


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 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[str]   			=       image.size
       _lowerCAmelCase    :							Optional[int]   			=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
       _lowerCAmelCase    :							Optional[Any]   			=       image.resize((w, h)   ,						resample=PIL_INTERPOLATION['lanczos']					)
       _lowerCAmelCase    :							Dict   			=       np.array(_A					).astype(np.floataa					) / 255.0
       _lowerCAmelCase    :							List[str]   			=       image[None].transpose(0   ,						3   ,						1   ,						2					)
       _lowerCAmelCase    :							int   			=       torch.from_numpy(_A					)
       return 2.0 * image - 1.0
class 	UpperCamelCase__					( __SCREAMING_SNAKE_CASE	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	):

            '''simple docstring'''

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







     @torch.no_grad()
     def __call__( self      ,	snake_case__ = None      ,	snake_case__ = 1      ,	snake_case__ = 100      ,	snake_case__ = 0.0      ,	snake_case__ = None      ,	snake_case__ = "pil"      ,	snake_case__ = True      ,	):

            '''simple docstring'''

            if isinstance(_a      ,	PIL.Image.Image   ):
                   _lowerCAmelCase    :							Any   			=       1
            elif isinstance(_a      ,	torch.Tensor   ):
                   _lowerCAmelCase    :							Optional[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   ):
                   _lowerCAmelCase    :							str   			=       preprocess(_a   )

            _lowerCAmelCase    :							Any   			=       image.shape[-2:]

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

            _lowerCAmelCase    :							int   			=       randn_tensor(_a      ,	generator=_a      ,	device=self.device      ,	dtype=_a   )

            _lowerCAmelCase    :							List[Any]   			=       image.to(device=self.device      ,	dtype=_a   )

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(_a      ,	device=self.device   )
            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler.timesteps

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

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

            # decode the image latents with the VQVAE
            _lowerCAmelCase    :							Any   			=       self.vqvae.decode(_a   ).sample
            _lowerCAmelCase    :							Tuple   			=       torch.clamp(_a      ,	-1.0      ,	1.0   )
            _lowerCAmelCase    :							int   			=       image / 2 + 0.5
            _lowerCAmelCase    :							Any   			=       image.cpu().permute(0      ,	2      ,	3      ,	1   ).numpy()

            if output_type == "pil":
                   _lowerCAmelCase    :							List[Any]   			=       self.numpy_to_pil(_a   )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=_a   )





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''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=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0979   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							str   			=       self.dummy_model()
            _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

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

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Dict   			=       pred_prev_sample

            _lowerCAmelCase    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

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

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

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

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Tuple   			=       pred_prev_sample

            _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.timesteps

            for i, timestep in enumerate(snake_case__   ):
                   if i == len(snake_case__   ) - 1:
                          _lowerCAmelCase    :							str   			=       -1
                   else:
                          _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

                   _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
                   _lowerCAmelCase    :							int   			=       prev_t.item()

                   self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
            _lowerCAmelCase    :							int   			=       len(snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Any   			=       [scheduler.config.num_train_timesteps]

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

