

import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__lowerCamelCase						    =      {
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[Any]:
       if got_ver is None or want_ver is None:
              raise ValueError(
                  F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
                  F''' reinstalling {pkg}.'''			)
       if not ops[op](version.parse(a__			),    version.parse(a__			)			):
              raise ImportError(
                  F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'''			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__ = None			)     ->     None:
       A_		   = F'''\n{hint}''' if hint is not None else """"""

       # non-versioned check
       if re.match(r"""^[\w_\-\d]+$""",    a__			):
              A_   ,     A_   ,     A_		   = requirement, None, None
       else:
              A_		   = re.findall(r"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)""",    a__			)
              if not match:
                     raise ValueError(
                         """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
                         F''' got {requirement}'''			)
              A_   ,     A_		   = match[0]
              A_		   = want_full.split(""","""			)  # there could be multiple requirements
              A_		   = {}
              for w in want_range:
                     A_		   = re.findall(r"""^([\s!=<>]{1,2})(.+)""",    a__			)
                     if not match:
                            raise ValueError(
                                """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
                                F''' but got {requirement}'''			)
                     A_   ,     A_		   = match[0]
                     A_		   = want_ver
                     if op not in ops:
                            raise ValueError(F'''{requirement}: need one of {list(ops.keys()			)}, but got {op}'''			)

    # special case
       if pkg == "python":
              A_		   = """.""".join([str(a__			) for x in sys.version_info[:3]]			)
              for op, want_ver in wanted.items():
                     _compare_versions(a__,    a__,    a__,    a__,    a__,    a__			)
              return

       # check if any version is installed
       try:
              A_		   = importlib.metadata.version(a__			)
       except importlib.metadata.PackageNotFoundError:
              raise importlib.metadata.PackageNotFoundError(
                  F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}'''			)

       # check that the right version is installed if version number or a range was provided
       if want_ver is not None:
              for op, want_ver in wanted.items():
                     _compare_versions(a__,    a__,    a__,    a__,    a__,    a__			)







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       A_		   = """Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main"""
       return require_version(a__,    a__			)



'''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,
)
__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = git.Repo(search_parent_directories=UpperCAmelCase__			)
       A_		   = {
           """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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       if params.n_gpu <= 0:
              A_		   = 0
              A_		   = -1
              A_		   = True
              A_		   = False
              return

       assert torch.cuda.is_available()

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

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

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

              A_		   = 1
              A_		   = 0
              A_		   = 0
              A_		   = 0
              A_		   = 1
              A_		   = 1
              A_		   = 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
       A_		   = params.node_id == 0 and params.local_rank == 0
       A_		   = params.n_nodes > 1

       # summary
       A_		   = 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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)
