






import copy
import re



class 				__UpperCAmelCase		:
   __snake_case    :				Any									=							"hp"
   __snake_case    :				str									=							{}
   __snake_case    :				List[Any]									=							None







   @classmethod
   def 							UpperCamelCase				(     cls:  Optional[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[int]					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  prefix
         _SCREAMING_SNAKE_CASE     				=  defaults
         cls.build_naming_info()







   @staticmethod
   def 							UpperCamelCase				(     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  str					):







         '''simple docstring'''




         if len(UpperCAmelCase_					) == 0:
               return ""
         _SCREAMING_SNAKE_CASE     				=  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					):
               _SCREAMING_SNAKE_CASE     				=  word[:prefix_len]
               if prefix in info["reverse_short_word"]:
                     continue
               else:
                     _SCREAMING_SNAKE_CASE     				=  prefix
                     break

         if short_word is None:
               # Paranoid fallback
               def int_to_alphabetic(UpperCAmelCase_:  List[Any]					):
                     _SCREAMING_SNAKE_CASE     				=  """"""
                     while integer != 0:
                           _SCREAMING_SNAKE_CASE     				=  chr(ord("""A"""					) + integer % 10					) + s
                           integer //= 10
                     return s

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

         _SCREAMING_SNAKE_CASE     				=  short_word
         _SCREAMING_SNAKE_CASE     				=  word
         return short_word







   @staticmethod
   def 							UpperCamelCase				(     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  List[str]					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  param_name.split("""_"""					)

         _SCREAMING_SNAKE_CASE     				=  [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
         _SCREAMING_SNAKE_CASE     				=  ["""""", """_"""]

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

         return param_name







   @staticmethod
   def 							UpperCamelCase				(     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Optional[int]					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  TrialShortNamer.shortname_for_key(UpperCAmelCase_				,     UpperCAmelCase_					)
         _SCREAMING_SNAKE_CASE     				=  short_name
         _SCREAMING_SNAKE_CASE     				=  param_name







   @classmethod
   def 							UpperCamelCase				(     cls:  str					):







         '''simple docstring'''




         if cls.NAMING_INFO is not None:
               return

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

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

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

         _SCREAMING_SNAKE_CASE     				=  info







   @classmethod
   def 							UpperCamelCase				(     cls:  Any				,     UpperCAmelCase_:  Optional[Any]					):







         '''simple docstring'''




         cls.build_naming_info()
         assert cls.PREFIX is not None
         _SCREAMING_SNAKE_CASE     				=  [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

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

               if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
                     _SCREAMING_SNAKE_CASE     				=  1 if v else 0

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

         return "_".join(UpperCAmelCase_					)







   @classmethod
   def 							UpperCamelCase				(     cls:  int				,     UpperCAmelCase_:  int					):







         '''simple docstring'''




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

         _SCREAMING_SNAKE_CASE     				=  {}

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

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

               _SCREAMING_SNAKE_CASE     				=  p_v

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

         return parameters









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




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







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







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									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 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

