
'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A			      =      {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['GPTSw3Tokenizer']


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

else:
					import sys

					A			      =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


'''simple docstring'''






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	(DPMSolverSinglestepScheduler,)
					_lowerCAmelCase			     =	(('''num_inference_steps''', 25),)
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= {
									    'num_train_timesteps': 1000,
									    '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(**A      )
									return config
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


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

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			List[Any]									= scheduler_class.from_pretrained(A      )
																	new_scheduler.set_timesteps(A      )
																	# copy over dummy past residuals
																	lowerCamelCase					:			Optional[int]									= dummy_past_residuals[: new_scheduler.config.solver_order]

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

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

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


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

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Tuple									= self.get_scheduler_config()
													lowerCamelCase					:			Optional[Any]									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			Tuple									= scheduler_class.from_pretrained(A      )
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(A      )

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

													lowerCamelCase					:			int									= scheduler.step(A,    A,    A,    **A      ).prev_sample
													lowerCamelCase					:			Dict									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

													assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self,    A=None,    **A      ):

									"""simple docstring"""


									if scheduler is None:
													lowerCamelCase					:			Any									= self.scheduler_classes[0]
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
									lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			Any									= 10
									lowerCamelCase					:			Dict									= self.dummy_model()
									lowerCamelCase					:			Any									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			Dict									= model(A,    A      )
													lowerCamelCase					:			List[str]									= scheduler.step(A,    A,    A      ).prev_sample

									return sample
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			Dict									= 50
									lowerCamelCase					:			Tuple									= self.dummy_model()
									lowerCamelCase					:			Optional[int]									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]      ):
													lowerCamelCase					:			Any									= model(A,    A      )
													lowerCamelCase					:			Optional[int]									= scheduler.step(A,    A,    A      ).prev_sample

									lowerCamelCase					:			Any									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2574      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

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

									lowerCamelCase					:			Dict									= DEISMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Any									= UniPCMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[Any]									= DPMSolverSinglestepScheduler.from_config(scheduler.config      )

									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(thresholding=A      )
									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=A,    prediction_type=A,    sample_max_value=A,    algorithm_type='dpmsolver++',    solver_order=A,    solver_type=A,    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									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=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									lowerCamelCase					:			Optional[Any]									= self.full_loop(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									assert not torch.isnan(A      ).any(), "Samples have nan numbers"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lower_order_final=A      )
									self.check_over_configs(lower_order_final=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
									self.check_over_configs(lambda_min_clipped=-5.1      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(variance_type=A      )
									self.check_over_configs(variance_type='learned_range'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=A,    time_step=0      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop()
									lowerCamelCase					:			str									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop(use_karras_sigmas=A      )
									lowerCamelCase					:			Tuple									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2248      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction'      )
									lowerCamelCase					:			Dict									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.1453      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction',    use_karras_sigmas=A      )
									lowerCamelCase					:			Optional[Any]									= torch.mean(torch.abs(A      )      )

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Dict									= self.get_scheduler_config(thresholding=A,    dynamic_thresholding_ratio=0      )
									lowerCamelCase					:			str									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= 10
									lowerCamelCase					:			List[str]									= self.dummy_model()
									lowerCamelCase					:			int									= self.dummy_sample_deter.half()
									scheduler.set_timesteps(A      )

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

									assert sample.dtype == torch.floataa
