


UpperCamelCase__				       =			'\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n'

UpperCamelCase__				       =			[{'type': 'code', 'content': INSTALL_CONTENT}]



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''
     UpperCAmelCase_         =  (DDIMScheduler,)
     UpperCAmelCase_         =  (('''eta''', 0.0), ('''num_inference_steps''', 5_0))




     def   lowerCAmelCase_   ( self       :       List[Any]	,      **UpperCamelCase       :       str		):





            """simple docstring"""



            _lowercase					:				Any           =			{
                '''num_train_timesteps''': 10_00,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''clip_sample''': True,
            }

            config.update(**UpperCamelCase		)
            return config




     def   lowerCAmelCase_   ( self       :       str	,      **UpperCamelCase       :       int		):





            """simple docstring"""



            _lowercase					:				str           =			self.scheduler_classes[0]
            _lowercase					:				Dict           =			self.get_scheduler_config(**UpperCamelCase		)
            _lowercase					:				List[str]           =			scheduler_class(**UpperCamelCase		)

            _lowercase       ,	_lowercase					:				int           =			10, 0.0

            _lowercase					:				List[str]           =			self.dummy_model()
            _lowercase					:				int           =			self.dummy_sample_deter

            scheduler.set_timesteps(UpperCamelCase		)

            for t in scheduler.timesteps:
                   _lowercase					:				Union[str, Any]           =			model(UpperCamelCase	,      UpperCamelCase		)
                   _lowercase					:				List[Any]           =			scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample

            return sample




     def   lowerCAmelCase_   ( self       :       Dict		):





            """simple docstring"""



            for timesteps in [1_00, 5_00, 10_00]:
                   self.check_over_configs(num_train_timesteps=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       List[Any]		):





            """simple docstring"""



            for steps_offset in [0, 1]:
                   self.check_over_configs(steps_offset=UpperCamelCase		)

            _lowercase					:				List[Any]           =			self.scheduler_classes[0]
            _lowercase					:				Tuple           =			self.get_scheduler_config(steps_offset=1		)
            _lowercase					:				Union[str, Any]           =			scheduler_class(**UpperCamelCase		)
            scheduler.set_timesteps(5		)
            assert torch.equal(scheduler.timesteps	,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]		)		)




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,      [0.002, 0.02, 0.2, 2]		):
                   self.check_over_configs(beta_start=UpperCamelCase	,      beta_end=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            for schedule in ["linear", "squaredcos_cap_v2"]:
                   self.check_over_configs(beta_schedule=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Optional[int]		):





            """simple docstring"""



            for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(prediction_type=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            for timestep_spacing in ["trailing", "leading"]:
                   self.check_over_configs(timestep_spacing=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            for rescale_betas_zero_snr in [True, False]:
                   self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



            self.check_over_configs(thresholding=UpperCamelCase		)
            for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "v_prediction"]:
                          self.check_over_configs(
                              thresholding=UpperCamelCase	,      prediction_type=UpperCamelCase	,      sample_max_value=UpperCamelCase	,      )




     def   lowerCAmelCase_   ( self       :       str		):





            """simple docstring"""



            for t in [1, 10, 49]:
                   self.check_over_forward(time_step=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Optional[int]		):





            """simple docstring"""



            for t, num_inference_steps in zip([1, 10, 50]	,      [10, 50, 5_00]		):
                   self.check_over_forward(time_step=UpperCamelCase	,      num_inference_steps=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



            for t, eta in zip([1, 10, 49]	,      [0.0, 0.5, 1.0]		):
                   self.check_over_forward(time_step=UpperCamelCase	,      eta=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				int           =			self.scheduler_classes[0]
            _lowercase					:				str           =			self.get_scheduler_config()
            _lowercase					:				int           =			scheduler_class(**UpperCamelCase		)

            assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_20	,      4_00		) - 0.14771		)		) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_80	,      9_60		) - 0.32460		)		) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_87	,      4_86		) - 0.00979		)		) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_99	,      9_98		) - 0.02		)		) < 1E-5




     def   lowerCAmelCase_   ( self       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				Any           =			self.full_loop()

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

            assert abs(result_sum.item() - 172.0067		) < 1E-2
            assert abs(result_mean.item() - 0.223967		) < 1E-3




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





            """simple docstring"""



            _lowercase					:				List[Any]           =			self.full_loop(prediction_type='''v_prediction'''		)

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

            assert abs(result_sum.item() - 52.5302		) < 1E-2
            assert abs(result_mean.item() - 0.0684		) < 1E-3




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            _lowercase					:				Tuple           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
            _lowercase					:				List[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
            _lowercase					:				Any           =			torch.mean(torch.abs(UpperCamelCase		)		)

            assert abs(result_sum.item() - 149.8295		) < 1E-2
            assert abs(result_mean.item() - 0.1951		) < 1E-3




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            _lowercase					:				int           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
            _lowercase					:				Optional[int]           =			torch.sum(torch.abs(UpperCamelCase		)		)
            _lowercase					:				Dict           =			torch.mean(torch.abs(UpperCamelCase		)		)

            assert abs(result_sum.item() - 149.0784		) < 1E-2
            assert abs(result_mean.item() - 0.1941		) < 1E-3