

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  List[str]		     =							(CMStochasticIterativeScheduler,)
			SCREAMING_SNAKE_CASE	:  int		     =							10







			def 							snake_case__(	self					:							Any				,  **_UpperCamelCase					:							str				) ->List[str]:
									snake_case_  =      {
									    '''num_train_timesteps''': 2_0_1,
									    '''sigma_min''': 0.002,
									    '''sigma_max''': 80.0,
									}

									config.update(**_UpperCamelCase				)
									return config







			def 							snake_case__(	self					:							Optional[Any]				) ->List[str]:
									snake_case_  =      1_0

									snake_case_  =      self.get_scheduler_config()
									snake_case_  =      self.scheduler_classes[0](**_UpperCamelCase				)

									scheduler.set_timesteps(_UpperCamelCase				)

									snake_case_  =      scheduler.timesteps[0]
									snake_case_  =      scheduler.timesteps[1]

									snake_case_  =      self.dummy_sample
									snake_case_  =      0.1 * sample

									snake_case_  =      scheduler.step(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				).prev_sample
									snake_case_  =      scheduler.step(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				).prev_sample

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







			def 							snake_case__(	self					:							Optional[Any]				) ->int:
									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[int]				) ->List[str]:
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=_UpperCamelCase				)







			def 							snake_case__(	self					:							int				) ->Optional[int]:
									snake_case_  =      self.scheduler_classes[0]
									snake_case_  =      self.get_scheduler_config()
									snake_case_  =      scheduler_class(**_UpperCamelCase				)

									snake_case_  =      1
									scheduler.set_timesteps(_UpperCamelCase				)
									snake_case_  =      scheduler.timesteps

									snake_case_  =      torch.manual_seed(0				)

									snake_case_  =      self.dummy_model()
									snake_case_  =      self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(_UpperCamelCase				):
															# 1. scale model input
															snake_case_  =      scheduler.scale_model_input(_UpperCamelCase				,  _UpperCamelCase				)

															# 2. predict noise residual
															snake_case_  =      model(_UpperCamelCase				,  _UpperCamelCase				)

															# 3. predict previous sample x_t-1
															snake_case_  =      scheduler.step(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  generator=_UpperCamelCase				).prev_sample

															snake_case_  =      pred_prev_sample

									snake_case_  =      torch.sum(torch.abs(_UpperCamelCase				)				)
									snake_case_  =      torch.mean(torch.abs(_UpperCamelCase				)				)

									assert abs(result_sum.item() - 192.7614				) < 1e-2
									assert abs(result_mean.item() - 0.2510				) < 1e-3







			def 							snake_case__(	self					:							List[str]				) ->Optional[int]:
									snake_case_  =      self.scheduler_classes[0]
									snake_case_  =      self.get_scheduler_config()
									snake_case_  =      scheduler_class(**_UpperCamelCase				)

									snake_case_  =      [1_0_6, 0]
									scheduler.set_timesteps(timesteps=_UpperCamelCase				)
									snake_case_  =      scheduler.timesteps

									snake_case_  =      torch.manual_seed(0				)

									snake_case_  =      self.dummy_model()
									snake_case_  =      self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															snake_case_  =      scheduler.scale_model_input(_UpperCamelCase				,  _UpperCamelCase				)

															# 2. predict noise residual
															snake_case_  =      model(_UpperCamelCase				,  _UpperCamelCase				)

															# 3. predict previous sample x_t-1
															snake_case_  =      scheduler.step(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  generator=_UpperCamelCase				).prev_sample

															snake_case_  =      pred_prev_sample

									snake_case_  =      torch.sum(torch.abs(_UpperCamelCase				)				)
									snake_case_  =      torch.mean(torch.abs(_UpperCamelCase				)				)

									assert abs(result_sum.item() - 347.6357				) < 1e-2
									assert abs(result_mean.item() - 0.4527				) < 1e-3







			def 							snake_case__(	self					:							Dict				) ->Optional[Any]:
									snake_case_  =      self.scheduler_classes[0]
									snake_case_  =      self.get_scheduler_config()
									snake_case_  =      scheduler_class(**_UpperCamelCase				)

									snake_case_  =      [3_9, 3_0, 1_2, 1_5, 0]

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







			def 							snake_case__(	self					:							List[str]				) ->Dict:
									snake_case_  =      self.scheduler_classes[0]
									snake_case_  =      self.get_scheduler_config()
									snake_case_  =      scheduler_class(**_UpperCamelCase				)

									snake_case_  =      [3_9, 3_0, 1_2, 1, 0]
									snake_case_  =      len(_UpperCamelCase				)

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







			def 							snake_case__(	self					:							int				) ->Dict:
									snake_case_  =      self.scheduler_classes[0]
									snake_case_  =      self.get_scheduler_config()
									snake_case_  =      scheduler_class(**_UpperCamelCase				)

									snake_case_  =      [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"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_text_to_video_synth import TextToVideoSDPipeline
					from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
					from .pipeline_text_to_video_zero import TextToVideoZeroPipeline




