

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a_    :						Optional[Any]    		=      logging.get_logger(__name__)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="linear"
    __UpperCamelCase         ="cosine"
    __UpperCamelCase         ="cosine_with_restarts"
    __UpperCamelCase         ="polynomial"
    __UpperCamelCase         ="constant"
    __UpperCamelCase         ="constant_with_warmup"
    __UpperCamelCase         ="piecewise_constant"


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optimizer		,							_UpperCamelCase		:  int = -1	)		->							int:
 '''simple docstring'''


 return LambdaLR(_UpperCamelCase		,							lambda _UpperCamelCase	: 1		,							last_epoch=_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optimizer		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int = -1	)		->							Optional[Any]:
 '''simple docstring'''


 def lr_lambda(_UpperCamelCase		:  int	):
  if current_step < num_warmup_steps:
   return float(_UpperCamelCase	) / float(max(1.0		,							_UpperCamelCase	)	)
  return 1.0

 return LambdaLR(_UpperCamelCase		,							_UpperCamelCase		,							last_epoch=_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optimizer		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  int = -1	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{}
 SCREAMING_SNAKE_CASE								=			step_rules.split(','	)
 for rule_str in rule_list[:-1]:
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			rule_str.split(':'	)
  SCREAMING_SNAKE_CASE								=			int(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			float(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			value
 SCREAMING_SNAKE_CASE								=			float(rule_list[-1]	)

 def create_rules_function(_UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Union[str, Any]	):
  def rule_func(_UpperCamelCase		:  int	) -> float:
   SCREAMING_SNAKE_CASE								=			sorted(rules_dict.keys()	)
   for i, sorted_step in enumerate(_UpperCamelCase	):
    if steps < sorted_step:
     return rules_dict[sorted_steps[i]]
   return last_lr_multiple

  return rule_func

 SCREAMING_SNAKE_CASE								=			create_rules_function(_UpperCamelCase		,							_UpperCamelCase	)

 return LambdaLR(_UpperCamelCase		,							_UpperCamelCase		,							last_epoch=_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  List[Any]=-1	)		->							List[str]:
 '''simple docstring'''


 def lr_lambda(_UpperCamelCase		:  int	):
  if current_step < num_warmup_steps:
   return float(_UpperCamelCase	) / float(max(1		,							_UpperCamelCase	)	)
  return max(
      0.0		,							float(num_training_steps - current_step	) / float(max(1		,							num_training_steps - num_warmup_steps	)	)	)

 return LambdaLR(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optimizer		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  float = 0.5		,							_UpperCamelCase		:  int = -1	)		->							Dict:
 '''simple docstring'''


 def lr_lambda(_UpperCamelCase		:  int	):
  if current_step < num_warmup_steps:
   return float(_UpperCamelCase	) / float(max(1		,							_UpperCamelCase	)	)
  SCREAMING_SNAKE_CASE								=			float(current_step - num_warmup_steps	) / float(max(1		,							num_training_steps - num_warmup_steps	)	)
  return max(0.0		,							0.5 * (1.0 + math.cos(math.pi * float(_UpperCamelCase	) * 2.0 * progress	))	)

 return LambdaLR(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optimizer		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int = 1		,							_UpperCamelCase		:  int = -1	)		->							Optional[int]:
 '''simple docstring'''


 def lr_lambda(_UpperCamelCase		:  Union[str, Any]	):
  if current_step < num_warmup_steps:
   return float(_UpperCamelCase	) / float(max(1		,							_UpperCamelCase	)	)
  SCREAMING_SNAKE_CASE								=			float(current_step - num_warmup_steps	) / float(max(1		,							num_training_steps - num_warmup_steps	)	)
  if progress >= 1.0:
   return 0.0
  return max(0.0		,							0.5 * (1.0 + math.cos(math.pi * ((float(_UpperCamelCase	) * progress) % 1.0)	))	)

 return LambdaLR(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Union[str, Any]=1e-7		,							_UpperCamelCase		:  Dict=1.0		,							_UpperCamelCase		:  Tuple=-1	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			optimizer.defaults['lr']
 if not (lr_init > lr_end):
  raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""	)

 def lr_lambda(_UpperCamelCase		:  int	):
  if current_step < num_warmup_steps:
   return float(_UpperCamelCase	) / float(max(1		,							_UpperCamelCase	)	)
  elif current_step > num_training_steps:
   return lr_end / lr_init  # as LambdaLR multiplies by lr_init
  else:
   SCREAMING_SNAKE_CASE								=			lr_init - lr_end
   SCREAMING_SNAKE_CASE								=			num_training_steps - num_warmup_steps
   SCREAMING_SNAKE_CASE								=			1 - (current_step - num_warmup_steps) / decay_steps
   SCREAMING_SNAKE_CASE								=			lr_range * pct_remaining**power + lr_end
   return decay / lr_init  # as LambdaLR multiplies by lr_init

 return LambdaLR(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)


a_    :						List[Any]    		=      {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, SchedulerType]		,							_UpperCamelCase		:  Optimizer		,							_UpperCamelCase		:  Optional[str] = None		,							_UpperCamelCase		:  Optional[int] = None		,							_UpperCamelCase		:  Optional[int] = None		,							_UpperCamelCase		:  int = 1		,							_UpperCamelCase		:  float = 1.0		,							_UpperCamelCase		:  int = -1		,							)		->							Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			SchedulerType(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			TYPE_TO_SCHEDULER_FUNCTION[name]
 if name == SchedulerType.CONSTANT:
  return schedule_func(_UpperCamelCase		,							last_epoch=_UpperCamelCase	)

 if name == SchedulerType.PIECEWISE_CONSTANT:
  return schedule_func(_UpperCamelCase		,							step_rules=_UpperCamelCase		,							last_epoch=_UpperCamelCase	)

 # All other schedulers require `num_warmup_steps`
 if num_warmup_steps is None:
  raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""	)

 if name == SchedulerType.CONSTANT_WITH_WARMUP:
  return schedule_func(_UpperCamelCase		,							num_warmup_steps=_UpperCamelCase		,							last_epoch=_UpperCamelCase	)

 # All other schedulers require `num_training_steps`
 if num_training_steps is None:
  raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""	)

 if name == SchedulerType.COSINE_WITH_RESTARTS:
  return schedule_func(
      _UpperCamelCase		,							num_warmup_steps=_UpperCamelCase		,							num_training_steps=_UpperCamelCase		,							num_cycles=_UpperCamelCase		,							last_epoch=_UpperCamelCase		,							)

 if name == SchedulerType.POLYNOMIAL:
  return schedule_func(
      _UpperCamelCase		,							num_warmup_steps=_UpperCamelCase		,							num_training_steps=_UpperCamelCase		,							power=_UpperCamelCase		,							last_epoch=_UpperCamelCase		,							)

 return schedule_func(
     _UpperCamelCase		,							num_warmup_steps=_UpperCamelCase		,							num_training_steps=_UpperCamelCase		,							last_epoch=_UpperCamelCase	)




#
# 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

