




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (CMStochasticIterativeScheduler,)
    lowerCAmelCase         = 10





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Union[str, Any]  =     {
             '''num_train_timesteps''': 201,
             '''sigma_min''': 0.0_02,
             '''sigma_max''': 80.0,
         }

         config.update(**_SCREAMING_SNAKE_CASE    )
         return config





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     10

         UpperCAmelCase_     :				str  =     self.get_scheduler_config()
         UpperCAmelCase_     :				List[str]  =     self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE    )

         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Tuple  =     scheduler.timesteps[0]
         UpperCAmelCase_     :				Optional[int]  =     scheduler.timesteps[1]

         UpperCAmelCase_     :				int  =     self.dummy_sample
         UpperCAmelCase_     :				List[Any]  =     0.1 * sample

         UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample
         UpperCAmelCase_     :				Any  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample

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





    def 				a__ ( self    )					->						Any:
         for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         for clip_denoised in [True, False]:
              self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Tuple  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				str  =     self.get_scheduler_config()
         UpperCAmelCase_     :				int  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     1
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     scheduler.timesteps

         UpperCAmelCase_     :				Dict  =     torch.manual_seed(0    )

         UpperCAmelCase_     :				List[str]  =     self.dummy_model()
         UpperCAmelCase_     :				Dict  =     self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(_SCREAMING_SNAKE_CASE    ):
              # 1. scale model input
              UpperCAmelCase_     :				Union[str, Any]  =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 2. predict noise residual
              UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 3. predict previous sample x_t-1
              UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,generator=_SCREAMING_SNAKE_CASE    ).prev_sample

              UpperCAmelCase_     :				List[Any]  =     pred_prev_sample

         UpperCAmelCase_     :				Dict  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Tuple  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 1_92.76_14    ) < 1e-2
         assert abs(result_mean.item() - 0.25_10    ) < 1e-3





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[str]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				List[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Union[str, Any]  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[int]  =     [106, 0]
         scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     scheduler.timesteps

         UpperCAmelCase_     :				Union[str, Any]  =     torch.manual_seed(0    )

         UpperCAmelCase_     :				Union[str, Any]  =     self.dummy_model()
         UpperCAmelCase_     :				Optional[int]  =     self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
              # 1. scale model input
              UpperCAmelCase_     :				Dict  =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 2. predict noise residual
              UpperCAmelCase_     :				Union[str, Any]  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # 3. predict previous sample x_t-1
              UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,generator=_SCREAMING_SNAKE_CASE    ).prev_sample

              UpperCAmelCase_     :				Optional[int]  =     pred_prev_sample

         UpperCAmelCase_     :				Optional[int]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Tuple  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 3_47.63_57    ) < 1e-2
         assert abs(result_mean.item() - 0.45_27    ) < 1e-3





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				str  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Optional[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Any  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     [39, 30, 12, 15, 0]

         with self.assertRaises(_SCREAMING_SNAKE_CASE  ,msg='''`timesteps` must be in descending order.'''    ):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Union[str, Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Dict  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     [39, 30, 12, 1, 0]
         UpperCAmelCase_     :				List[Any]  =     len(_SCREAMING_SNAKE_CASE    )

         with self.assertRaises(_SCREAMING_SNAKE_CASE  ,msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''    ):
              scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE  ,timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Any  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				List[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Any  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             _SCREAMING_SNAKE_CASE  ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''  ,):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )





__a							     =					'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Union[str, Any]  =     f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(_lowercase )

     UpperCAmelCase_     :				Any  =     ''''''.join(bin(_lowercase )[2:].zfill(8 ) for byte in data )

     UpperCAmelCase_     :				Any  =     len(_lowercase ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          UpperCAmelCase_     :				Union[str, Any]  =     B'''=''' * ((6 - len(_lowercase ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(_lowercase ) % 6)
     else:
          UpperCAmelCase_     :				int  =     B''''''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]					,		2 )]
             for index in range(0					,		len(_lowercase )					,		6 ) ).encode()
         + padding
     )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ) and not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Tuple  =     (
              '''argument should be a bytes-like object or ASCII string, '''
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(_lowercase )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(_lowercase					,		_lowercase ):
          try:
               UpperCAmelCase_     :				Any  =     encoded_data.decode('''utf-8''' )
          except UnicodeDecodeError:
               raise ValueError('''base64 encoded data should only contain ASCII characters''' )

     UpperCAmelCase_     :				str  =     encoded_data.count('''=''' )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(_lowercase ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          UpperCAmelCase_     :				List[Any]  =     encoded_data[:-padding]

          UpperCAmelCase_     :				List[Any]  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
     else:
          UpperCAmelCase_     :				Tuple  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )

     UpperCAmelCase_     :				str  =     [
         int(binary_stream[index : index + 8]					,		2 )
         for index in range(0					,		len(_lowercase )					,		8 )
     ]

     return bytes(_lowercase )


if __name__ == "__main__":
     import doctest

     doctest.testmod()