
import copy
import re

class       a		:
							lowercase_		:						Optional[int]   							=  'hp'
							lowercase_		:						List[str]   							=  {}
							lowercase_		:						Any   							=  None







							@classmethod
							def       UpperCAmelCase__					(						cls  :  Any      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							prefix
								__lowerCAmelCase         =							defaults
								cls.build_naming_info()







							@staticmethod
							def       UpperCAmelCase__					(						snake_case__  :  Tuple      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								if len(snake_case__ ) == 0:
									return ""
								__lowerCAmelCase         =							None
								if any(char.isdigit() for char in word ):
									raise Exception(F"Parameters should not contain numbers: '{word}' contains a number" )
								if word in info["short_word"]:
									return info["short_word"][word]
								for prefix_len in range(1      ,				len(snake_case__ ) + 1 ):
									__lowerCAmelCase         =							word[:prefix_len]
									if prefix in info["reverse_short_word"]:
										continue
									else:
										__lowerCAmelCase         =							prefix
										break

								if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(snake_case__  :  Union[str, Any] ):
										__lowerCAmelCase         =							""
										while integer != 0:
											__lowerCAmelCase         =							chr(ord("A" ) + integer % 10 ) + s
											integer //= 10
										return s

									__lowerCAmelCase         =							0
									while True:
										__lowerCAmelCase         =							word + "#" + int_to_alphabetic(snake_case__ )
										if sword in info["reverse_short_word"]:
											continue
										else:
											__lowerCAmelCase         =							sword
											break

								__lowerCAmelCase         =							short_word
								__lowerCAmelCase         =							word
								return short_word







							@staticmethod
							def       UpperCAmelCase__					(						snake_case__  :  List[Any]      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							param_name.split("_" )

								__lowerCAmelCase         =							[TrialShortNamer.shortname_for_word(snake_case__      ,				snake_case__ ) for word in words]

								# We try to create a separatorless short name, but if there is a collision we have to fallback
								# to a separated short name
								__lowerCAmelCase         =							["", "_"]

								for separator in separators:
									__lowerCAmelCase         =							separator.join(snake_case__ )
									if shortname not in info["reverse_short_param"]:
										__lowerCAmelCase         =							shortname
										__lowerCAmelCase         =							param_name
										return shortname

								return param_name







							@staticmethod
							def       UpperCAmelCase__					(						snake_case__  :  Optional[int]      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							TrialShortNamer.shortname_for_key(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							short_name
								__lowerCAmelCase         =							param_name







							@classmethod
							def       UpperCAmelCase__					(						cls  :  Optional[int] ):
								"""simple docstring"""







								if cls.NAMING_INFO is not None:
									return

								__lowerCAmelCase         =							{
								    "short_word": {},
								    "reverse_short_word": {},
								    "short_param": {},
								    "reverse_short_param": {},
								}

								__lowerCAmelCase         =							list(cls.DEFAULTS.keys() )

								for k in field_keys:
									cls.add_new_param_name(snake_case__      ,				snake_case__ )

								__lowerCAmelCase         =							info







							@classmethod
							def       UpperCAmelCase__					(						cls  :  Optional[Any]      ,				snake_case__  :  str ):
								"""simple docstring"""







								cls.build_naming_info()
								assert cls.PREFIX is not None
								__lowerCAmelCase         =							[copy.copy(cls.PREFIX )]

								for k, v in params.items():
									if k not in cls.DEFAULTS:
										raise Exception(F"You should provide a default value for the param name {k} with value {v}" )
									if v == cls.DEFAULTS[k]:
										# The default value is not added to the name
										continue

									__lowerCAmelCase         =							cls.NAMING_INFO["short_param"][k]

									if isinstance(snake_case__      ,				snake_case__ ):
										__lowerCAmelCase         =							1 if v else 0

									__lowerCAmelCase         =							"" if isinstance(snake_case__      ,				(int, float) ) else "-"
									__lowerCAmelCase         =							F"{key}{sep}{v}"
									name.append(snake_case__ )

								return "_".join(snake_case__ )







							@classmethod
							def       UpperCAmelCase__					(						cls  :  Optional[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							repr[len(cls.PREFIX ) + 1 :]
								if repr == "":
									__lowerCAmelCase         =							[]
								else:
									__lowerCAmelCase         =							repr.split("_" )

								__lowerCAmelCase         =							{}

								for value in values:
									if "-" in value:
										__lowerCAmelCase   ,						__lowerCAmelCase         =							value.split("-" )
									else:
										__lowerCAmelCase         =							re.sub("[0-9.]"      ,				""      ,				snake_case__ )
										__lowerCAmelCase         =							float(re.sub("[^0-9.]"      ,				""      ,				snake_case__ ) )

									__lowerCAmelCase         =							cls.NAMING_INFO["reverse_short_param"][p_k]

									__lowerCAmelCase         =							p_v

								for k in cls.DEFAULTS:
									if k not in parameters:
										__lowerCAmelCase         =							cls.DEFAULTS[k]

								return parameters








import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  (DPMSolverSinglestepScheduler,)
							lowercase_		:						Optional[Any]   							=  (('num_inference_steps', 25),)







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "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(**snake_case__ )
								return config







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

									__lowerCAmelCase   ,						__lowerCAmelCase         =							sample, sample
									for t in range(snake_case__      ,				time_step + scheduler.config.solver_order + 1 ):
										__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
										__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

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







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__lowerCAmelCase         =							self.get_scheduler_config()
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )
									scheduler.set_timesteps(snake_case__ )

									# 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(snake_case__ )
										__lowerCAmelCase         =							scheduler_class.from_pretrained(snake_case__ )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(snake_case__ )

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

									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
									__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Optional[Any]=None      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

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

								return sample







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							50
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

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

								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_5_7_4 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 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=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								self.check_over_configs(thresholding=snake_case__ )
								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=snake_case__      ,				prediction_type=snake_case__      ,				sample_max_value=snake_case__      ,				algorithm_type="dpmsolver++"      ,				solver_order=snake_case__      ,				solver_type=snake_case__      ,				)







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str ):
								"""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=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												__lowerCAmelCase         =							self.full_loop(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												assert not torch.isnan(snake_case__ ).any(), "Samples have nan numbers"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.check_over_configs(lower_order_final=snake_case__ )
								self.check_over_configs(lower_order_final=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								self.check_over_configs(variance_type=snake_case__ )
								self.check_over_configs(variance_type="learned_range" )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop()
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_2_4_8 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction" )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.1_4_5_3 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction"      ,				use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.0_6_4_9 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(thresholding=snake_case__      ,				dynamic_thresholding_ratio=0 )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case__ )

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

								assert sample.dtype == torch.floataa






