

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: str												=	(DPMSolverMultistepScheduler,)
	_lowercase	: Union[str, Any]												=	(("num_inference_steps", 2_5),)







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			**__UpperCamelCase:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     {
						    'num_train_timesteps': 10_00,
						    '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 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			Any=0	,			**__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     dict(self.forward_default_kwargs   )
						__magic_name__				=     kwargs.pop('num_inference_steps'	,			__UpperCamelCase   )
						__magic_name__				=     self.dummy_sample
						__magic_name__				=     0.1 * sample
						__magic_name__				=     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

											__magic_name__,						__magic_name__				=     sample, sample
											for t in range(__UpperCamelCase	,			time_step + scheduler.config.solver_order + 1   ):
																__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase   ).prev_sample
																__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						pass







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]	,			__UpperCamelCase:			Optional[Any]=0	,			**__UpperCamelCase:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     dict(self.forward_default_kwargs   )
						__magic_name__				=     kwargs.pop('num_inference_steps'	,			__UpperCamelCase   )
						__magic_name__				=     self.dummy_sample
						__magic_name__				=     0.1 * sample
						__magic_name__				=     [residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
											__magic_name__				=     self.get_scheduler_config()
											__magic_name__				=     scheduler_class(**__UpperCamelCase   )
											scheduler.set_timesteps(__UpperCamelCase   )

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

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

											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase   ).prev_sample
											__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			int=None	,			**__UpperCamelCase:			Any   ):

						'''simple docstring'''





						if scheduler is None:
											__magic_name__				=     self.scheduler_classes[0]
											__magic_name__				=     self.get_scheduler_config(**__UpperCamelCase   )
											__magic_name__				=     scheduler_class(**__UpperCamelCase   )

						__magic_name__				=     10
						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter
						scheduler.set_timesteps(__UpperCamelCase   )

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

						return sample







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

						'''simple docstring'''





						__magic_name__				=     dict(self.forward_default_kwargs   )

						__magic_name__				=     kwargs.pop('num_inference_steps'	,			__UpperCamelCase   )

						for scheduler_class in self.scheduler_classes:
											__magic_name__				=     self.get_scheduler_config()
											__magic_name__				=     scheduler_class(**__UpperCamelCase   )

											__magic_name__				=     self.dummy_sample
											__magic_name__				=     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'   ):
																__magic_name__				=     num_inference_steps

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

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

											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase   ).prev_sample
											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase   ).prev_sample

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						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 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						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	,			)
																										__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						self.check_over_configs(lower_order_final=__UpperCamelCase   )
						self.check_over_configs(lower_order_final=__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						self.check_over_configs(variance_type=__UpperCamelCase   )
						self.check_over_configs(variance_type='learned_range'   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[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=__UpperCamelCase	,			time_step=0   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop()
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

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







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

						'''simple docstring'''





						__magic_name__				=     self.full_loop(thresholding=__UpperCamelCase	,			dynamic_thresholding_ratio=0.87	,			sample_max_value=0.5   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop(prediction_type='v_prediction'   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop(prediction_type='v_prediction'	,			use_karras_sigmas=__UpperCamelCase   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

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







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

						'''simple docstring'''





						__magic_name__				=     DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
						__magic_name__				=     self.full_loop(scheduler=__UpperCamelCase   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

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

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

						__magic_name__				=     self.full_loop(scheduler=__UpperCamelCase   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config(thresholding=__UpperCamelCase	,			dynamic_thresholding_ratio=0   )
						__magic_name__				=     scheduler_class(**__UpperCamelCase   )

						__magic_name__				=     10
						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter.half()
						scheduler.set_timesteps(__UpperCamelCase   )

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

						assert sample.dtype == torch.floataa







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			**__UpperCamelCase:			str   ):

						'''simple docstring'''





						for scheduler_class in self.scheduler_classes:
											__magic_name__				=     self.get_scheduler_config(**__UpperCamelCase   )
											__magic_name__				=     scheduler_class(**__UpperCamelCase   )

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







from typing import TYPE_CHECKING

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


A__							=				{"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"]}

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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PegasusForCausalLM",
						    "PegasusForConditionalGeneration",
						    "PegasusModel",
						    "PegasusPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "TFPegasusForConditionalGeneration",
						    "TFPegasusModel",
						    "TFPegasusPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "FlaxPegasusForConditionalGeneration",
						    "FlaxPegasusModel",
						    "FlaxPegasusPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

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




