

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					A__(a_       ):
						"""simple docstring"""






						_A						:							Any									= (EulerAncestralDiscreteScheduler,)
						_A						:							str									= 10





						def  UpperCamelCase__				(						self     ,			**_lowercase						)    ->   Tuple:
									a_	:	Optional[Any]          =   {
									    """num_train_timesteps""": 1_100,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									}

									config.update(**_lowercase						)
									return config





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





						def  UpperCamelCase__				(						self						)    ->   List[str]:
									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=_lowercase     ,			beta_end=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Tuple:
									for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   str:
									a_	:	int          =   self.scheduler_classes[0]
									a_	:	int          =   self.get_scheduler_config()
									a_	:	int          =   scheduler_class(**_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps						)

									a_	:	List[Any]          =   torch.manual_seed(0						)

									a_	:	List[Any]          =   self.dummy_model()
									a_	:	List[Any]          =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									a_	:	Union[str, Any]          =   sample.to(_lowercase						)

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

												a_	:	List[str]          =   model(_lowercase     ,			_lowercase						)

												a_	:	Tuple          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase     ,			generator=_lowercase						)
												a_	:	Optional[Any]          =   output.prev_sample

									a_	:	List[str]          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	str          =   torch.mean(torch.abs(_lowercase						)						)

									assert abs(result_sum.item() - 152.3_192						) < 1e-2
									assert abs(result_mean.item() - 0.1_9_8_3						) < 1e-3





						def  UpperCamelCase__				(						self						)    ->   Tuple:
									a_	:	List[str]          =   self.scheduler_classes[0]
									a_	:	Optional[int]          =   self.get_scheduler_config(prediction_type="""v_prediction"""						)
									a_	:	Any          =   scheduler_class(**_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps						)

									a_	:	Optional[Any]          =   torch.manual_seed(0						)

									a_	:	List[str]          =   self.dummy_model()
									a_	:	Union[str, Any]          =   self.dummy_sample_deter * scheduler.init_noise_sigma
									a_	:	List[str]          =   sample.to(_lowercase						)

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

												a_	:	Dict          =   model(_lowercase     ,			_lowercase						)

												a_	:	Tuple          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase     ,			generator=_lowercase						)
												a_	:	List[Any]          =   output.prev_sample

									a_	:	int          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	List[Any]          =   torch.mean(torch.abs(_lowercase						)						)

									assert abs(result_sum.item() - 108.4_439						) < 1e-2
									assert abs(result_mean.item() - 0.1_4_1_2						) < 1e-3





						def  UpperCamelCase__				(						self						)    ->   str:
									a_	:	str          =   self.scheduler_classes[0]
									a_	:	int          =   self.get_scheduler_config()
									a_	:	Optional[Any]          =   scheduler_class(**_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps     ,			device=_lowercase						)
									a_	:	Optional[Any]          =   torch.manual_seed(0						)

									a_	:	Union[str, Any]          =   self.dummy_model()
									a_	:	Union[str, Any]          =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									a_	:	int          =   sample.to(_lowercase						)

									for t in scheduler.timesteps:
												a_	:	Optional[Any]          =   scheduler.scale_model_input(_lowercase     ,			_lowercase						)

												a_	:	Tuple          =   model(_lowercase     ,			_lowercase						)

												a_	:	Union[str, Any]          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase     ,			generator=_lowercase						)
												a_	:	List[Any]          =   output.prev_sample

									a_	:	Any          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	Dict          =   torch.mean(torch.abs(_lowercase						)						)

									assert abs(result_sum.item() - 152.3_192						) < 1e-2
									assert abs(result_mean.item() - 0.1_9_8_3						) < 1e-3





import argparse
import os


__snake_case							:			List[Any]      		= {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
	__snake_case							:			List[str]      		= argparse.ArgumentParser("""Choose the framework and version to install""")
	parser.add_argument(
	    """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
	)
	parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
	__snake_case							:			int      		= parser.parse_args()

	__snake_case							:			Optional[Any]      		= past_versions_testing[args.framework][args.version]

	os.system(F"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")
	print(F"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")

	__snake_case							:			Dict      		= """"""
	if args.framework == "pytorch":
		__snake_case							:			Union[str, Any]      		= info["""cuda"""]
	os.system(F"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")
	print(F"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")


