


"""simple docstring"""


import fcntl
import os
import socket

import torch
import torch.distributed as dist




def   snake_case		(     *UpperCamelCase__       :						Any    )   ->      Tuple:
      with open(UpperCamelCase__			,				"""r"""    ) as fh:
            fcntl.flock(UpperCamelCase__			,				fcntl.LOCK_EX    )
            try:
                  print(*UpperCamelCase__    )
            finally:
                  fcntl.flock(UpperCamelCase__			,				fcntl.LOCK_UN    )


__lowerCamelCase      :Union[str, Any]	    =		int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowerCamelCase      :Tuple	    =		torch.device('cuda', local_rank)
__lowerCamelCase      :int	    =		socket.gethostname()

__lowerCamelCase      :Tuple	    =		f"""[{hostname}-{local_rank}]"""

try:
    # test distributed
    dist.init_process_group('nccl')
    dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
    dist.barrier()

    # test cuda is available and can allocate memory
    torch.cuda.is_available()
    torch.ones(1).cuda(local_rank)

    # global rank
    __lowerCamelCase      :Union[str, Any]	    =		dist.get_rank()
    __lowerCamelCase      :str	    =		dist.get_world_size()

    printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")

    dist.barrier()
    if rank == 0:
        printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
    printflock(f"""{gpu} is broken""")
    raise








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=(KDPMaDiscreteScheduler,)
     snake_case__	:					Tuple													=10

     def 		a__						(	self:						List[Any]     ,		**__a:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**__a	)
           return config

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

     def 		a__						(	self:						str	)->    int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]	):
                 self.check_over_configs(beta_start=__a     ,		beta_end=__a	)

     def 		a__						(	self:						int	)->    Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=__a	)

     def 		a__						(	self:						List[Any]	)->    List[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase					:					List[str]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCamelCase					:					List[str]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					Dict        =	self.dummy_model()
           lowerCamelCase					:					List[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					List[Any]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Optional[Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					Optional[Any]        =	output.prev_sample

           lowerCamelCase					:					List[str]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 0.00_02	) < 1e-3

     def 		a__						(	self:						Any	)->    Any:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Dict        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					int        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					List[Any]        =	self.dummy_model()
           lowerCamelCase					:					Optional[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					Optional[int]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Dict        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[Any]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					str        =	output.prev_sample

           lowerCamelCase					:					Tuple        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3







     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Any        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config()
           lowerCamelCase					:					Optional[Any]        =	scheduler_class(**__a	)

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

           lowerCamelCase					:					Union[str, Any]        =	self.dummy_model()
           lowerCamelCase					:					List[str]        =	self.dummy_sample_deter.to(__a	) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 lowerCamelCase					:					Union[str, Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					int        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					int        =	output.prev_sample

           lowerCamelCase					:					Union[str, Any]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					int        =	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() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3




