
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=(DPMSolverSinglestepScheduler,)
				_lowercase    	=(('''num_inference_steps''', 25),)




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			{
							    "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",
							    "lambda_min_clipped": -float("inf"  ),
							    "variance_type": None,
							}

							config.update(**_UpperCamelCase  )
							return config




				def   __a		(      self       ,       _UpperCamelCase=0       ,       **_UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )
							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			self.dummy_sample
							lowerCAmelCase_      			=			0.1 * sample
							lowerCAmelCase_      			=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

										lowerCAmelCase_			,						lowerCAmelCase_      			=			sample, sample
										for t in range(_UpperCamelCase       ,       time_step + scheduler.config.solver_order + 1  ):
													lowerCAmelCase_      			=			scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  ).prev_sample
													lowerCAmelCase_      			=			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   __a		(      self  )       ->   str:
							pass




				def   __a		(      self       ,       _UpperCamelCase=0       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )
							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			self.dummy_sample
							lowerCAmelCase_      			=			0.1 * sample
							lowerCAmelCase_      			=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
										lowerCAmelCase_      			=			self.get_scheduler_config()
										lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )
										scheduler.set_timesteps(_UpperCamelCase  )

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

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

										lowerCAmelCase_      			=			scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  ).prev_sample
										lowerCAmelCase_      			=			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   __a		(      self       ,       _UpperCamelCase=None       ,       **_UpperCamelCase  )       ->   Tuple:
							if scheduler is None:
										lowerCAmelCase_      			=			self.scheduler_classes[0]
										lowerCAmelCase_      			=			self.get_scheduler_config(**_UpperCamelCase  )
										lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(**_UpperCamelCase  )
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			10
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							scheduler.set_timesteps(_UpperCamelCase  )

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

							return sample




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
							lowerCAmelCase_      			=			50
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							scheduler.set_timesteps(_UpperCamelCase  )

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]  ):
										lowerCAmelCase_      			=			model(_UpperCamelCase       ,       _UpperCamelCase  )
										lowerCAmelCase_      			=			scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  ).prev_sample

							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2574  ) < 1e-3




				def   __a		(      self  )       ->   Any:
							for timesteps in [25, 50, 100, 999, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							lowerCAmelCase_      			=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
							lowerCAmelCase_      			=			self.full_loop(scheduler=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2791  ) < 1e-3

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

							lowerCAmelCase_      			=			self.full_loop(scheduler=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2791  ) < 1e-3




				def   __a		(      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   __a		(      self  )       ->   Optional[Any]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_UpperCamelCase  )




				def   __a		(      self  )       ->   Dict:
							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
										for solver_type in ["midpoint", "heun"]:
													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       ,       )
																			lowerCAmelCase_      			=			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   __a		(      self  )       ->   List[str]:
							self.check_over_configs(lower_order_final=_UpperCamelCase  )
							self.check_over_configs(lower_order_final=_UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[int]:
							self.check_over_configs(lambda_min_clipped=-float("inf"  )  )
							self.check_over_configs(lambda_min_clipped=-5.1  )




				def   __a		(      self  )       ->   List[str]:
							self.check_over_configs(variance_type=_UpperCamelCase  )
							self.check_over_configs(variance_type="learned_range"  )




				def   __a		(      self  )       ->   List[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   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			self.full_loop()
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2791  ) < 1e-3




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			self.full_loop(use_karras_sigmas=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2248  ) < 1e-3




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.full_loop(prediction_type="v_prediction"  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.1453  ) < 1e-3




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			self.full_loop(prediction_type="v_prediction"       ,       use_karras_sigmas=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.0649  ) < 1e-3




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(thresholding=_UpperCamelCase       ,       dynamic_thresholding_ratio=0  )
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			10
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(_UpperCamelCase  )

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

							assert sample.dtype == torch.floataa





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A     =  {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



