





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	str								=	(UniPCMultistepScheduler,)
						_UpperCamelCase  :	int								=	(("num_inference_steps", 25),)







						def 		__UpperCAmelCase       (    self			:		Dict ,				**lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   {
										    'num_train_timesteps': 10_00,
										    'beta_start': 0.0001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'solver_order': 2,
										    'solver_type': 'bh2',
										}

										config.update(**lowerCamelCase_					)
										return config







						def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		Dict=0 ,				**lowerCamelCase_			:		Dict					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   dict(self.forward_default_kwargs					)
										_snake_case :			Union[str, Any]  						=   kwargs.pop('num_inference_steps' ,				lowerCamelCase_					)
										_snake_case :			str  						=   self.dummy_sample
										_snake_case :			Optional[Any]  						=   0.1 * sample
										_snake_case :			Any  						=   [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_snake_case :			str  						=   self.get_scheduler_config(**lowerCamelCase_					)
														_snake_case :			Dict  						=   scheduler_class(**lowerCamelCase_					)
														scheduler.set_timesteps(lowerCamelCase_					)
														# copy over dummy past residuals
														_snake_case :			Any  						=   dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowerCamelCase_					)
																		_snake_case :			Optional[int]  						=   scheduler_class.from_pretrained(lowerCamelCase_					)
																		new_scheduler.set_timesteps(lowerCamelCase_					)
																		# copy over dummy past residuals
																		_snake_case :			Union[str, Any]  						=   dummy_past_residuals[: new_scheduler.config.solver_order]

														_snake_case						,			_snake_case :			Dict  						=   sample, sample
														for t in range(lowerCamelCase_ ,				time_step + scheduler.config.solver_order + 1					):
																		_snake_case :			Dict  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					).prev_sample
																		_snake_case :			Optional[Any]  						=   new_scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					).prev_sample

																		assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		str=0 ,				**lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   dict(self.forward_default_kwargs					)
										_snake_case :			List[str]  						=   kwargs.pop('num_inference_steps' ,				lowerCamelCase_					)
										_snake_case :			Dict  						=   self.dummy_sample
										_snake_case :			str  						=   0.1 * sample
										_snake_case :			Union[str, Any]  						=   [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_snake_case :			Union[str, Any]  						=   self.get_scheduler_config()
														_snake_case :			Any  						=   scheduler_class(**lowerCamelCase_					)
														scheduler.set_timesteps(lowerCamelCase_					)

														# copy over dummy past residuals (must be after setting timesteps)
														_snake_case :			Union[str, Any]  						=   dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowerCamelCase_					)
																		_snake_case :			List[Any]  						=   scheduler_class.from_pretrained(lowerCamelCase_					)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(lowerCamelCase_					)

																		# copy over dummy past residual (must be after setting timesteps)
																		_snake_case :			Tuple  						=   dummy_past_residuals[: new_scheduler.config.solver_order]

														_snake_case :			Optional[Any]  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					).prev_sample
														_snake_case :			str  						=   new_scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					).prev_sample

														assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		List[Any]=None ,				**lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										if scheduler is None:
														_snake_case :			List[Any]  						=   self.scheduler_classes[0]
														_snake_case :			str  						=   self.get_scheduler_config(**lowerCamelCase_					)
														_snake_case :			str  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.scheduler_classes[0]
										_snake_case :			int  						=   self.get_scheduler_config(**lowerCamelCase_					)
										_snake_case :			List[Any]  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			Any  						=   10
										_snake_case :			List[str]  						=   self.dummy_model()
										_snake_case :			str  						=   self.dummy_sample_deter
										scheduler.set_timesteps(lowerCamelCase_					)

										for i, t in enumerate(scheduler.timesteps					):
														_snake_case :			Tuple  						=   model(lowerCamelCase_ ,				lowerCamelCase_					)
														_snake_case :			str  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					).prev_sample

										return sample







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			int  						=   dict(self.forward_default_kwargs					)

										_snake_case :			Union[str, Any]  						=   kwargs.pop('num_inference_steps' ,				lowerCamelCase_					)

										for scheduler_class in self.scheduler_classes:
														_snake_case :			Optional[int]  						=   self.get_scheduler_config()
														_snake_case :			Optional[Any]  						=   scheduler_class(**lowerCamelCase_					)

														_snake_case :			List[Any]  						=   self.dummy_sample
														_snake_case :			Union[str, Any]  						=   0.1 * sample

														if num_inference_steps is not None and hasattr(lowerCamelCase_ ,				'set_timesteps'					):
																		scheduler.set_timesteps(lowerCamelCase_					)
														elif num_inference_steps is not None and not hasattr(lowerCamelCase_ ,				'set_timesteps'					):
																		_snake_case :			Dict  						=   num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_snake_case :			List[str]  						=   [residual + 0.2, residual + 0.15, residual + 0.10]
														_snake_case :			int  						=   dummy_past_residuals[: scheduler.config.solver_order]

														_snake_case :			List[str]  						=   scheduler.timesteps[5]
														_snake_case :			Tuple  						=   scheduler.timesteps[6]

														_snake_case :			Any  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					).prev_sample
														_snake_case :			Optional[Any]  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					).prev_sample

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







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			str  						=   UniPCMultistepScheduler(**self.get_scheduler_config()					)
										_snake_case :			Union[str, Any]  						=   self.full_loop(scheduler=lowerCamelCase_					)
										_snake_case :			List[Any]  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_mean.item() - 0.2464					) < 1e-3

										_snake_case :			Dict  						=   DPMSolverSinglestepScheduler.from_config(scheduler.config					)
										_snake_case :			Any  						=   DEISMultistepScheduler.from_config(scheduler.config					)
										_snake_case :			Any  						=   DPMSolverMultistepScheduler.from_config(scheduler.config					)
										_snake_case :			List[Any]  						=   UniPCMultistepScheduler.from_config(scheduler.config					)

										_snake_case :			Tuple  						=   self.full_loop(scheduler=lowerCamelCase_					)
										_snake_case :			str  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_mean.item() - 0.2464					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										for timesteps in [25, 50, 1_00, 9_99, 10_00]:
														self.check_over_configs(num_train_timesteps=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										self.check_over_configs(thresholding=lowerCamelCase_					)
										for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																						for prediction_type in ["epsilon", "sample"]:
																										self.check_over_configs(
																										    thresholding=lowerCamelCase_ ,				prediction_type=lowerCamelCase_ ,				sample_max_value=lowerCamelCase_ ,				solver_order=lowerCamelCase_ ,				solver_type=lowerCamelCase_ ,				)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										for solver_type in ["bh1", "bh2"]:
														for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=lowerCamelCase_ ,				solver_type=lowerCamelCase_ ,				prediction_type=lowerCamelCase_ ,				)
																						_snake_case :			int  						=   self.full_loop(
																						    solver_order=lowerCamelCase_ ,				solver_type=lowerCamelCase_ ,				prediction_type=lowerCamelCase_ ,				)
																						assert not torch.isnan(lowerCamelCase_					).any(), "Samples have nan numbers"







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										self.check_over_configs(lower_order_final=lowerCamelCase_					)
										self.check_over_configs(lower_order_final=lowerCamelCase_					)







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





										'''simple docstring'''







										for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
														self.check_over_forward(num_inference_steps=lowerCamelCase_ ,				time_step=0					)







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.full_loop()
										_snake_case :			Dict  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_mean.item() - 0.2464					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   self.full_loop(prediction_type='v_prediction'					)
										_snake_case :			str  						=   torch.mean(torch.abs(lowerCamelCase_					)					)

										assert abs(result_mean.item() - 0.1014					) < 1e-3







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			str  						=   self.scheduler_classes[0]
										_snake_case :			Any  						=   self.get_scheduler_config(thresholding=lowerCamelCase_ ,				dynamic_thresholding_ratio=0					)
										_snake_case :			Tuple  						=   scheduler_class(**lowerCamelCase_					)

										_snake_case :			Tuple  						=   10
										_snake_case :			str  						=   self.dummy_model()
										_snake_case :			str  						=   self.dummy_sample_deter.half()
										scheduler.set_timesteps(lowerCamelCase_					)

										for i, t in enumerate(scheduler.timesteps					):
														_snake_case :			Tuple  						=   model(lowerCamelCase_ ,				lowerCamelCase_					)
														_snake_case :			Any  						=   scheduler.step(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					).prev_sample

										assert sample.dtype == torch.floataa







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				**lowerCamelCase_			:		int					):





										'''simple docstring'''







										for scheduler_class in self.scheduler_classes:
														_snake_case :			Any  						=   self.get_scheduler_config(**lowerCamelCase_					)
														_snake_case :			int  						=   scheduler_class(**lowerCamelCase_					)

														scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
														assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps













lowercase_		:				Tuple				=						'''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_		:				Optional[int]				=						[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_		:				str				=						{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






