


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,
)
snake_case__				:	str					       =					logging.getLogger(__name__)


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Dict:
   lowerCamelCase_						:   Optional[Any]		= git.Repo(search_parent_directories=A__					)
   lowerCamelCase_						:   Dict		= {
       '''repo_id''': str(A__					),
       '''repo_sha''': str(repo.head.object.hexsha					),
       '''repo_branch''': str(repo.active_branch					),
   }

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


def    __lowerCamelCase   (      A__						: List[str]					)     ->    Optional[int]:






   if params.n_gpu <= 0:
      lowerCamelCase_						:   Union[str, Any]		= 0
      lowerCamelCase_						:   Optional[Any]		= -1
      lowerCamelCase_						:   int		= True
      lowerCamelCase_						:   Union[str, Any]		= False
      return

   assert torch.cuda.is_available()

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

      lowerCamelCase_						:   Any		= int(os.environ["""WORLD_SIZE"""]					)
      lowerCamelCase_						:   str		= int(os.environ["""N_GPU_NODE"""]					)
      lowerCamelCase_						:   List[str]		= int(os.environ["""RANK"""]					)

      # number of nodes / node ID
      lowerCamelCase_						:   Optional[Any]		= params.world_size // params.n_gpu_per_node
      lowerCamelCase_						:   Optional[int]		= params.global_rank // params.n_gpu_per_node
      lowerCamelCase_						:   str		= 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

      lowerCamelCase_						:   str		= 1
      lowerCamelCase_						:   str		= 0
      lowerCamelCase_						:   Dict		= 0
      lowerCamelCase_						:   Tuple		= 0
      lowerCamelCase_						:   int		= 1
      lowerCamelCase_						:   int		= 1
      lowerCamelCase_						:   Dict		= 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
   lowerCamelCase_						:   List[Any]		= params.node_id == 0 and params.local_rank == 0
   lowerCamelCase_						:   Tuple		= params.n_nodes > 1

   # summary
   lowerCamelCase_						:   Optional[int]		= 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   (      A__						: List[str]					)     ->    Union[str, Any]:
   np.random.seed(args.seed					)
   torch.manual_seed(args.seed					)
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed					)













SCREAMING_SNAKE_CASE__   :      Tuple				=		{
    """a""": """AAAAA""",
    """b""": """AAAAB""",
    """c""": """AAABA""",
    """d""": """AAABB""",
    """e""": """AABAA""",
    """f""": """AABAB""",
    """g""": """AABBA""",
    """h""": """AABBB""",
    """i""": """ABAAA""",
    """j""": """BBBAA""",
    """k""": """ABAAB""",
    """l""": """ABABA""",
    """m""": """ABABB""",
    """n""": """ABBAA""",
    """o""": """ABBAB""",
    """p""": """ABBBA""",
    """q""": """ABBBB""",
    """r""": """BAAAA""",
    """s""": """BAAAB""",
    """t""": """BAABA""",
    """u""": """BAABB""",
    """v""": """BBBAB""",
    """w""": """BABAA""",
    """x""": """BABAB""",
    """y""": """BABBA""",
    """z""": """BABBB""",
    """ """: """ """,
}


SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		{value: key for key, value in encode_dict.items()}

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   ''''''
				for letter in word.lower():
								if letter.isalpha() or letter == " ":
												encoded += encode_dict[letter]
								else:
												raise Exception('''encode() accepts only letters of the alphabet and spaces'''			)
				return encoded

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if set(snake_case			) - {"A", "B", " "} != set():
								raise Exception('''decode() accepts only \'A\', \'B\' and spaces'''			)
				__magic_name__		:Dict   =   ''''''
				for word in coded.split():
								while len(snake_case			) != 0:
												decoded += decode_dict[word[:5]]
												__magic_name__		:int   =   word[5:]
								decoded += " "
				return decoded.strip()


if __name__ == "__main__":
					from doctest import testmod

					testmod()






