





"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  (UnCLIPScheduler,)







					def  _lowerCamelCase		(						self	,			**_UpperCAmelCase     ):
									__a :						Tuple			       =  {
									    "num_train_timesteps": 1000,
									    "variance_type": "fixed_small_log",
									    "clip_sample": True,
									    "clip_sample_range": 1.0,
									    "prediction_type": "epsilon",
									}

									config.update(**UpperCamelCase_     )
									return config







					def  _lowerCamelCase		(						self     ):
									for timesteps in [1, 5, 100, 1000]:
													self.check_over_configs(num_train_timesteps=UpperCamelCase_     )







					def  _lowerCamelCase		(						self     ):
									for variance in ["fixed_small_log", "learned_range"]:
													self.check_over_configs(variance_type=UpperCamelCase_     )







					def  _lowerCamelCase		(						self     ):
									for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=UpperCamelCase_     )







					def  _lowerCamelCase		(						self     ):
									for clip_sample_range in [1, 5, 10, 20]:
													self.check_over_configs(clip_sample_range=UpperCamelCase_     )







					def  _lowerCamelCase		(						self     ):
									for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(prediction_type=UpperCamelCase_     )







					def  _lowerCamelCase		(						self     ):
									for time_step in [0, 500, 999]:
													for prev_timestep in [None, 5, 100, 250, 500, 750]:
																	if prev_timestep is not None and prev_timestep >= time_step:
																					continue

																	self.check_over_forward(time_step=UpperCamelCase_	,			prev_timestep=UpperCamelCase_     )







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  self.scheduler_classes[0]
									__a :						Optional[int]			       =  self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									__a :						int			       =  scheduler_class(**UpperCamelCase_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.0_5_4_9_6_2_5     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.9_9_9_4_9_8_7     )     ) < 1e-5







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  self.scheduler_classes[0]
									__a :						str			       =  self.get_scheduler_config(variance_type='''learned_range'''     )
									__a :						Any			       =  scheduler_class(**UpperCamelCase_     )

									__a :						Tuple			       =  0.5

									assert scheduler._get_variance(1	,			predicted_variance=UpperCamelCase_     ) - -10.1712790 < 1e-5
									assert scheduler._get_variance(487	,			predicted_variance=UpperCamelCase_     ) - -5.7_9_9_8_0_5_2 < 1e-5
									assert scheduler._get_variance(999	,			predicted_variance=UpperCamelCase_     ) - -0.0_0_1_0_0_1_1 < 1e-5







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  self.scheduler_classes[0]
									__a :						Dict			       =  self.get_scheduler_config()
									__a :						Tuple			       =  scheduler_class(**UpperCamelCase_     )

									__a :						Union[str, Any]			       =  scheduler.timesteps

									__a :						Any			       =  self.dummy_model()
									__a :						List[Any]			       =  self.dummy_sample_deter
									__a :						Any			       =  torch.manual_seed(0     )

									for i, t in enumerate(UpperCamelCase_     ):
													# 1. predict noise residual
													__a :						Dict			       =  model(UpperCamelCase_	,			UpperCamelCase_     )

													# 2. predict previous mean of sample x_t-1
													__a :						Optional[int]			       =  scheduler.step(UpperCamelCase_	,			UpperCamelCase_	,			UpperCamelCase_	,			generator=UpperCamelCase_     ).prev_sample

													__a :						List[Any]			       =  pred_prev_sample

									__a :						Tuple			       =  torch.sum(torch.abs(UpperCamelCase_     )     )
									__a :						Tuple			       =  torch.mean(torch.abs(UpperCamelCase_     )     )

									assert abs(result_sum.item() - 252.2682495     ) < 1e-2
									assert abs(result_mean.item() - 0.3_2_8_4_7_4_3     ) < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  self.scheduler_classes[0]
									__a :						List[str]			       =  self.get_scheduler_config()
									__a :						str			       =  scheduler_class(**UpperCamelCase_     )

									scheduler.set_timesteps(25     )

									__a :						str			       =  scheduler.timesteps

									__a :						Optional[int]			       =  self.dummy_model()
									__a :						Dict			       =  self.dummy_sample_deter
									__a :						List[str]			       =  torch.manual_seed(0     )

									for i, t in enumerate(UpperCamelCase_     ):
													# 1. predict noise residual
													__a :						Tuple			       =  model(UpperCamelCase_	,			UpperCamelCase_     )

													if i + 1 == timesteps.shape[0]:
																	__a :						Optional[int]			       =  None
													else:
																	__a :						Dict			       =  timesteps[i + 1]

													# 2. predict previous mean of sample x_t-1
													__a :						Tuple			       =  scheduler.step(
													    UpperCamelCase_	,			UpperCamelCase_	,			UpperCamelCase_	,			prev_timestep=UpperCamelCase_	,			generator=UpperCamelCase_     ).prev_sample

													__a :						Optional[int]			       =  pred_prev_sample

									__a :						str			       =  torch.sum(torch.abs(UpperCamelCase_     )     )
									__a :						Union[str, Any]			       =  torch.mean(torch.abs(UpperCamelCase_     )     )

									assert abs(result_sum.item() - 258.2044983     ) < 1e-2
									assert abs(result_mean.item() - 0.3_3_6_2_0_3_8     ) < 1e-3







					def  _lowerCamelCase		(						self     ):
									pass







					def  _lowerCamelCase		(						self     ):
									pass










"""simple docstring"""






import copy
import re




class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  '''hp'''
					__lowerCAmelCase  =  {}
					__lowerCAmelCase  =  None







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  prefix
									__a :						List[str]			       =  defaults
									cls.build_naming_info()







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									if len(_UpperCAmelCase     ) == 0:
													return ""
									__a :						Optional[int]			       =  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(_UpperCAmelCase     ) + 1     ):
													__a :						str			       =  word[:prefix_len]
													if prefix in info["reverse_short_word"]:
																	continue
													else:
																	__a :						Tuple			       =  prefix
																	break

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

													__a :						Optional[int]			       =  0
													while True:
																	__a :						List[str]			       =  word + '''#''' + int_to_alphabetic(_UpperCAmelCase     )
																	if sword in info["reverse_short_word"]:
																					continue
																	else:
																					__a :						Optional[Any]			       =  sword
																					break

									__a :						List[str]			       =  short_word
									__a :						Dict			       =  word
									return short_word







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  param_name.split('''_'''     )

									__a :						str			       =  [TrialShortNamer.shortname_for_word(_UpperCAmelCase	,			_UpperCAmelCase     ) 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
									__a :						Dict			       =  ['''''', '''_''']

									for separator in separators:
													__a :						Union[str, Any]			       =  separator.join(_UpperCAmelCase     )
													if shortname not in info["reverse_short_param"]:
																	__a :						List[str]			       =  shortname
																	__a :						Union[str, Any]			       =  param_name
																	return shortname

									return param_name







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						int			       =  TrialShortNamer.shortname_for_key(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  short_name
									__a :						Optional[Any]			       =  param_name







					@classmethod
					def  _lowerCamelCase		(						cls     ):
									if cls.NAMING_INFO is not None:
													return

									__a :						Optional[Any]			       =  {
									    '''short_word''': {},
									    '''reverse_short_word''': {},
									    '''short_param''': {},
									    '''reverse_short_param''': {},
									}

									__a :						List[str]			       =  list(cls.DEFAULTS.keys()     )

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

									__a :						Optional[Any]			       =  info







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									cls.build_naming_info()
									assert cls.PREFIX is not None
									__a :						str			       =  [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

													__a :						Union[str, Any]			       =  cls.NAMING_INFO['''short_param'''][k]

													if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						str			       =  1 if v else 0

													__a :						Tuple			       =  '''''' if isinstance(_UpperCAmelCase	,			(int, float)     ) else '''-'''
													__a :						Dict			       =  f"""{key}{sep}{v}"""
													name.append(_UpperCAmelCase     )

									return "_".join(_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  repr[len(cls.PREFIX     ) + 1 :]
									if repr == "":
													__a :						Tuple			       =  []
									else:
													__a :						str			       =  repr.split('''_'''     )

									__a :						Optional[Any]			       =  {}

									for value in values:
													if "-" in value:
																	__a       ,						__a :						List[Any]			       =  value.split('''-'''     )
													else:
																	__a :						int			       =  re.sub('''[0-9.]'''	,			''''''	,			_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  float(re.sub('''[^0-9.]'''	,			''''''	,			_UpperCAmelCase     )     )

													__a :						Dict			       =  cls.NAMING_INFO['''reverse_short_param'''][p_k]

													__a :						Union[str, Any]			       =  p_v

									for k in cls.DEFAULTS:
													if k not in parameters:
																	__a :						Optional[int]			       =  cls.DEFAULTS[k]

									return parameters