
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 					__lowercase		(					A							,       A       ):


					def __init__(	self						)		->							Any:





									'''simple docstring'''
									super().__init__()

									self.register_buffer('''watermark_image'''       ,       torch.zeros((6_2, 6_2, 4)						)						)
									A_       	=  None


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=None						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  images[0].height
									A_       	=  images[0].width

									A_       	=  sample_size or h

									A_       	=  min(h / sample_size       ,       w / sample_size						)
									A_	,					A_       	=  (int(h / coef						), int(w / coef						)) if coef < 1 else (h, w)

									A_	,					A_       	=  1_0_2_4**2, img_w * img_h
									A_       	=  (Sa / Sa) ** 0.5
									A_	,					A_	,					A_       	=  int(K * 6_2						), img_w - int(1_4 * K						), img_h - int(1_4 * K						)

									if self.watermark_image_as_pil is None:
													A_       	=  self.watermark_image.to(torch.uinta						).cpu().numpy()
													A_       	=  Image.fromarray(a__       ,       mode='''RGBA'''						)
													A_       	=  watermark_image

									A_       	=  self.watermark_image_as_pil.resize(
									    (wm_size, wm_size)       ,       PIL_INTERPOLATION['''bicubic''']       ,       reducing_gap=a__						)

									for pil_img in images:
													pil_img.paste(a__       ,       box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)       ,       mask=wm_img.split()[-1]						)

									return images


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker