






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

def        snake_case   (     A__		):
			UpperCAmelCase_  :				List[str]      =      (images / 2 + 0.5).clamp(0			,1		)
			UpperCAmelCase_  :				Any      =      images.cpu().permute(0			,2			,3			,1		).float().numpy()
			UpperCAmelCase_  :				List[str]      =      numpy_to_pil(A__		)
			return images




def        snake_case   (     A__		):
			if images.ndim == 3:
						UpperCAmelCase_  :				Any      =      images[None, ...]
			UpperCAmelCase_  :				str      =      (images * 2_55).round().astype("uint8"		)
			if images.shape[-1] == 1:
						# special case for grayscale (single channel) images
						UpperCAmelCase_  :				Optional[int]      =      [Image.fromarray(image.squeeze()			,mode="L"		) for image in images]
			else:
						UpperCAmelCase_  :				Union[str, Any]      =      [Image.fromarray(A__		) for image in images]

			return pil_images











"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



