




"""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       UpperCAmelCase__ (snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case	,      _snake_case							:	Any			=						image.size
  _snake_case	,      _snake_case							:	Tuple			=						(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  _snake_case							:	int			=						image.resize((w, h)	,     resample=PIL_INTERPOLATION["""lanczos"""]					)
  _snake_case							:	Tuple			=						np.array(snake_case__					).astype(np.floataa					) / 2_55.0
  _snake_case							:	str			=						image[None].transpose(0	,     3	,     1	,     2					)
  _snake_case							:	str			=						torch.from_numpy(snake_case__					)
  return 2.0 * image - 1.0






class       lowercase(  __a				):







 '''simple docstring'''





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




   '''simple docstring'''



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


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




   '''simple docstring'''



   if isinstance(a_,   PIL.Image.Image	):
     _snake_case							:	List[Any]			=						1
   elif isinstance(a_,   torch.Tensor	):
     _snake_case							:	int			=						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							:	Optional[int]			=						preprocess(a_	)

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

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

   _snake_case							:	int			=						randn_tensor(a_,   generator=a_,   device=self.device,   dtype=a_	)

   _snake_case							:	List[str]			=						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							:	Optional[int]			=						self.scheduler.timesteps

   # scale the initial noise by the standard deviation required by the scheduler
   _snake_case							:	str			=						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							:	Tuple			=						"""eta""" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
   _snake_case							:	Optional[int]			=						{}
   if accepts_eta:
     _snake_case							:	Optional[Any]			=						eta

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

   # decode the image latents with the VQVAE
   _snake_case							:	Tuple			=						self.vqvae.decode(a_	).sample
   _snake_case							:	Dict			=						torch.clamp(a_,   -1.0,   1.0	)
   _snake_case							:	Optional[Any]			=						image / 2 + 0.5
   _snake_case							:	List[Any]			=						image.cpu().permute(0,   2,   3,   1	).numpy()

   if output_type == "pil":
     _snake_case							:	List[str]			=						self.numpy_to_pil(a_	)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=a_	)








"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      (IPNDMScheduler,)
 lowercase__				      =      (("num_inference_steps", 50),)


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




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""num_train_timesteps""": 1_000}
   config.update(**a_	)
   return config


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]=0,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						dict(self.forward_default_kwargs	)
   _snake_case							:	Optional[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[Any]			=						self.dummy_sample
   _snake_case							:	Dict			=						0.1 * sample
   _snake_case							:	Any			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	int			=						self.get_scheduler_config(**a_	)
     _snake_case							:	Dict			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)
     # copy over dummy past residuals
     _snake_case							:	int			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Union[str, Any]			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	Tuple			=						scheduler_class.from_pretrained(a_	)
       new_scheduler.set_timesteps(a_	)
       # copy over dummy past residuals
       _snake_case							:	Optional[Any]			=						dummy_past_residuals[:]

     _snake_case							:	List[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	str			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	Optional[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Optional[int]			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str,   a_:	Any=0,   **a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)
   _snake_case							:	List[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[int]			=						self.dummy_sample
   _snake_case							:	Tuple			=						0.1 * sample
   _snake_case							:	Optional[int]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Any			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)

     # copy over dummy past residuals (must be after setting timesteps)
     _snake_case							:	Union[str, Any]			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Tuple			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	List[str]			=						scheduler_class.from_pretrained(a_	)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(a_	)

       # copy over dummy past residual (must be after setting timesteps)
       _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	int			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.scheduler_classes[0]
   _snake_case							:	Any			=						self.get_scheduler_config(**a_	)
   _snake_case							:	List[Any]			=						scheduler_class(**a_	)

   _snake_case							:	Union[str, Any]			=						10
   _snake_case							:	Union[str, Any]			=						self.dummy_model()
   _snake_case							:	List[Any]			=						self.dummy_sample_deter
   scheduler.set_timesteps(a_	)

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Optional[Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Union[str, Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   return sample


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)

   _snake_case							:	int			=						kwargs.pop("""num_inference_steps""",   a_	)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Union[str, Any]			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)

     _snake_case							:	Dict			=						self.dummy_sample
     _snake_case							:	List[str]			=						0.1 * sample

     if num_inference_steps is not None and hasattr(a_,   """set_timesteps"""	):
       scheduler.set_timesteps(a_	)
     elif num_inference_steps is not None and not hasattr(a_,   """set_timesteps"""	):
       _snake_case							:	Dict			=						num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _snake_case							:	Union[str, Any]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	Optional[int]			=						scheduler.timesteps[5]
     _snake_case							:	Optional[Any]			=						scheduler.timesteps[6]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	List[str]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)

     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for timesteps in [100, 1_000]:
     self.check_over_configs(num_train_timesteps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for t, num_inference_steps in zip([1, 5, 10],   [10, 50, 100]	):
     self.check_over_forward(num_inference_steps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.full_loop()
   _snake_case							:	Optional[int]			=						torch.mean(torch.abs(a_	)	)

   assert abs(result_mean.item() - 2_540_529	) < 10


