"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 	UpperCAmelCase_      (       A_):
      lowerCamelCase__						:       Any					    =    (DDPMScheduler,)


      def 						_UpperCAmelCase			(  self						,     **a				)			->   Optional[int]:
             lowercase__     :						Optional[int]         =      {
                 """num_train_timesteps""": 1_0_0_0,
                 """beta_start""": 0.0_001,
                 """beta_end""": 0.02,
                 """beta_schedule""": """linear""",
                 """variance_type""": """fixed_small""",
                 """clip_sample""": True,
             }

             config.update(**a				)
             return config


      def 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=a				)


      def 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,     [0.002, 0.02, 0.2, 2]				):
                    self.check_over_configs(beta_start=a						,     beta_end=a				)


      def 						_UpperCAmelCase			(  self				)			->   List[Any]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=a				)


      def 						_UpperCAmelCase			(  self				)			->   str:
             for variance in ["fixed_small", "fixed_large", "other"]:
                    self.check_over_configs(variance_type=a				)


      def 						_UpperCAmelCase			(  self				)			->   Union[str, Any]:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=a				)


      def 						_UpperCAmelCase			(  self				)			->   Optional[Any]:
             self.check_over_configs(thresholding=a				)
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=a						,     prediction_type=a						,     sample_max_value=a						,     )


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(prediction_type=a				)


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             for t in [0, 5_0_0, 9_9_9]:
                    self.check_over_forward(time_step=a				)


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             lowercase__     :						Union[str, Any]         =      self.scheduler_classes[0]
             lowercase__     :						Dict         =      self.get_scheduler_config()
             lowercase__     :						Union[str, Any]         =      scheduler_class(**a				)

             assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				) - 0.00_979				)				) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				) - 0.02				)				) < 1e-5


      def 						_UpperCAmelCase			(  self				)			->   str:
             lowercase__     :						List[Any]         =      self.scheduler_classes[0]
             lowercase__     :						List[Any]         =      self.get_scheduler_config()
             lowercase__     :						List[str]         =      scheduler_class(**a				)

             lowercase__     :						Optional[int]         =      len(a				)

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

             for t in reversed(range(a				)				):
                    # 1. predict noise residual
                    lowercase__     :						Tuple         =      model(a						,     a				)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__     :						Tuple         =      scheduler.step(a						,     a						,     a						,     generator=a				).prev_sample

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

             lowercase__     :						Dict         =      torch.sum(torch.abs(a				)				)
             lowercase__     :						Dict         =      torch.mean(torch.abs(a				)				)

             assert abs(result_sum.item() - 2_5_8.9_6_0_6				) < 1e-2
             assert abs(result_mean.item() - 0.3_372				) < 1e-3


      def 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             lowercase__     :						Optional[Any]         =      self.scheduler_classes[0]
             lowercase__     :						Dict         =      self.get_scheduler_config(prediction_type='v_prediction'				)
             lowercase__     :						Tuple         =      scheduler_class(**a				)

             lowercase__     :						List[Any]         =      len(a				)

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

             for t in reversed(range(a				)				):
                    # 1. predict noise residual
                    lowercase__     :						Tuple         =      model(a						,     a				)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__     :						Union[str, Any]         =      scheduler.step(a						,     a						,     a						,     generator=a				).prev_sample

                    # if t > 0:
                    #     noise = self.dummy_sample_deter
                    #     variance = scheduler.get_variance(t) ** (0.5) * noise
                    #
                    # sample = pred_prev_sample + variance
                    lowercase__     :						Optional[Any]         =      pred_prev_sample

             lowercase__     :						Union[str, Any]         =      torch.sum(torch.abs(a				)				)
             lowercase__     :						int         =      torch.mean(torch.abs(a				)				)

             assert abs(result_sum.item() - 2_0_2.0_2_9_6				) < 1e-2
             assert abs(result_mean.item() - 0.2_631				) < 1e-3


      def 						_UpperCAmelCase			(  self				)			->   Dict:
             lowercase__     :						Any         =      self.scheduler_classes[0]
             lowercase__     :						Union[str, Any]         =      self.get_scheduler_config()
             lowercase__     :						Union[str, Any]         =      scheduler_class(**a				)

             lowercase__     :						int         =      [1_0_0, 8_7, 5_0, 1, 0]

             scheduler.set_timesteps(timesteps=a				)

             lowercase__     :						Optional[Any]         =      scheduler.timesteps

             for i, timestep in enumerate(a				):
                    if i == len(a				) - 1:
                           lowercase__     :						Dict         =      -1
                    else:
                           lowercase__     :						int         =      timesteps[i + 1]

                    lowercase__     :						Union[str, Any]         =      scheduler.previous_timestep(a				)
                    lowercase__     :						Optional[Any]         =      prev_t.item()

                    self.assertEqual(a						,     a				)


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             lowercase__     :						Dict         =      self.scheduler_classes[0]
             lowercase__     :						List[str]         =      self.get_scheduler_config()
             lowercase__     :						Any         =      scheduler_class(**a				)

             lowercase__     :						int         =      [1_0_0, 8_7, 5_0, 5_1, 0]

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


      def 						_UpperCAmelCase			(  self				)			->   str:
             lowercase__     :						str         =      self.scheduler_classes[0]
             lowercase__     :						Dict         =      self.get_scheduler_config()
             lowercase__     :						Any         =      scheduler_class(**a				)

             lowercase__     :						Any         =      [1_0_0, 8_7, 5_0, 1, 0]
             lowercase__     :						List[str]         =      len(a				)

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


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             lowercase__     :						Optional[Any]         =      self.scheduler_classes[0]
             lowercase__     :						Optional[Any]         =      self.get_scheduler_config()
             lowercase__     :						Dict         =      scheduler_class(**a				)

             lowercase__     :						int         =      [scheduler.config.num_train_timesteps]

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





"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch

def 				_snake_case      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   for param in module.parameters():
      _lowerCamelCase	:					Optional[Any]   =		False

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      _lowerCamelCase	:					Any   =		"""mps"""
   if device == "mps":
      print(
          """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
          """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
          """ with generations."""   )
   return device

def 				_snake_case      (   __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		plt.imshow(__snake_case   )
   fig.axes.get_xaxis().set_visible(__snake_case   )
   fig.axes.get_yaxis().set_visible(__snake_case   )
   plt.show()





def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		datetime.now()
   _lowerCamelCase	:					Optional[Any]   =		current_time.strftime("""%H:%M:%S"""   )
   return timestamp

