



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

class 					_A (			__UpperCAmelCase					):



							def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  VQModel  ,	__SCREAMING_SNAKE_CASE							:  UNetaDModel  ,	__SCREAMING_SNAKE_CASE							:  Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]  ,	):


											'''simple docstring'''

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





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


											'''simple docstring'''

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

											if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
															__a		 =       preprocess(__SCREAMING_SNAKE_CASE)

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

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

											__a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

											__a		 =       image.to(device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

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

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

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

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

											if output_type == "pil":
															__a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)










# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


__snake_case		:List[str]   	=  re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')

@total_ordering
@dataclass
class 					_A :
       UpperCamelCase__						:					str
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None



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


           '''simple docstring'''

           __a ,     __a ,     __a		 =       _str_to_version_tuple(self.version_str)



       def __repr__(	self							:  Tuple):


           '''simple docstring'''

           return F'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'



       @property
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           return self.major, self.minor, self.patch



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               return Version(__SCREAMING_SNAKE_CASE)
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               return other
           raise TypeError(F'{other} (type {type(__SCREAMING_SNAKE_CASE)}) cannot be compared to version.')



       def __eq__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           try:
               __a		 =       self._validate_operand(__SCREAMING_SNAKE_CASE)
           except (TypeError, ValueError):
               return False
           else:
               return self.tuple == other.tuple



       def __lt__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       self._validate_operand(__SCREAMING_SNAKE_CASE)
           return self.tuple < other.tuple



       def __hash__(	self							:  Optional[Any]):


           '''simple docstring'''

           return hash(_version_tuple_to_str(self.tuple))



       @classmethod
       def 	_lowerCamelCase    (	cls							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       {f.name for f in dataclasses.fields(cls)}
           return cls(**{k: v for k, v in dic.items() if k in field_names})





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.version_str





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       _VERSION_REG.match(_UpperCAmelCase       )
    if not res:
        raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
    return tuple(int(_UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )





def   __snake_case			(    _UpperCAmelCase       ):
    return ".".join(str(_UpperCAmelCase       ) for v in version_tuple       )





