

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 				snake_case_(_UpperCamelCase					)							->     str:



		"""simple docstring"""






		_snake_case,					_snake_case						=							image.size
		_snake_case,					_snake_case						=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		_snake_case						=							image.resize((w, h)			,	resample=PIL_INTERPOLATION['''lanczos''']					)
		_snake_case						=							np.array(lowerCAmelCase__					).astype(np.floataa					) / 255.0
		_snake_case						=							image[None].transpose(0			,	3			,	1			,	2					)
		_snake_case						=							torch.from_numpy(lowerCAmelCase__					)
		return 2.0 * image - 1.0



class   lowercase_   (  _SCREAMING_SNAKE_CASE					):



						def __init__(      self				:    int      , A__				:    Optional[int]      , A__				:    List[str]      , A__				:    Optional[int]      , )   ->					List[Any]:
								super().__init__()
								self.register_modules(vqvae=A__      , unet=A__      , scheduler=A__		)







						@torch.no_grad()
						def __call__(      self				:    Any      , A__				:    Optional[int] = None      , A__				:    Dict = 1      , A__				:    Any = 100      , A__				:    Any = 0.0      , A__				:    Tuple = None      , A__				:    Dict = "pil"      , A__				:    Tuple = True      , )   ->					Union[Tuple, ImagePipelineOutput]:
								if isinstance(A__      , PIL.Image.Image		):
										_snake_case						=							1
								elif isinstance(A__      , torch.Tensor		):
										_snake_case						=							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		):
										_snake_case						=							preprocess(A__		)

								_snake_case,					_snake_case						=							image.shape[-2:]

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

								_snake_case						=							randn_tensor(A__      , generator=A__      , device=self.device      , dtype=A__		)

								_snake_case						=							image.to(device=self.device      , dtype=A__		)

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

								# scale the initial noise by the standard deviation required by the scheduler
								_snake_case						=							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]
								_snake_case						=							'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
								_snake_case						=							{}
								if accepts_eta:
										_snake_case						=							eta

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

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

								if output_type == "pil":
										_snake_case						=							self.numpy_to_pil(A__		)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=A__		)




from ..utils import DummyObject, requires_backends



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[int]										=    ["speech"]



      def __init__(      self				:    str      , *A__				:    List[str]      , **A__				:    Tuple		)   ->					Optional[Any]:
        requires_backends(self      , ['''speech''']		)



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    ["speech"]



      def __init__(      self				:    Dict      , *A__				:    int      , **A__				:    int		)   ->					Tuple:
        requires_backends(self      , ['''speech''']		)

