



"""simple docstring"""







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     (KDPMaDiscreteScheduler,)
			__magic_name__     					=     10



			def 				__lowercase					(			self							:				Tuple   ,	**_lowercase							:				Optional[int]		)							->					Optional[Any]:
									snake_case   :			str						    = {
									    '''num_train_timesteps''': 1100,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**A_		)
									return config



			def 				__lowercase					(			self							:				Optional[int]		)							->					int:




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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:




									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 				__lowercase					(			self							:				List[str]		)							->					str:




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



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




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



			def 				__lowercase					(			self							:				int		)							->					Any:
									snake_case   :			Any						    = self.scheduler_classes[0]
									snake_case   :			Union[str, Any]						    = self.get_scheduler_config(prediction_type="v_prediction"		)
									snake_case   :			Union[str, Any]						    = scheduler_class(**A_		)

									scheduler.set_timesteps(self.num_inference_steps		)

									snake_case   :			str						    = self.dummy_model()
									snake_case   :			Any						    = self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case   :			Dict						    = sample.to(A_		)

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

															snake_case   :			Dict						    = model(A_   ,	A_		)

															snake_case   :			Any						    = scheduler.step(A_   ,	A_   ,	A_		)
															snake_case   :			Dict						    = output.prev_sample

									snake_case   :			str						    = torch.sum(torch.abs(A_		)		)
									snake_case   :			Dict						    = torch.mean(torch.abs(A_		)		)

									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() - 4.693_4286_5017_0972E-07		) < 1E-2
															assert abs(result_mean.item() - 0.0002		) < 1E-3



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:




									if torch_device == "mps":
															return
									snake_case   :			Optional[Any]						    = self.scheduler_classes[0]
									snake_case   :			str						    = self.get_scheduler_config()
									snake_case   :			Optional[int]						    = scheduler_class(**A_		)

									scheduler.set_timesteps(self.num_inference_steps		)

									snake_case   :			Optional[int]						    = self.dummy_model()
									snake_case   :			Tuple						    = self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case   :			List[str]						    = sample.to(A_		)

									for i, t in enumerate(scheduler.timesteps		):
															snake_case   :			Optional[int]						    = scheduler.scale_model_input(A_   ,	A_		)

															snake_case   :			List[str]						    = model(A_   ,	A_		)

															snake_case   :			Tuple						    = scheduler.step(A_   ,	A_   ,	A_		)
															snake_case   :			Any						    = output.prev_sample

									snake_case   :			Tuple						    = torch.sum(torch.abs(A_		)		)
									snake_case   :			Any						    = 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 				__lowercase					(			self							:				Union[str, Any]		)							->					Union[str, Any]:




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

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

									snake_case   :			Tuple						    = self.dummy_model()
									snake_case   :			Any						    = self.dummy_sample_deter.to(A_		) * scheduler.init_noise_sigma

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

															snake_case   :			Dict						    = model(A_   ,	A_		)

															snake_case   :			Union[str, Any]						    = scheduler.step(A_   ,	A_   ,	A_		)
															snake_case   :			Tuple						    = output.prev_sample

									snake_case   :			List[Any]						    = torch.sum(torch.abs(A_		)		)
									snake_case   :			List[str]						    = 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





"""simple docstring"""




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A__ :       Optional[int]				= [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
  A__ :       Optional[int]				= parser.parse_args()
  if args.check_lib:
    A__ :       Union[str, Any]				= importlib.import_module('transformers')
    A__ :       Optional[Any]				= Path(transformers_module.__file__).parent
  else:
    A__ :       str				= Path.cwd() / 'build/lib/transformers'
  if not test_custom_files_are_present(transformers_path):
    raise ValueError('The built release does not contain the custom files. Fix this before going further!')
