

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =1

    @register_to_config
    def __init__(			self :      Union[str, Any]							,    snake_case__ :      Any=2_0_0_0							,    snake_case__ :      Optional[int]=0.1							,    snake_case__ :      List[Any]=2_0							,    snake_case__ :      Dict=1E-3    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      Union[str, torch.device] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.linspace(1							,    self.config.sampling_eps							,    snake_case__							,    device=snake_case__    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      str							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      List[Any]=None    ):

     """simple docstring"""






     if self.timesteps is None:
      raise ValueError(
          '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'    )

     # TODO(Patrick) better comments + non-PyTorch
     # postprocess model score
     SCREAMING_SNAKE_CASE								=			(
         -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     SCREAMING_SNAKE_CASE								=			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
     SCREAMING_SNAKE_CASE								=			std.flatten()
     while len(std.shape    ) < len(score.shape    ):
      SCREAMING_SNAKE_CASE								=			std.unsqueeze(-1    )
     SCREAMING_SNAKE_CASE								=			-score / std

     # compute
     SCREAMING_SNAKE_CASE								=			-1.0 / len(self.timesteps    )

     SCREAMING_SNAKE_CASE								=			self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
     SCREAMING_SNAKE_CASE								=			beta_t.flatten()
     while len(beta_t.shape    ) < len(x.shape    ):
      SCREAMING_SNAKE_CASE								=			beta_t.unsqueeze(-1    )
     SCREAMING_SNAKE_CASE								=			-0.5 * beta_t * x

     SCREAMING_SNAKE_CASE								=			torch.sqrt(snake_case__    )
     SCREAMING_SNAKE_CASE								=			drift - diffusion**2 * score
     SCREAMING_SNAKE_CASE								=			x + drift * dt

     # add noise
     SCREAMING_SNAKE_CASE								=			randn_tensor(x.shape							,    layout=x.layout							,    generator=snake_case__							,    device=x.device							,    dtype=x.dtype    )
     SCREAMING_SNAKE_CASE								=			x_mean + diffusion * math.sqrt(-dt    ) * noise

     return x, x_mean




    def __len__(			self :      Any    ):

     """simple docstring"""






     return self.config.num_train_timesteps




#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist


def        __lowerCAmelCase    (       *_UpperCamelCase		:  Optional[Any]	)		->							Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,							'r'	) as fh:
  fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_EX	)
  try:
   print(*_UpperCamelCase	)
  finally:
   fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_UN	)


a_    :						int    		=      int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
a_    :						str    		=      torch.device("cuda", local_rank)
a_    :						Optional[int]    		=      socket.gethostname()

a_    :						Union[str, Any]    		=      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
      a_    :						Dict    		=      dist.get_rank()
      a_    :						Any    		=      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

