	[Image.fromarray(image.squeeze()				,   mode="""L""" ) for image in images]
 else:
  __lowercase									=	[Image.fromarray(UpperCamelCase__ ) for image in images]

 return pil_images




"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	len(A_							)
       __lowercase									=	num_heads
       __lowercase									=	window_size
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	use_absolute_embeddings
       __lowercase									=	layer_norm_eps
       __lowercase									=	initializer_range
       __lowercase									=	encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       __lowercase									=	int(embed_dim * 2 ** (len(A_							) - 1)							)
       __lowercase									=	(0, 0, 0, 0)
