import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							(UnCLIPScheduler,)




   def 			__a		(					self    :       Dict ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {
         """num_train_timesteps""": 10_00,
         """variance_type""": """fixed_small_log""",
         """clip_sample""": True,
         """clip_sample_range""": 1.0,
         """prediction_type""": """epsilon""",
     }

     config.update(**_lowercase   )
     return config




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for timesteps in [1, 5, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowercase   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






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




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



     """simple docstring"""






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




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for time_step in [0, 5_00, 9_99]:
       for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
         if prev_timestep is not None and prev_timestep >= time_step:
           continue

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




   def 			__a		(					self    :       List[str]   ):



     """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(**_lowercase   )

     assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_54_96_25   )   ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.9_99_49_87   )   ) < 1E-5




   def 			__a		(					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(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   0.5

     assert scheduler._get_variance(1 ,			predicted_variance=_lowercase   ) - -10.1_71_27_90 < 1E-5
     assert scheduler._get_variance(4_87 ,			predicted_variance=_lowercase   ) - -5.7_99_80_52 < 1E-5
     assert scheduler._get_variance(9_99 ,			predicted_variance=_lowercase   ) - -0.0_01_00_11 < 1E-5




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**_lowercase   )

     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(_lowercase   ):
       # 1. predict noise residual
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			_lowercase   )

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

       SCREAMING_SNAKE_CASE__    							=   pred_prev_sample

     SCREAMING_SNAKE_CASE__    							=   torch.sum(torch.abs(_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   torch.mean(torch.abs(_lowercase   )   )

     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 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**_lowercase   )

     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(_lowercase   ):
       # 1. predict noise residual
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			_lowercase   )

       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(
           _lowercase ,			_lowercase ,			_lowercase ,			prev_timestep=_lowercase ,			generator=_lowercase   ).prev_sample

       SCREAMING_SNAKE_CASE__    							=   pred_prev_sample

     SCREAMING_SNAKE_CASE__    							=   torch.sum(torch.abs(_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   torch.mean(torch.abs(_lowercase   )   )

     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 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     pass

import enum
import shutil
import sys


__lowerCamelCase     ,		__lowerCamelCase    :				Optional[Any]							     =	shutil.get_terminal_size()

__lowerCamelCase    :				Any							     =	{'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}







class __snake_case       (		enum.Enum	):
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							1





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]=""			)							->      int:







  """simple docstring"""





  sys.stdout.write(str(__UpperCamelCase			) + end			)
  sys.stdout.flush()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Optional[Any]=""			)							->      Dict:







  """simple docstring"""





  forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[Any]:







  """simple docstring"""





  forceWrite("""\r"""			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: str			)							->      str:







  """simple docstring"""





  forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Dict:







  """simple docstring"""





  forceWrite(""" """ * TERMINAL_WIDTH			)
  reset_cursor()






def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  reset_cursor()
  forceWrite("""-""" * TERMINAL_WIDTH			)
