
"""simple docstring"""


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE      =     logging.getLogger(__name__)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		git.Repo(search_parent_directories=UpperCAmelCase_				)
       UpperCamelCase					 =		{
           "repo_id": str(UpperCAmelCase_				),
           "repo_sha": str(repo.head.object.hexsha				),
           "repo_branch": str(repo.active_branch				),
       }

       with open(os.path.join(UpperCAmelCase_		,      "git_log.json"				)		,      "w"				) as f:
              json.dump(UpperCAmelCase_		,      UpperCAmelCase_		,      indent=4				)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Optional[int]:







       """simple docstring"""
       if params.n_gpu <= 0:
              UpperCamelCase					 =		0
              UpperCamelCase					 =		-1
              UpperCamelCase					 =		True
              UpperCamelCase					 =		False
              return

       assert torch.cuda.is_available()

       logger.info("Initializing GPUs"				)
       if params.n_gpu > 1:
              assert params.local_rank != -1

              UpperCamelCase					 =		int(os.environ["WORLD_SIZE"]				)
              UpperCamelCase					 =		int(os.environ["N_GPU_NODE"]				)
              UpperCamelCase					 =		int(os.environ["RANK"]				)

              # number of nodes / node ID
              UpperCamelCase					 =		params.world_size // params.n_gpu_per_node
              UpperCamelCase					 =		params.global_rank // params.n_gpu_per_node
              UpperCamelCase					 =		True

              assert params.n_nodes == int(os.environ["N_NODES"]				)
              assert params.node_id == int(os.environ["NODE_RANK"]				)

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              UpperCamelCase					 =		1
              UpperCamelCase					 =		0
              UpperCamelCase					 =		0
              UpperCamelCase					 =		0
              UpperCamelCase					 =		1
              UpperCamelCase					 =		1
              UpperCamelCase					 =		False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       UpperCamelCase					 =		params.node_id == 0 and params.local_rank == 0
       UpperCamelCase					 =		params.n_nodes > 1

       # summary
       UpperCamelCase					 =		F"--- Global rank: {params.global_rank} - "
       logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes				)
       logger.info(PREFIX + "Node ID        : %i" % params.node_id				)
       logger.info(PREFIX + "Local rank     : %i" % params.local_rank				)
       logger.info(PREFIX + "World size     : %i" % params.world_size				)
       logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node				)
       logger.info(PREFIX + "Master         : %s" % str(params.is_master				)				)
       logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node				)				)
       logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu				)				)
       logger.info(PREFIX + "Hostname       : %s" % socket.gethostname()				)

       # set GPU device
       torch.cuda.set_device(params.local_rank				)

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("Initializing PyTorch distributed"				)
              torch.distributed.init_process_group(
                  init_method="env://"		,      backend="nccl"		,      )



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Tuple:







       """simple docstring"""
       np.random.seed(args.seed				)
       torch.manual_seed(args.seed				)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed				)







"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		inspect.getfile(accelerate.test_utils			)
              UpperCamelCase					 =		os.path.sep.join(
                  mod_file.split(os.path.sep			)[:-1] + ["scripts", "external_deps", "test_metrics.py"]			)

              from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

              UpperCamelCase					 =		test_metrics






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main       ,      num_processes=1			)






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main			)






       @require_single_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              self.test_metrics.main()






       @require_multi_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              print(f"Found {torch.cuda.device_count()} devices."			)
              UpperCamelCase					 =		["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
              with patch_environment(omp_num_threads=1			):
                     execute_subprocess_async(UpperCAmelCase_       ,      env=os.environ.copy()			)





