





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


__UpperCamelCase:  Optional[int]   =   logging.get_logger(__name__)

__UpperCamelCase:  Dict   =   {
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							"swiftformer"

	def __init__(	self:	Dict,				lowerCamelCase_:	List[Any]=3,				lowerCamelCase_:	Optional[Any]=[3, 3, 6, 4],				lowerCamelCase_:	Dict=[48, 56, 112, 220],				lowerCamelCase_:	Union[str, Any]=4,				lowerCamelCase_:	Union[str, Any]=[True, True, True, True],				lowerCamelCase_:	List[Any]="gelu",				lowerCamelCase_:	Dict=3,				lowerCamelCase_:	Tuple=2,				lowerCamelCase_:	Union[str, Any]=1,				lowerCamelCase_:	List[Any]=0.0,				lowerCamelCase_:	Optional[int]=True,				lowerCamelCase_:	Any=1E-5,				lowerCamelCase_:	Union[str, Any]=1E-5,				**lowerCamelCase_:	Optional[int],				):
			super().__init__(**lowerCamelCase_ )
			lowercase__				:			str            =		num_channels
			lowercase__				:			str            =		depths
			lowercase__				:			List[Any]            =		embed_dims
			lowercase__				:			str            =		mlp_ratio
			lowercase__				:			List[str]            =		downsamples
			lowercase__				:			str            =		hidden_act
			lowercase__				:			int            =		down_patch_size
			lowercase__				:			Optional[int]            =		down_stride
			lowercase__				:			List[str]            =		down_pad
			lowercase__				:			List[Any]            =		drop_path_rate
			lowercase__				:			Optional[int]            =		use_layer_scale
			lowercase__				:			Tuple            =		layer_scale_init_value
			lowercase__				:			str            =		batch_norm_eps


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							version.parse("1.11"			)

	@property
	def       snake_case__(	self:	Optional[int] ):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ] )





	@property
	def       snake_case__(	self:	str ):
			return 1E-4









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				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		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


