"""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   (		A      ):






     '''simple docstring'''



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






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				):
        '''simple docstring'''



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


   @torch.no_grad()
   def __call__(     self,				_UpperCAmelCase = None,				_UpperCAmelCase = 1,				_UpperCAmelCase = 100,				_UpperCAmelCase = 0.0,				_UpperCAmelCase = None,				_UpperCAmelCase = "pil",				_UpperCAmelCase = True,				):
        '''simple docstring'''



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

        if isinstance(_UpperCAmelCase,				PIL.Image.Image  ):
             lowercase__				 =							preprocess(_UpperCAmelCase  )

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

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

        lowercase__				 =							randn_tensor(_UpperCAmelCase,				generator=_UpperCAmelCase,				device=self.device,				dtype=_UpperCAmelCase  )

        lowercase__				 =							image.to(device=self.device,				dtype=_UpperCAmelCase  )

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

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

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

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

        if output_type == "pil":
             lowercase__				 =							self.numpy_to_pil(_UpperCAmelCase  )

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_UpperCAmelCase  )





"""simple docstring"""


lowerCAmelCase_:					Union[str, Any]     =					[
    9_9_9,
    8_0_0,
    7_9_9,
    6_0_0,
    5_9_9,
    5_0_0,
    4_0_0,
    3_9_9,
    3_7_7,
    3_5_5,
    3_3_3,
    3_1_1,
    2_8_8,
    2_6_6,
    2_4_4,
    2_2_2,
    2_0_0,
    1_9_9,
    1_7_7,
    1_5_5,
    1_3_3,
    1_1_1,
    8_8,
    6_6,
    4_4,
    2_2,
    0,
]

lowerCAmelCase_:					List[str]     =					[
    9_9_9,
    9_7_6,
    9_5_2,
    9_2_8,
    9_0_5,
    8_8_2,
    8_5_8,
    8_5_7,
    8_1_0,
    7_6_2,
    7_1_5,
    7_1_4,
    5_7_2,
    4_2_9,
    4_2_8,
    2_8_6,
    2_8_5,
    2_3_8,
    1_9_0,
    1_4_3,
    1_4_2,
    1_1_8,
    9_5,
    7_1,
    4_7,
    2_4,
    0,
]

lowerCAmelCase_:					List[str]     =					[
    9_9_9,
    9_8_8,
    9_7_7,
    9_6_6,
    9_5_5,
    9_4_4,
    9_3_3,
    9_2_2,
    9_1_1,
    9_0_0,
    8_9_9,
    8_7_9,
    8_5_9,
    8_4_0,
    8_2_0,
    8_0_0,
    7_9_9,
    7_6_6,
    7_3_3,
    7_0_0,
    6_9_9,
    6_5_0,
    6_0_0,
    5_9_9,
    5_0_0,
    4_9_9,
    4_0_0,
    3_9_9,
    3_5_0,
    3_0_0,
    2_9_9,
    2_6_6,
    2_3_3,
    2_0_0,
    1_9_9,
    1_7_9,
    1_5_9,
    1_4_0,
    1_2_0,
    1_0_0,
    9_9,
    8_8,
    7_7,
    6_6,
    5_5,
    4_4,
    3_3,
    2_2,
    1_1,
    0,
]

lowerCAmelCase_:					Dict     =					[
    9_9_9,
    9_9_5,
    9_9_2,
    9_8_9,
    9_8_5,
    9_8_1,
    9_7_8,
    9_7_5,
    9_7_1,
    9_6_7,
    9_6_4,
    9_6_1,
    9_5_7,
    9_5_6,
    9_5_1,
    9_4_7,
    9_4_2,
    9_3_7,
    9_3_3,
    9_2_8,
    9_2_3,
    9_1_9,
    9_1_4,
    9_1_3,
    9_0_8,
    9_0_3,
    8_9_7,
    8_9_2,
    8_8_7,
    8_8_1,
    8_7_6,
    8_7_1,
    8_7_0,
    8_6_4,
    8_5_8,
    8_5_2,
    8_4_6,
    8_4_0,
    8_3_4,
    8_2_8,
    8_2_7,
    8_2_0,
    8_1_3,
    8_0_6,
    7_9_9,
    7_9_2,
    7_8_5,
    7_8_4,
    7_7_7,
    7_7_0,
    7_6_3,
    7_5_6,
    7_4_9,
    7_4_2,
    7_4_1,
    7_3_3,
    7_2_4,
    7_1_6,
    7_0_7,
    6_9_9,
    6_9_8,
    6_8_8,
    6_7_7,
    6_6_6,
    6_5_6,
    6_5_5,
    6_4_5,
    6_3_4,
    6_2_3,
    6_1_3,
    6_1_2,
    5_9_8,
    5_8_4,
    5_7_0,
    5_6_9,
    5_5_5,
    5_4_1,
    5_2_7,
    5_2_6,
    5_0_5,
    4_8_4,
    4_8_3,
    4_6_2,
    4_4_0,
    4_3_9,
    3_9_6,
    3_9_5,
    3_5_2,
    3_5_1,
    3_0_8,
    3_0_7,
    2_6_4,
    2_6_3,
    2_2_0,
    2_1_9,
    1_7_6,
    1_3_2,
    8_8,
    4_4,
    0,
]

lowerCAmelCase_:					Optional[int]     =					[
    9_9_9,
    9_9_7,
    9_9_5,
    9_9_2,
    9_9_0,
    9_8_8,
    9_8_6,
    9_8_4,
    9_8_1,
    9_7_9,
    9_7_7,
    9_7_5,
    9_7_2,
    9_7_0,
    9_6_8,
    9_6_6,
    9_6_4,
    9_6_1,
    9_5_9,
    9_5_7,
    9_5_6,
    9_5_4,
    9_5_1,
    9_4_9,
    9_4_6,
    9_4_4,
    9_4_1,
    9_3_9,
    9_3_6,
    9_3_4,
    9_3_1,
    9_2_9,
    9_2_6,
    9_2_4,
    9_2_1,
    9_1_9,
    9_1_6,
    9_1_4,
    9_1_3,
    9_1_0,
    9_0_7,
    9_0_5,
    9_0_2,
    8_9_9,
    8_9_6,
    8_9_3,
    8_9_1,
    8_8_8,
    8_8_5,
    8_8_2,
    8_7_9,
    8_7_7,
    8_7_4,
    8_7_1,
    8_7_0,
    8_6_7,
    8_6_4,
    8_6_1,
    8_5_8,
    8_5_5,
    8_5_2,
    8_4_9,
    8_4_6,
    8_4_3,
    8_4_0,
    8_3_7,
    8_3_4,
    8_3_1,
    8_2_8,
    8_2_7,
    8_2_4,
    8_2_1,
    8_1_7,
    8_1_4,
    8_1_1,
    8_0_8,
    8_0_4,
    8_0_1,
    7_9_8,
    7_9_5,
    7_9_1,
    7_8_8,
    7_8_5,
    7_8_4,
    7_8_0,
    7_7_7,
    7_7_4,
    7_7_0,
    7_6_6,
    7_6_3,
    7_6_0,
    7_5_6,
    7_5_2,
    7_4_9,
    7_4_6,
    7_4_2,
    7_4_1,
    7_3_7,
    7_3_3,
    7_3_0,
    7_2_6,
    7_2_2,
    7_1_8,
    7_1_4,
    7_1_0,
    7_0_7,
    7_0_3,
    6_9_9,
    6_9_8,
    6_9_4,
    6_9_0,
    6_8_5,
    6_8_1,
    6_7_7,
    6_7_3,
    6_6_9,
    6_6_4,
    6_6_0,
    6_5_6,
    6_5_5,
    6_5_0,
    6_4_6,
    6_4_1,
    6_3_6,
    6_3_2,
    6_2_7,
    6_2_2,
    6_1_8,
    6_1_3,
    6_1_2,
    6_0_7,
    6_0_2,
    5_9_6,
    5_9_1,
    5_8_6,
    5_8_0,
    5_7_5,
    5_7_0,
    5_6_9,
    5_6_3,
    5_5_7,
    5_5_1,
    5_4_5,
    5_3_9,
    5_3_3,
    5_2_7,
    5_2_6,
    5_1_9,
    5_1_2,
    5_0_5,
    4_9_8,
    4_9_1,
    4_8_4,
    4_8_3,
    4_7_4,
    4_6_6,
    4_5_7,
    4_4_9,
    4_4_0,
    4_3_9,
    4_2_8,
    4_1_8,
    4_0_7,
    3_9_6,
    3_9_5,
    3_8_1,
    3_6_6,
    3_5_2,
    3_5_1,
    3_3_0,
    3_0_8,
    3_0_7,
    2_8_6,
    2_6_4,
    2_6_3,
    2_4_2,
    2_2_0,
    2_1_9,
    1_7_6,
    1_7_5,
    1_3_2,
    1_3_1,
    8_8,
    4_4,
    0,
]

lowerCAmelCase_:					Tuple     =					[
    9_9_9,
    9_9_1,
    9_8_2,
    9_7_4,
    9_6_6,
    9_5_8,
    9_5_0,
    9_4_1,
    9_3_3,
    9_2_5,
    9_1_6,
    9_0_8,
    9_0_0,
    8_9_9,
    8_7_4,
    8_5_0,
    8_2_5,
    8_0_0,
    7_9_9,
    7_0_0,
    6_0_0,
    5_0_0,
    4_0_0,
    3_0_0,
    2_0_0,
    1_0_0,
    0,
]

lowerCAmelCase_:					str     =					[
    9_9_9,
    9_9_2,
    9_8_5,
    9_7_8,
    9_7_1,
    9_6_4,
    9_5_7,
    9_4_9,
    9_4_2,
    9_3_5,
    9_2_8,
    9_2_1,
    9_1_4,
    9_0_7,
    9_0_0,
    8_9_9,
    8_7_9,
    8_5_9,
    8_4_0,
    8_2_0,
    8_0_0,
    7_9_9,
    7_6_6,
    7_3_3,
    7_0_0,
    6_9_9,
    6_5_0,
    6_0_0,
    5_9_9,
    5_0_0,
    4_9_9,
    4_0_0,
    3_9_9,
    3_0_0,
    2_9_9,
    2_0_0,
    1_9_9,
    1_0_0,
    9_9,
    0,
]

lowerCAmelCase_:					int     =					[
    9_9_9,
    9_9_6,
    9_9_2,
    9_8_9,
    9_8_5,
    9_8_2,
    9_7_9,
    9_7_5,
    9_7_2,
    9_6_8,
    9_6_5,
    9_6_1,
    9_5_8,
    9_5_5,
    9_5_1,
    9_4_8,
    9_4_4,
    9_4_1,
    9_3_8,
    9_3_4,
    9_3_1,
    9_2_7,
    9_2_4,
    9_2_0,
    9_1_7,
    9_1_4,
    9_1_0,
    9_0_7,
    9_0_3,
    9_0_0,
    8_9_9,
    8_9_1,
    8_8_4,
    8_7_6,
    8_6_9,
    8_6_1,
    8_5_3,
    8_4_6,
    8_3_8,
    8_3_0,
    8_2_3,
    8_1_5,
    8_0_8,
    8_0_0,
    7_9_9,
    7_8_8,
    7_7_7,
    7_6_6,
    7_5_5,
    7_4_4,
    7_3_3,
    7_2_2,
    7_1_1,
    7_0_0,
    6_9_9,
    6_8_8,
    6_7_7,
    6_6_6,
    6_5_5,
    6_4_4,
    6_3_3,
    6_2_2,
    6_1_1,
    6_0_0,
    5_9_9,
    5_8_5,
    5_7_1,
    5_5_7,
    5_4_2,
    5_2_8,
    5_1_4,
    5_0_0,
    4_9_9,
    4_8_5,
    4_7_1,
    4_5_7,
    4_4_2,
    4_2_8,
    4_1_4,
    4_0_0,
    3_9_9,
    3_7_9,
    3_5_9,
    3_4_0,
    3_2_0,
    3_0_0,
    2_9_9,
    2_7_9,
    2_5_9,
    2_4_0,
    2_2_0,
    2_0_0,
    1_9_9,
    1_6_6,
    1_3_3,
    1_0_0,
    9_9,
    6_6,
    3_3,
    0,
]




