



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase__			= transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				if isinstance(_SCREAMING_SNAKE_CASE				,     torch.Tensor    ):
								return image
				elif isinstance(_SCREAMING_SNAKE_CASE				,     PIL.Image.Image    ):
								__a       =		[image]

				__a       =		[trans(img.convert("""RGB"""    )    ) for img in image]
				__a       =		torch.stack(_SCREAMING_SNAKE_CASE    )
				return image






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def __init__(   self		:		List[str]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		List[str]	):

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

											# make sure scheduler can always be converted to DDIM
											__a       =		DDIMScheduler.from_config(scheduler.config	)

											self.register_modules(unet=__lowercase      ,				scheduler=__lowercase	)





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		int	):

											'''simple docstring'''
											if strength < 0 or strength > 1:
															raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"	)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Any      ,				__lowercase		:		int	):

											'''simple docstring'''
											# get the original timestep using init_timestep
											__a       =		min(int(num_inference_steps * strength	)      ,				__lowercase	)

											__a       =		max(num_inference_steps - init_timestep      ,				0	)
											__a       =		self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start





							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Any      ,				__lowercase		:		Tuple      ,				__lowercase		:		List[str]      ,				__lowercase		:		Tuple      ,				__lowercase		:		List[str]=None	):

											'''simple docstring'''
											if not isinstance(__lowercase      ,				(torch.Tensor, PIL.Image.Image, list)	):
															raise ValueError(
															    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowercase	)}"	)

											__a       =		image.to(device=__lowercase      ,				dtype=__lowercase	)

											if isinstance(__lowercase      ,				__lowercase	) and len(__lowercase	) != batch_size:
															raise ValueError(
															    F"You have passed a list of generators of length {len(__lowercase	)}, but requested an effective batch"
															    F" size of {batch_size}. Make sure the batch size matches the length of the generators."	)

											__a       =		init_latents.shape
											__a       =		randn_tensor(__lowercase      ,				generator=__lowercase      ,				device=__lowercase      ,				dtype=__lowercase	)

											# get latents
											print("""add noise to latents at timestep"""      ,				__lowercase	)
											__a       =		self.scheduler.add_noise(__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		init_latents

											return latents







							@torch.no_grad()
							def __call__(   self		:		str      ,				__lowercase		:		Union[torch.FloatTensor, PIL.Image.Image] = None      ,				__lowercase		:		float = 0.8      ,				__lowercase		:		int = 1      ,				__lowercase		:		Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 50      ,				__lowercase		:		Optional[bool] = None      ,				__lowercase		:		Optional[str] = "pil"      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											self.check_inputs(__lowercase	)

											# 2. Preprocess image
											__a       =		preprocess(__lowercase	)

											# 3. set timesteps
											self.scheduler.set_timesteps(__lowercase      ,				device=self.device	)
											__a     ,					__a       =		self.get_timesteps(__lowercase      ,				__lowercase      ,				self.device	)
											__a       =		timesteps[:1].repeat(__lowercase	)

											# 4. Prepare latent variables
											__a       =		self.prepare_latents(__lowercase      ,				__lowercase      ,				__lowercase      ,				self.unet.dtype      ,				self.device      ,				__lowercase	)
											__a       =		latents

											# 5. Denoising loop
											for t in self.progress_bar(__lowercase	):
															# 1. predict noise model_output
															__a       =		self.unet(__lowercase      ,				__lowercase	).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															__a       =		self.scheduler.step(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				eta=__lowercase      ,				use_clipped_model_output=__lowercase      ,				generator=__lowercase      ,				).prev_sample

											__a       =		(image / 2 + 0.5).clamp(0      ,				1	)
											__a       =		image.cpu().permute(0      ,				2      ,				3      ,				1	).numpy()
											if output_type == "pil":
															__a       =		self.numpy_to_pil(__lowercase	)

											if not return_dict:
															return (image, latent_timestep.item())

											return ImagePipelineOutput(images=__lowercase	)





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
