'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 						__A							(     UpperCAmelCase					)     ->			Optional[int]:




						'''simple docstring'''
						_UpperCamelCase   			=						image.size
						_UpperCamelCase   			=						(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						_UpperCamelCase   			=						image.resize((w, h)     ,resample=PIL_INTERPOLATION["lanczos"]					)
						_UpperCamelCase   			=						np.array(snake_case__					).astype(np.floataa					) / 2_5_5.0
						_UpperCamelCase   			=						image[None].transpose(0     ,3     ,1     ,2					)
						_UpperCamelCase   			=						torch.from_numpy(snake_case__					)
						return 2.0 * image - 1.0






class 		SCREAMING_SNAKE_CASE     (					__a	):







				'''simple docstring'''





				def __init__(						self					:						Any       ,		lowercase__					:						VQModel       ,		lowercase__					:						UNetaDModel       ,		lowercase__					:						Union[
				        DDIMScheduler,
				        PNDMScheduler,
				        LMSDiscreteScheduler,
				        EulerDiscreteScheduler,
				        EulerAncestralDiscreteScheduler,
				        DPMSolverMultistepScheduler,
				    ]       ,		)							->Union[str, Any]:


										'''simple docstring'''




										super().__init__()
										self.register_modules(vqvae=a_       ,		unet=a_       ,		scheduler=a_					)







				@torch.no_grad()
				def __call__(						self					:						Dict       ,		lowercase__					:						Union[torch.Tensor, PIL.Image.Image] = None       ,		lowercase__					:						Optional[int] = 1       ,		lowercase__					:						Optional[int] = 100       ,		lowercase__					:						Optional[float] = 0.0       ,		lowercase__					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		lowercase__					:						Optional[str] = "pil"       ,		lowercase__					:						bool = True       ,		)							->Optional[int]:


										'''simple docstring'''




										if isinstance(a_       ,		PIL.Image.Image					):
																_UpperCamelCase   			=						1
										elif isinstance(a_       ,		torch.Tensor					):
																_UpperCamelCase   			=						image.shape[0]
										else:
																raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(a_					)}'''					)

										if isinstance(a_       ,		PIL.Image.Image					):
																_UpperCamelCase   			=						preprocess(a_					)

										_UpperCamelCase   			=						image.shape[-2:]

										# in_channels should be 6: 3 for latents, 3 for low resolution image
										_UpperCamelCase   			=						(batch_size, self.unet.config.in_channels // 2, height, width)
										_UpperCamelCase   			=						next(self.unet.parameters()					).dtype

										_UpperCamelCase   			=						randn_tensor(a_       ,		generator=a_       ,		device=self.device       ,		dtype=a_					)

										_UpperCamelCase   			=						image.to(device=self.device       ,		dtype=a_					)

										# set timesteps and move to the correct device
										self.scheduler.set_timesteps(a_       ,		device=self.device					)
										_UpperCamelCase   			=						self.scheduler.timesteps

										# scale the initial noise by the standard deviation required by the scheduler
										_UpperCamelCase   			=						latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										_UpperCamelCase   			=						"""eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
										_UpperCamelCase   			=						{}
										if accepts_eta:
																_UpperCamelCase   			=						eta

										for t in self.progress_bar(a_					):
																# concat latents and low resolution image in the channel dimension.
																_UpperCamelCase   			=						torch.cat([latents, image]       ,		dim=1					)
																_UpperCamelCase   			=						self.scheduler.scale_model_input(a_       ,		a_					)
																# predict the noise residual
																_UpperCamelCase   			=						self.unet(a_       ,		a_					).sample
																# compute the previous noisy sample x_t -> x_t-1
																_UpperCamelCase   			=						self.scheduler.step(a_       ,		a_       ,		a_       ,		**a_					).prev_sample

										# decode the image latents with the VQVAE
										_UpperCamelCase   			=						self.vqvae.decode(a_					).sample
										_UpperCamelCase   			=						torch.clamp(a_       ,		-1.0       ,		1.0					)
										_UpperCamelCase   			=						image / 2 + 0.5
										_UpperCamelCase   			=						image.cpu().permute(0       ,		2       ,		3       ,		1					).numpy()

										if output_type == "pil":
																_UpperCamelCase   			=						self.numpy_to_pil(a_					)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=a_					)



'''simple docstring'''





lowerCAmelCase_	:		Union[str, Any]			=     """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCAmelCase_	:		List[Any]			=     [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase_	:		int			=     {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


