


"""simple docstring"""



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Any				    =    (DDPMParallelScheduler,)







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : int    =       {
           "num_train_timesteps": 1000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "variance_type": "fixed_small",
           "clip_sample": True,
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,      [0.002, 0.02, 0.2, 2]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,      beta_end=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       self.check_over_configs(thresholding=UpperCamelCase__		)
       for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=UpperCamelCase__		,      prediction_type=UpperCamelCase__		,      sample_max_value=UpperCamelCase__		,      )







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       for t in [0, 500, 999]:
           self.check_over_forward(time_step=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Dict    =       self.scheduler_classes[0]
       snake_case       : int    =       self.get_scheduler_config()
       snake_case       : str    =       scheduler_class(**UpperCamelCase__		)

       assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.scheduler_classes[0]
       snake_case       : int    =       self.get_scheduler_config()
       snake_case       : Optional[int]    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Dict    =       len(UpperCamelCase__		)

       snake_case       : Union[str, Any]    =       self.dummy_model()
       snake_case       : Tuple    =       self.dummy_sample_deter
       snake_case       : str    =       self.dummy_sample_deter + 0.1
       snake_case       : Tuple    =       self.dummy_sample_deter - 0.1

       snake_case       : Tuple    =       samplea.shape[0]
       snake_case       : List[str]    =       torch.stack([samplea, samplea, samplea]		,      dim=0		)
       snake_case       : Optional[Any]    =       torch.arange(UpperCamelCase__		)[0:3, None].repeat(1		,      UpperCamelCase__		)

       snake_case       : Optional[Any]    =       model(samples.flatten(0		,      1		)		,      timesteps.flatten(0		,      1		)		)
       snake_case       : Union[str, Any]    =       scheduler.batch_step_no_noise(UpperCamelCase__		,      timesteps.flatten(0		,      1		)		,      samples.flatten(0		,      1		)		)

       snake_case       : List[str]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : List[Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 1153.1833		) < 1e-2
       assert abs(result_mean.item() - 0.5005		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Dict    =       self.scheduler_classes[0]
       snake_case       : Optional[int]    =       self.get_scheduler_config()
       snake_case       : str    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Optional[int]    =       len(UpperCamelCase__		)

       snake_case       : List[str]    =       self.dummy_model()
       snake_case       : Any    =       self.dummy_sample_deter
       snake_case       : Optional[int]    =       torch.manual_seed(0		)

       for t in reversed(range(UpperCamelCase__		)		):
           # 1. predict noise residual
           snake_case       : Union[str, Any]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           # 2. predict previous mean of sample x_t-1
           snake_case       : Dict    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      generator=UpperCamelCase__		).prev_sample

           snake_case       : int    =       pred_prev_sample

       snake_case       : Union[str, Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Optional[int]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 258.9606		) < 1e-2
       assert abs(result_mean.item() - 0.3372		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[str]    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case       : int    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : int    =       len(UpperCamelCase__		)

       snake_case       : int    =       self.dummy_model()
       snake_case       : List[Any]    =       self.dummy_sample_deter
       snake_case       : Tuple    =       torch.manual_seed(0		)

       for t in reversed(range(UpperCamelCase__		)		):
           # 1. predict noise residual
           snake_case       : List[Any]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           # 2. predict previous mean of sample x_t-1
           snake_case       : Optional[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      generator=UpperCamelCase__		).prev_sample

           snake_case       : int    =       pred_prev_sample

       snake_case       : int    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : List[Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 202.0296		) < 1e-2
       assert abs(result_mean.item() - 0.2631		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Tuple    =       self.scheduler_classes[0]
       snake_case       : Union[str, Any]    =       self.get_scheduler_config()
       snake_case       : List[Any]    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Dict    =       [100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=UpperCamelCase__		)

       snake_case       : Optional[int]    =       scheduler.timesteps

       for i, timestep in enumerate(UpperCamelCase__		):
           if i == len(UpperCamelCase__		) - 1:
               snake_case       : int    =       -1
           else:
               snake_case       : str    =       timesteps[i + 1]

           snake_case       : int    =       scheduler.previous_timestep(UpperCamelCase__		)
           snake_case       : int    =       prev_t.item()

           self.assertEqual(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : List[str]    =       self.get_scheduler_config()
       snake_case       : Optional[Any]    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : str    =       [100, 87, 50, 51, 0]

       with self.assertRaises(UpperCamelCase__		,      msg="`custom_timesteps` must be in descending order."		):
           scheduler.set_timesteps(timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : str    =       self.scheduler_classes[0]
       snake_case       : List[str]    =       self.get_scheduler_config()
       snake_case       : Tuple    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Dict    =       [100, 87, 50, 1, 0]
       snake_case       : Tuple    =       len(UpperCamelCase__		)

       with self.assertRaises(UpperCamelCase__		,      msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
           scheduler.set_timesteps(num_inference_steps=UpperCamelCase__		,      timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Optional[Any]    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Dict    =       [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           UpperCamelCase__		,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,      ):
           scheduler.set_timesteps(timesteps=UpperCamelCase__		)









"""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     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





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







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''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		,      )      ->						Union[Tuple, ImagePipelineOutput]:

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Tuple    =       1
       elif isinstance(UpperCamelCase__		,      torch.Tensor		):
           snake_case       : str    =       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		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

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

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

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

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

       # decode the image latents with the VQVAE
       snake_case       : Optional[int]    =       self.vqvae.decode(UpperCamelCase__		).sample
       snake_case       : Tuple    =       torch.clamp(UpperCamelCase__		,      -1.0		,      1.0		)
       snake_case       : Union[str, Any]    =       image / 2 + 0.5
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		).numpy()

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





