





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        __snake_case			(				_lowercase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	(DPMSolverMultistepScheduler,)
       lowerCAmelCase__ 						=	(("""num_inference_steps""", 25),)




       def 				UpperCAmelCase__					(    self					:       Optional[int]				,							**A					:       Optional[Any]						):
         __snake_case:		List[str]					    =		{
             """num_train_timesteps""": 1_000,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
             """prediction_type""": """epsilon""",
             """thresholding""": False,
             """sample_max_value""": 1.0,
             """algorithm_type""": """dpmsolver++""",
             """solver_type""": """midpoint""",
             """lower_order_final""": False,
             """lambda_min_clipped""": -float("""inf"""						),
             """variance_type""": None,
         }

         config.update(**UpperCamelCase__						)
         return config




       def 				UpperCAmelCase__					(    self					:       Optional[int]				,							A					:       Optional[Any]=0				,							**A					:       Optional[Any]						):
         __snake_case:		Any					    =		dict(self.forward_default_kwargs						)
         __snake_case:		Any					    =		kwargs.pop("""num_inference_steps"""				,							UpperCamelCase__						)
         __snake_case:		List[str]					    =		self.dummy_sample
         __snake_case:		Tuple					    =		0.1 * sample
         __snake_case:		str					    =		[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           __snake_case:		int					    =		self.get_scheduler_config(**UpperCamelCase__						)
           __snake_case:		str					    =		scheduler_class(**UpperCamelCase__						)
           scheduler.set_timesteps(UpperCamelCase__						)
           # copy over dummy past residuals
           __snake_case:		Optional[Any]					    =		dummy_past_residuals[: scheduler.config.solver_order]

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

           __snake_case:		Tuple					    =		sample, sample
           for t in range(UpperCamelCase__				,							time_step + scheduler.config.solver_order + 1						):
             __snake_case:		List[Any]					    =		scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__						).prev_sample
             __snake_case:		Dict					    =		new_scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__						).prev_sample

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




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         pass




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       Any=0				,							**A					:       Any						):
         __snake_case:		Optional[Any]					    =		dict(self.forward_default_kwargs						)
         __snake_case:		int					    =		kwargs.pop("""num_inference_steps"""				,							UpperCamelCase__						)
         __snake_case:		str					    =		self.dummy_sample
         __snake_case:		Any					    =		0.1 * sample
         __snake_case:		str					    =		[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           __snake_case:		List[Any]					    =		self.get_scheduler_config()
           __snake_case:		List[Any]					    =		scheduler_class(**UpperCamelCase__						)
           scheduler.set_timesteps(UpperCamelCase__						)

           # copy over dummy past residuals (must be after setting timesteps)
           __snake_case:		Dict					    =		dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(UpperCamelCase__						)
             __snake_case:		int					    =		scheduler_class.from_pretrained(UpperCamelCase__						)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(UpperCamelCase__						)

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

           __snake_case:		Union[str, Any]					    =		scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__						).prev_sample
           __snake_case:		Optional[Any]					    =		new_scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__						).prev_sample

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




       def 				UpperCAmelCase__					(    self					:       Any				,							A					:       List[str]=None				,							**A					:       int						):
         if scheduler is None:
           __snake_case:		Any					    =		self.scheduler_classes[0]
           __snake_case:		Union[str, Any]					    =		self.get_scheduler_config(**UpperCamelCase__						)
           __snake_case:		Optional[Any]					    =		scheduler_class(**UpperCamelCase__						)

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

         for i, t in enumerate(scheduler.timesteps						):
           __snake_case:		str					    =		model(UpperCamelCase__				,							UpperCamelCase__						)
           __snake_case:		Any					    =		scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						).prev_sample

         return sample




       def 				UpperCAmelCase__					(    self					:       Dict						):
         __snake_case:		Any					    =		dict(self.forward_default_kwargs						)

         __snake_case:		int					    =		kwargs.pop("""num_inference_steps"""				,							UpperCamelCase__						)

         for scheduler_class in self.scheduler_classes:
           __snake_case:		str					    =		self.get_scheduler_config()
           __snake_case:		List[str]					    =		scheduler_class(**UpperCamelCase__						)

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

           if num_inference_steps is not None and hasattr(UpperCamelCase__				,							"""set_timesteps"""						):
             scheduler.set_timesteps(UpperCamelCase__						)
           elif num_inference_steps is not None and not hasattr(UpperCamelCase__				,							"""set_timesteps"""						):
             __snake_case:		Any					    =		num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           __snake_case:		Tuple					    =		[residual + 0.2, residual + 0.15, residual + 0.10]
           __snake_case:		Optional[Any]					    =		dummy_past_residuals[: scheduler.config.solver_order]

           __snake_case:		List[str]					    =		scheduler.timesteps[5]
           __snake_case:		Optional[Any]					    =		scheduler.timesteps[6]

           __snake_case:		Union[str, Any]					    =		scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__						).prev_sample
           __snake_case:		Optional[Any]					    =		scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							**UpperCamelCase__						).prev_sample

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




       def 				UpperCAmelCase__					(    self					:       str						):
         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__						)




       def 				UpperCAmelCase__					(    self					:       int						):
         self.check_over_configs(thresholding=UpperCamelCase__						)
         for order in [1, 2, 3]:
           for solver_type in ["midpoint", "heun"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=UpperCamelCase__				,							prediction_type=UpperCamelCase__				,							sample_max_value=UpperCamelCase__				,							algorithm_type="""dpmsolver++"""				,							solver_order=UpperCamelCase__				,							solver_type=UpperCamelCase__				,							)




       def 				UpperCAmelCase__					(    self					:       Any						):
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=UpperCamelCase__						)




       def 				UpperCAmelCase__					(    self					:       Tuple						):
         for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
           for solver_type in ["midpoint", "heun"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                   if order == 3:
                     continue
                 else:
                   self.check_over_configs(
                       solver_order=UpperCamelCase__				,							solver_type=UpperCamelCase__				,							prediction_type=UpperCamelCase__				,							algorithm_type=UpperCamelCase__				,							)
                 __snake_case:		List[str]					    =		self.full_loop(
                     solver_order=UpperCamelCase__				,							solver_type=UpperCamelCase__				,							prediction_type=UpperCamelCase__				,							algorithm_type=UpperCamelCase__				,							)
                 assert not torch.isnan(UpperCamelCase__						).any(), "Samples have nan numbers"




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         self.check_over_configs(lower_order_final=UpperCamelCase__						)
         self.check_over_configs(lower_order_final=UpperCamelCase__						)




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         self.check_over_configs(lambda_min_clipped=-float("""inf"""						)						)
         self.check_over_configs(lambda_min_clipped=-5.1						)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         self.check_over_configs(variance_type=UpperCamelCase__						)
         self.check_over_configs(variance_type="""learned_range"""						)




       def 				UpperCAmelCase__					(    self					:       Any						):
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=UpperCamelCase__				,							time_step=0						)




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Tuple					    =		self.full_loop()
         __snake_case:		str					    =		torch.mean(torch.abs(UpperCamelCase__						)						)

         assert abs(result_mean.item() - 0.3301						) < 1E-3




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Optional[Any]					    =		self.full_loop(thresholding=UpperCamelCase__				,							dynamic_thresholding_ratio=0.87				,							sample_max_value=0.5						)
         __snake_case:		Tuple					    =		torch.mean(torch.abs(UpperCamelCase__						)						)

         assert abs(result_mean.item() - 1.1364						) < 1E-3




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.full_loop(prediction_type="""v_prediction"""						)
         __snake_case:		Optional[Any]					    =		torch.mean(torch.abs(UpperCamelCase__						)						)

         assert abs(result_mean.item() - 0.2251						) < 1E-3




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Tuple					    =		self.full_loop(prediction_type="""v_prediction"""				,							use_karras_sigmas=UpperCamelCase__						)
         __snake_case:		int					    =		torch.mean(torch.abs(UpperCamelCase__						)						)

         assert abs(result_mean.item() - 0.2096						) < 1E-3




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         __snake_case:		List[Any]					    =		DPMSolverMultistepScheduler(**self.get_scheduler_config()						)
         __snake_case:		Optional[int]					    =		self.full_loop(scheduler=UpperCamelCase__						)
         __snake_case:		Dict					    =		torch.mean(torch.abs(UpperCamelCase__						)						)

         assert abs(result_mean.item() - 0.3301						) < 1E-3

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

         __snake_case:		str					    =		self.full_loop(scheduler=UpperCamelCase__						)
         __snake_case:		List[Any]					    =		torch.mean(torch.abs(UpperCamelCase__						)						)

         assert abs(result_mean.item() - 0.3301						) < 1E-3




       def 				UpperCAmelCase__					(    self					:       Dict						):
         __snake_case:		Any					    =		self.scheduler_classes[0]
         __snake_case:		str					    =		self.get_scheduler_config(thresholding=UpperCamelCase__				,							dynamic_thresholding_ratio=0						)
         __snake_case:		List[str]					    =		scheduler_class(**UpperCamelCase__						)

         __snake_case:		Dict					    =		10
         __snake_case:		Union[str, Any]					    =		self.dummy_model()
         __snake_case:		int					    =		self.dummy_sample_deter.half()
         scheduler.set_timesteps(UpperCamelCase__						)

         for i, t in enumerate(scheduler.timesteps						):
           __snake_case:		Tuple					    =		model(UpperCamelCase__				,							UpperCamelCase__						)
           __snake_case:		List[str]					    =		scheduler.step(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						).prev_sample

         assert sample.dtype == torch.floataa






       def 				UpperCAmelCase__					(    self					:       str				,							**A					:       Tuple						):
         for scheduler_class in self.scheduler_classes:
           __snake_case:		Optional[Any]					    =		self.get_scheduler_config(**UpperCamelCase__						)
           __snake_case:		List[str]					    =		scheduler_class(**UpperCamelCase__						)

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














'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      :     Optional[int]       	={'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Optional[int]       	=[
			    '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''VisualBertForMultipleChoice''',
			    '''VisualBertForPreTraining''',
			    '''VisualBertForQuestionAnswering''',
			    '''VisualBertForRegionToPhraseAlignment''',
			    '''VisualBertForVisualReasoning''',
			    '''VisualBertLayer''',
			    '''VisualBertModel''',
			    '''VisualBertPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_visual_bert import (
						    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VisualBertForMultipleChoice,
						    VisualBertForPreTraining,
						    VisualBertForQuestionAnswering,
						    VisualBertForRegionToPhraseAlignment,
						    VisualBertForVisualReasoning,
						    VisualBertLayer,
						    VisualBertModel,
						    VisualBertPreTrainedModel,
						)


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

