



"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			str							=		(DDPMScheduler,)

 def 						_snake_case					(     self		:       Optional[Any]  ,     **lowercase_		:       int				):
     snake_case_ :     List[str]				=	{
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**lowercase_				)
     return config

 def 						_snake_case					(     self		:       Optional[Any]				):
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,     [0.0_02, 0.02, 0.2, 2]				):
         self.check_over_configs(beta_start=lowercase_  ,     beta_end=lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_				)

 def 						_snake_case					(     self		:       List[Any]				):
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=lowercase_				)

 def 						_snake_case					(     self		:       int				):
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=lowercase_				)

 def 						_snake_case					(     self		:       Optional[int]				):
     self.check_over_configs(thresholding=lowercase_				)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=lowercase_  ,     prediction_type=lowercase_  ,     sample_max_value=lowercase_  ,     )

 def 						_snake_case					(     self		:       Tuple				):
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     for t in [0, 500, 999]:
         self.check_over_forward(time_step=lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Optional[int]				=	self.scheduler_classes[0]
     snake_case_ :     str				=	self.get_scheduler_config()
     snake_case_ :     Dict				=	scheduler_class(**lowercase_				)

     assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     List[str]				=	self.scheduler_classes[0]
     snake_case_ :     str				=	self.get_scheduler_config()
     snake_case_ :     Tuple				=	scheduler_class(**lowercase_				)

     snake_case_ :     int				=	len(lowercase_				)

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

     for t in reversed(range(lowercase_				)				):
         # 1. predict noise residual
         snake_case_ :     int				=	model(lowercase_  ,     lowercase_				)

         # 2. predict previous mean of sample x_t-1
         snake_case_ :     List[str]				=	scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_  ,     generator=lowercase_				).prev_sample

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

     snake_case_ :     Tuple				=	torch.sum(torch.abs(lowercase_				)				)
     snake_case_ :     Optional[int]				=	torch.mean(torch.abs(lowercase_				)				)

     assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
     assert abs(result_mean.item() - 0.33_72				) < 1E-3

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Dict				=	self.scheduler_classes[0]
     snake_case_ :     Optional[Any]				=	self.get_scheduler_config(prediction_type='''v_prediction'''				)
     snake_case_ :     Dict				=	scheduler_class(**lowercase_				)

     snake_case_ :     str				=	len(lowercase_				)

     snake_case_ :     Dict				=	self.dummy_model()
     snake_case_ :     str				=	self.dummy_sample_deter
     snake_case_ :     List[Any]				=	torch.manual_seed(0				)

     for t in reversed(range(lowercase_				)				):
         # 1. predict noise residual
         snake_case_ :     Optional[Any]				=	model(lowercase_  ,     lowercase_				)

         # 2. predict previous mean of sample x_t-1
         snake_case_ :     int				=	scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_  ,     generator=lowercase_				).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         snake_case_ :     List[str]				=	pred_prev_sample

     snake_case_ :     Optional[Any]				=	torch.sum(torch.abs(lowercase_				)				)
     snake_case_ :     Union[str, Any]				=	torch.mean(torch.abs(lowercase_				)				)

     assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
     assert abs(result_mean.item() - 0.26_31				) < 1E-3

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     str				=	self.scheduler_classes[0]
     snake_case_ :     List[Any]				=	self.get_scheduler_config()
     snake_case_ :     Optional[Any]				=	scheduler_class(**lowercase_				)

     snake_case_ :     List[str]				=	[100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=lowercase_				)

     snake_case_ :     List[Any]				=	scheduler.timesteps

     for i, timestep in enumerate(lowercase_				):
         if i == len(lowercase_				) - 1:
             snake_case_ :     List[str]				=	-1
         else:
             snake_case_ :     List[str]				=	timesteps[i + 1]

         snake_case_ :     List[Any]				=	scheduler.previous_timestep(lowercase_				)
         snake_case_ :     Union[str, Any]				=	prev_t.item()

         self.assertEqual(lowercase_  ,     lowercase_				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     List[str]				=	self.scheduler_classes[0]
     snake_case_ :     int				=	self.get_scheduler_config()
     snake_case_ :     Dict				=	scheduler_class(**lowercase_				)

     snake_case_ :     Dict				=	[100, 87, 50, 51, 0]

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

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Tuple				=	self.scheduler_classes[0]
     snake_case_ :     int				=	self.get_scheduler_config()
     snake_case_ :     Optional[int]				=	scheduler_class(**lowercase_				)

     snake_case_ :     Optional[Any]				=	[100, 87, 50, 1, 0]
     snake_case_ :     Any				=	len(lowercase_				)

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






 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Dict				=	self.scheduler_classes[0]
     snake_case_ :     Optional[int]				=	self.get_scheduler_config()
     snake_case_ :     Optional[int]				=	scheduler_class(**lowercase_				)

     snake_case_ :     Tuple				=	[scheduler.config.num_train_timesteps]

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










"""simple docstring"""

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Any  ,     lowercase_		:       TransformeraDModel  ,     lowercase_		:       AutoencoderKL  ,     lowercase_		:       KarrasDiffusionSchedulers  ,     lowercase_		:       Optional[Dict[int, str]] = None  ,     ):
     super().__init__()
     self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_				)

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     for l in label:
         if l not in self.labels:
             raise ValueError(
                 f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."				)

     return [self.labels[l] for l in label]






 @torch.no_grad()
 def __call__(     self		:       Optional[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

     snake_case_ :     str				=	randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)  ,     generator=lowercase_  ,     device=self.device  ,     dtype=self.transformer.dtype  ,     )
     snake_case_ :     Optional[Any]				=	torch.cat([latents] * 2				) if guidance_scale > 1 else latents

     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_  ,     device=self.device				).reshape(-1				)
     snake_case_ :     Dict				=	torch.tensor([1000] * batch_size  ,     device=self.device				)
     snake_case_ :     Tuple				=	torch.cat([class_labels, class_null]  ,     0				) if guidance_scale > 1 else class_labels

     # set step values
     self.scheduler.set_timesteps(lowercase_				)

     for t in self.progress_bar(self.scheduler.timesteps				):
         if guidance_scale > 1:
             snake_case_ :     List[Any]				=	latent_model_input[: len(lowercase_				) // 2]
             snake_case_ :     Union[str, Any]				=	torch.cat([half, half]  ,     dim=0				)
         snake_case_ :     Optional[Any]				=	self.scheduler.scale_model_input(lowercase_  ,     lowercase_				)

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

     if guidance_scale > 1:
         snake_case_,	snake_case_ :     Optional[Any]				=	latent_model_input.chunk(2  ,     dim=0				)
     else:
         snake_case_ :     Dict				=	latent_model_input

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_ :     Union[str, Any]				=	samples.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()

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

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





