



"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class        __snake_case       (	_lowercase):
  snake_case__				:   Optional[Any]  			=						(CMStochasticIterativeScheduler,)
  snake_case__				:   Tuple  			=						1_0




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= {
             '''num_train_timesteps''': 2_0_1,
             '''sigma_min''': 0.0_02,
             '''sigma_max''': 80.0,
         }

         config.update(**__lowerCAmelCase		)
         return config




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= 1_0

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

         scheduler.set_timesteps(__lowerCAmelCase		)

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

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

         _lowerCamelCase						:				str    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		).prev_sample
         _lowerCamelCase						:				Tuple    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		).prev_sample

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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         for clip_denoised in [True, False]:
                self.check_over_configs(clip_denoised=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Optional[Any]    			= self.get_scheduler_config()
         _lowerCamelCase						:				int    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				str    			= 1
         scheduler.set_timesteps(__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= scheduler.timesteps

         _lowerCamelCase						:				Tuple    			= torch.manual_seed(0		)

         _lowerCamelCase						:				int    			= self.dummy_model()
         _lowerCamelCase						:				Dict    			= self.dummy_sample_deter * scheduler.init_noise_sigma

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

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

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

                _lowerCamelCase						:				List[str]    			= pred_prev_sample

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

         assert abs(result_sum.item() - 1_92.76_14		) < 1E-2
         assert abs(result_mean.item() - 0.25_10		) < 1E-3




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				List[str]    			= self.get_scheduler_config()
         _lowerCamelCase						:				str    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				int    			= [1_0_6, 0]
         scheduler.set_timesteps(timesteps=__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= scheduler.timesteps

         _lowerCamelCase						:				str    			= torch.manual_seed(0		)

         _lowerCamelCase						:				Optional[int]    			= self.dummy_model()
         _lowerCamelCase						:				List[str]    			= self.dummy_sample_deter * scheduler.init_noise_sigma

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

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

                # 3. predict previous sample x_t-1
                _lowerCamelCase						:				Union[str, Any]    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:				List[str]    			= pred_prev_sample

         _lowerCamelCase						:				Optional[int]    			= torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				Tuple    			= torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 3_47.63_57		) < 1E-2
         assert abs(result_mean.item() - 0.45_27		) < 1E-3




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				str    			= self.get_scheduler_config()
         _lowerCamelCase						:				List[Any]    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Union[str, Any]    			= [3_9, 3_0, 1_2, 1_5, 0]

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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Any    			= self.get_scheduler_config()
         _lowerCamelCase						:				List[Any]    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				List[Any]    			= [3_9, 3_0, 1_2, 1, 0]
         _lowerCamelCase						:				Optional[Any]    			= len(__lowerCAmelCase		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Dict    			= self.get_scheduler_config()
         _lowerCamelCase						:				Dict    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Dict    			= [scheduler.config.num_train_timesteps]

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






'''simple docstring'''



import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



