
'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[Any]       =					(DEISMultistepScheduler,)
		a							:					Union[str, Any]       =					(("""num_inference_steps""", 25),)




		def     lowercase       (				self					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								}

								config.update(**UpperCamelCase      )
								return config




		def     lowercase       (				self					,						UpperCamelCase=0					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dict(self.forward_default_kwargs      )
								_SCREAMING_SNAKE_CASE				= kwargs.pop("num_inference_steps"					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.dummy_sample
								_SCREAMING_SNAKE_CASE				= 0.1 * sample
								_SCREAMING_SNAKE_CASE				= [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE				= self.get_scheduler_config(**UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= scheduler_class(**UpperCamelCase      )
														scheduler.set_timesteps(UpperCamelCase      )
														# copy over dummy past residuals
														_SCREAMING_SNAKE_CASE				= dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= scheduler_class.from_pretrained(UpperCamelCase      )
																				new_scheduler.set_timesteps(UpperCamelCase      )
																				# copy over dummy past residuals
																				_SCREAMING_SNAKE_CASE				= dummy_past_residuals[: new_scheduler.config.solver_order]

														_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= sample, sample
														for t in range(UpperCamelCase					,						time_step + scheduler.config.solver_order + 1      ):
																				_SCREAMING_SNAKE_CASE				= scheduler.step(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase      ).prev_sample
																				_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								pass




		def     lowercase       (				self					,						UpperCamelCase=0					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dict(self.forward_default_kwargs      )
								_SCREAMING_SNAKE_CASE				= kwargs.pop("num_inference_steps"					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.dummy_sample
								_SCREAMING_SNAKE_CASE				= 0.1 * sample
								_SCREAMING_SNAKE_CASE				= [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE				= self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE				= scheduler_class(**UpperCamelCase      )
														scheduler.set_timesteps(UpperCamelCase      )

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(UpperCamelCase      )
																				_SCREAMING_SNAKE_CASE				= 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)
																				_SCREAMING_SNAKE_CASE				= dummy_past_residuals[: new_scheduler.config.solver_order]

														_SCREAMING_SNAKE_CASE				= scheduler.step(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase      ).prev_sample
														_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase=None					,						**UpperCamelCase      ):
								if scheduler is None:
														_SCREAMING_SNAKE_CASE				= self.scheduler_classes[0]
														_SCREAMING_SNAKE_CASE				= self.get_scheduler_config(**UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= scheduler_class(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.scheduler_classes[0]
								_SCREAMING_SNAKE_CASE				= self.get_scheduler_config(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= scheduler_class(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= 10
								_SCREAMING_SNAKE_CASE				= self.dummy_model()
								_SCREAMING_SNAKE_CASE				= self.dummy_sample_deter
								scheduler.set_timesteps(UpperCamelCase      )

								for i, t in enumerate(scheduler.timesteps      ):
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= scheduler.step(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ).prev_sample

								return sample




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= dict(self.forward_default_kwargs      )

								_SCREAMING_SNAKE_CASE				= kwargs.pop("num_inference_steps"					,						UpperCamelCase      )

								for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE				= self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE				= scheduler_class(**UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= self.dummy_sample
														_SCREAMING_SNAKE_CASE				= 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"      ):
																				_SCREAMING_SNAKE_CASE				= num_inference_steps

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

														_SCREAMING_SNAKE_CASE				= scheduler.timesteps[5]
														_SCREAMING_SNAKE_CASE				= scheduler.timesteps[6]

														_SCREAMING_SNAKE_CASE				= scheduler.step(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase      ).prev_sample
														_SCREAMING_SNAKE_CASE				= scheduler.step(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase      ).prev_sample

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




		def     lowercase       (				self      ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								_SCREAMING_SNAKE_CASE				= DEISMultistepScheduler(**self.get_scheduler_config()      )
								_SCREAMING_SNAKE_CASE				= self.full_loop(scheduler=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= torch.mean(torch.abs(UpperCamelCase      )      )

								assert abs(result_mean.item() - 0.2_39_16      ) < 1e-3

								_SCREAMING_SNAKE_CASE				= DPMSolverSinglestepScheduler.from_config(scheduler.config      )
								_SCREAMING_SNAKE_CASE				= DPMSolverMultistepScheduler.from_config(scheduler.config      )
								_SCREAMING_SNAKE_CASE				= UniPCMultistepScheduler.from_config(scheduler.config      )
								_SCREAMING_SNAKE_CASE				= DEISMultistepScheduler.from_config(scheduler.config      )

								_SCREAMING_SNAKE_CASE				= self.full_loop(scheduler=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= torch.mean(torch.abs(UpperCamelCase      )      )

								assert abs(result_mean.item() - 0.2_39_16      ) < 1e-3




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




		def     lowercase       (				self      ):
								self.check_over_configs(thresholding=UpperCamelCase      )
								for order in [1, 2, 3]:
														for solver_type in ["logrho"]:
																				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="deis"					,						solver_order=UpperCamelCase					,						solver_type=UpperCamelCase					,						)




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




		def     lowercase       (				self      ):
								for algorithm_type in ["deis"]:
														for solver_type in ["logrho"]:
																				for order in [1, 2, 3]:
																										for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    solver_order=UpperCamelCase					,						solver_type=UpperCamelCase					,						prediction_type=UpperCamelCase					,						algorithm_type=UpperCamelCase					,						)
																																_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								self.check_over_configs(lower_order_final=UpperCamelCase      )
								self.check_over_configs(lower_order_final=UpperCamelCase      )




		def     lowercase       (				self      ):
								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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.full_loop()
								_SCREAMING_SNAKE_CASE				= torch.mean(torch.abs(UpperCamelCase      )      )

								assert abs(result_mean.item() - 0.2_39_16      ) < 1e-3




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.full_loop(prediction_type="v_prediction"      )
								_SCREAMING_SNAKE_CASE				= torch.mean(torch.abs(UpperCamelCase      )      )

								assert abs(result_mean.item() - 0.0_91      ) < 1e-3




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.scheduler_classes[0]
								_SCREAMING_SNAKE_CASE				= self.get_scheduler_config(thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0      )
								_SCREAMING_SNAKE_CASE				= scheduler_class(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= 10
								_SCREAMING_SNAKE_CASE				= self.dummy_model()
								_SCREAMING_SNAKE_CASE				= self.dummy_sample_deter.half()
								scheduler.set_timesteps(UpperCamelCase      )

								for i, t in enumerate(scheduler.timesteps      ):
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= scheduler.step(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ).prev_sample

								assert sample.dtype == torch.floataa

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						_snake_case				:   str     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)