




"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class lowerCAmelCase__			(  lowercase__		):






   '''simple docstring'''



   lowerCamelCase__   =    (CMStochasticIterativeScheduler,)
   lowerCamelCase__   =    10
   def       A_					(						self						,   **lowercase						):
       _lowerCamelCase  :    Optional[int]					=					{
           '''num_train_timesteps''': 201,
           '''sigma_min''': 0.0_02,
           '''sigma_max''': 80.0,
       }

       config.update(**UpperCAmelCase__						)
       return config
   def       A_					(						self						):
       _lowerCamelCase  :    str					=					10

       _lowerCamelCase  :    Dict					=					self.get_scheduler_config()
       _lowerCamelCase  :    Union[str, Any]					=					self.scheduler_classes[0](**UpperCAmelCase__						)

       scheduler.set_timesteps(UpperCAmelCase__						)

       _lowerCamelCase  :    Optional[int]					=					scheduler.timesteps[0]
       _lowerCamelCase  :    List[Any]					=					scheduler.timesteps[1]

       _lowerCamelCase  :    List[Any]					=					self.dummy_sample
       _lowerCamelCase  :    Tuple					=					0.1 * sample

       _lowerCamelCase  :    Union[str, Any]					=					scheduler.step(UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__						).prev_sample
       _lowerCamelCase  :    Optional[int]					=					scheduler.step(UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__						).prev_sample

       self.assertEqual(output_a.shape						,   sample.shape						)
       self.assertEqual(output_a.shape						,   output_a.shape						)
   def       A_					(						self						):
       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCAmelCase__						)
   def       A_					(						self						):
       for clip_denoised in [True, False]:
           self.check_over_configs(clip_denoised=UpperCAmelCase__						)
   def       A_					(						self						):
       _lowerCamelCase  :    Any					=					self.scheduler_classes[0]
       _lowerCamelCase  :    Dict					=					self.get_scheduler_config()
       _lowerCamelCase  :    Optional[Any]					=					scheduler_class(**UpperCAmelCase__						)

       _lowerCamelCase  :    Optional[int]					=					1
       scheduler.set_timesteps(UpperCAmelCase__						)
       _lowerCamelCase  :    Optional[int]					=					scheduler.timesteps

       _lowerCamelCase  :    Optional[int]					=					torch.manual_seed(0						)

       _lowerCamelCase  :    Union[str, Any]					=					self.dummy_model()
       _lowerCamelCase  :    Any					=					self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(UpperCAmelCase__						):
           # 1. scale model input
           _lowerCamelCase  :    Tuple					=					scheduler.scale_model_input(UpperCAmelCase__						,   UpperCAmelCase__						)

           # 2. predict noise residual
           _lowerCamelCase  :    Optional[Any]					=					model(UpperCAmelCase__						,   UpperCAmelCase__						)

           # 3. predict previous sample x_t-1
           _lowerCamelCase  :    Tuple					=					scheduler.step(UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__						,   generator=UpperCAmelCase__						).prev_sample

           _lowerCamelCase  :    List[Any]					=					pred_prev_sample

       _lowerCamelCase  :    int					=					torch.sum(torch.abs(UpperCAmelCase__						)						)
       _lowerCamelCase  :    List[str]					=					torch.mean(torch.abs(UpperCAmelCase__						)						)

       assert abs(result_sum.item() - 192.7614						) < 1E-2
       assert abs(result_mean.item() - 0.25_10						) < 1E-3
   def       A_					(						self						):
       _lowerCamelCase  :    str					=					self.scheduler_classes[0]
       _lowerCamelCase  :    Optional[Any]					=					self.get_scheduler_config()
       _lowerCamelCase  :    str					=					scheduler_class(**UpperCAmelCase__						)

       _lowerCamelCase  :    Union[str, Any]					=					[106, 0]
       scheduler.set_timesteps(timesteps=UpperCAmelCase__						)
       _lowerCamelCase  :    Any					=					scheduler.timesteps

       _lowerCamelCase  :    Dict					=					torch.manual_seed(0						)

       _lowerCamelCase  :    Union[str, Any]					=					self.dummy_model()
       _lowerCamelCase  :    Tuple					=					self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
           # 1. scale model input
           _lowerCamelCase  :    Optional[Any]					=					scheduler.scale_model_input(UpperCAmelCase__						,   UpperCAmelCase__						)

           # 2. predict noise residual
           _lowerCamelCase  :    str					=					model(UpperCAmelCase__						,   UpperCAmelCase__						)

           # 3. predict previous sample x_t-1
           _lowerCamelCase  :    Tuple					=					scheduler.step(UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__						,   generator=UpperCAmelCase__						).prev_sample

           _lowerCamelCase  :    Dict					=					pred_prev_sample

       _lowerCamelCase  :    str					=					torch.sum(torch.abs(UpperCAmelCase__						)						)
       _lowerCamelCase  :    int					=					torch.mean(torch.abs(UpperCAmelCase__						)						)

       assert abs(result_sum.item() - 347.6357						) < 1E-2
       assert abs(result_mean.item() - 0.45_27						) < 1E-3
   def       A_					(						self						):
       _lowerCamelCase  :    List[str]					=					self.scheduler_classes[0]
       _lowerCamelCase  :    Dict					=					self.get_scheduler_config()
       _lowerCamelCase  :    str					=					scheduler_class(**UpperCAmelCase__						)

       _lowerCamelCase  :    Any					=					[39, 30, 12, 15, 0]

       with self.assertRaises(UpperCAmelCase__						,   msg='`timesteps` must be in descending order.'						):
           scheduler.set_timesteps(timesteps=UpperCAmelCase__						)
   def       A_					(						self						):
       _lowerCamelCase  :    List[Any]					=					self.scheduler_classes[0]
       _lowerCamelCase  :    Dict					=					self.get_scheduler_config()
       _lowerCamelCase  :    Dict					=					scheduler_class(**UpperCAmelCase__						)

       _lowerCamelCase  :    Optional[Any]					=					[39, 30, 12, 1, 0]
       _lowerCamelCase  :    str					=					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       A_					(						self						):
       _lowerCamelCase  :    Union[str, Any]					=					self.scheduler_classes[0]
       _lowerCamelCase  :    Union[str, Any]					=					self.get_scheduler_config()
       _lowerCamelCase  :    Optional[Any]					=					scheduler_class(**UpperCAmelCase__						)

       _lowerCamelCase  :    Optional[Any]					=					[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 lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    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