





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	(KDPMaDiscreteScheduler,)
 UpperCAmelCase__									=	10
 def   A_			(   self						:   str    ,  **UpperCAmelCase						:   Any			)    ->			Optional[int]:
     lowerCamelCase__					:   Any      				=	{
         'num_train_timesteps': 1100,
         'beta_start': 0.0_0_0_1,
         'beta_end': 0.0_2,
         'beta_schedule': 'linear',
     }

     config.update(**UpperCAmelCase			)
     return config
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase			)
 def   A_			(   self						:   Tuple			)    ->			Any:
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
         self.check_over_configs(beta_start=UpperCAmelCase    ,  beta_end=UpperCAmelCase			)
 def   A_			(   self						:   Tuple			)    ->			Optional[int]:
     for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			str:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			str:
     lowerCamelCase__					:   str      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   List[Any]      				=	self.get_scheduler_config(prediction_type='v_prediction'			)
     lowerCamelCase__					:   Optional[int]      				=	scheduler_class(**UpperCAmelCase			)

     scheduler.set_timesteps(self.num_inference_steps			)

     lowerCamelCase__					:   Optional[Any]      				=	self.dummy_model()
     lowerCamelCase__					:   int      				=	self.dummy_sample_deter * scheduler.init_noise_sigma
     lowerCamelCase__					:   List[str]      				=	sample.to(UpperCAmelCase			)

     for i, t in enumerate(scheduler.timesteps			):
         lowerCamelCase__					:   Union[str, Any]      				=	scheduler.scale_model_input(UpperCAmelCase    ,  UpperCAmelCase			)

         lowerCamelCase__					:   List[str]      				=	model(UpperCAmelCase    ,  UpperCAmelCase			)

         lowerCamelCase__					:   int      				=	scheduler.step(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	output.prev_sample

     lowerCamelCase__					:   Tuple      				=	torch.sum(torch.abs(UpperCAmelCase			)			)
     lowerCamelCase__					:   Union[str, Any]      				=	torch.mean(torch.abs(UpperCAmelCase			)			)

     if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
         assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
     else:
         # CUDA
         assert abs(result_sum.item() - 4.693428650170972e-07			) < 1e-2
         assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-3
 def   A_			(   self						:   Union[str, Any]			)    ->			str:
     if torch_device == "mps":
         return
     lowerCamelCase__					:   Optional[int]      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   Dict      				=	self.get_scheduler_config()
     lowerCamelCase__					:   str      				=	scheduler_class(**UpperCAmelCase			)

     scheduler.set_timesteps(self.num_inference_steps			)

     lowerCamelCase__					:   List[str]      				=	self.dummy_model()
     lowerCamelCase__					:   Dict      				=	self.dummy_sample_deter * scheduler.init_noise_sigma
     lowerCamelCase__					:   Any      				=	sample.to(UpperCAmelCase			)

     for i, t in enumerate(scheduler.timesteps			):
         lowerCamelCase__					:   List[Any]      				=	scheduler.scale_model_input(UpperCAmelCase    ,  UpperCAmelCase			)

         lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase    ,  UpperCAmelCase			)

         lowerCamelCase__					:   Union[str, Any]      				=	scheduler.step(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
         lowerCamelCase__					:   Optional[int]      				=	output.prev_sample

     lowerCamelCase__					:   List[Any]      				=	torch.sum(torch.abs(UpperCAmelCase			)			)
     lowerCamelCase__					:   Union[str, Any]      				=	torch.mean(torch.abs(UpperCAmelCase			)			)

     if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
         assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3
     else:
         # CUDA
         assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
         assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3







 def   A_			(   self						:   Optional[Any]			)    ->			Optional[int]:
     if torch_device == "mps":
         return
     lowerCamelCase__					:   List[Any]      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   List[str]      				=	self.get_scheduler_config()
     lowerCamelCase__					:   Union[str, Any]      				=	scheduler_class(**UpperCAmelCase			)

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

     lowerCamelCase__					:   int      				=	self.dummy_model()
     lowerCamelCase__					:   Union[str, Any]      				=	self.dummy_sample_deter.to(UpperCAmelCase			) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
         lowerCamelCase__					:   Tuple      				=	scheduler.scale_model_input(UpperCAmelCase    ,  UpperCAmelCase			)

         lowerCamelCase__					:   int      				=	model(UpperCAmelCase    ,  UpperCAmelCase			)

         lowerCamelCase__					:   str      				=	scheduler.step(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
         lowerCamelCase__					:   Tuple      				=	output.prev_sample

     lowerCamelCase__					:   List[str]      				=	torch.sum(torch.abs(UpperCAmelCase			)			)
     lowerCamelCase__					:   Dict      				=	torch.mean(torch.abs(UpperCAmelCase			)			)

     if str(UpperCAmelCase			).startswith('cpu'			):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
         assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3
     else:
         # CUDA
         assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
         assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3












from pathlib import Path

import fire


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Union[str, Any]      				=	Path(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	Path(_UpperCAmelCase				)
    dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
    for path in src_dir.iterdir():
        lowerCamelCase__					:   Tuple      				=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
        lowerCamelCase__					:   Optional[int]      				=	dest_dir.joinpath(path.name				)
        print(_UpperCAmelCase				)
        dest_path.open('w'				).write('\n'.join(_UpperCAmelCase				)				)


if __name__ == "__main__":
    fire.Fire(minify)





