




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(KDPMaDiscreteScheduler,)
 UpperCamelCase		  =		10

 def 							a__					(							self   :    List[str]		,				**A_   :    Union[str, Any]    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       {
           'num_train_timesteps': 1100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**A_    )
       return config

 def 							a__					(							self   :    int    )  ->      Any:

       """simple docstring"""






       for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=A_    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      Optional[Any]:

       """simple docstring"""






       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,				[0.0002, 0.002, 0.02]    ):
             self.check_over_configs(beta_start=A_		,				beta_end=A_    )

 def 							a__					(							self   :    Tuple    )  ->      Dict:

       """simple docstring"""






       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=A_    )

 def 							a__					(							self   :    int    )  ->      List[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    Optional[Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config(prediction_type='v_prediction'    )
       lowerCamelCase_							=       scheduler_class(**A_    )

       scheduler.set_timesteps(self.num_inference_steps    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCamelCase_							=       sample.to(A_    )

       for i, t in enumerate(scheduler.timesteps    ):
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_934E-07    ) < 1E-2
             assert abs(result_mean.item() - 6.1_112E-10    ) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.693_428_650_170_972E-07    ) < 1E-2
             assert abs(result_mean.item() - 0.0002    ) < 1E-3

 def 							a__					(							self   :    Union[str, Any]    )  ->      Any:

       """simple docstring"""






       if torch_device == "mps":
             return
       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**A_    )

       scheduler.set_timesteps(self.num_inference_steps    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCamelCase_							=       sample.to(A_    )

       for i, t in enumerate(scheduler.timesteps    ):
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125    ) < 1E-2
             assert abs(result_mean.item() - 0.0266    ) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125    ) < 1E-2
             assert abs(result_mean.item() - 0.0266    ) < 1E-3





 def 							a__					(							self   :    Union[str, Any]    )  ->      List[str]:

       """simple docstring"""






       if torch_device == "mps":
             return
       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**A_    )

       scheduler.set_timesteps(self.num_inference_steps		,				device=A_    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter.to(A_    ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       if str(A_    ).startswith('cpu'    ):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125    ) < 1E-2
             assert abs(result_mean.item() - 0.0266    ) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125    ) < 1E-2
             assert abs(result_mean.item() - 0.0266    ) < 1E-3




from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """canine"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0xE000  ,				__SCREAMING_SNAKE_CASE			:   int=0xE001  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1_28  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	max_position_embeddings
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	type_vocab_size
   __a							=	layer_norm_eps

   # Character config:
   __a							=	downsampling_rate
   __a							=	upsampling_kernel_size
   __a							=	num_hash_functions
   __a							=	num_hash_buckets
   __a							=	local_transformer_stride


