

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,
)
UpperCAmelCase_							:					List[str]		 =   logging.getLogger(__name__)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				Tuple:
	__A :			Optional[Any]		    =			git.Repo(search_parent_directories=a__       )
	__A :			str		    =			{
	    """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 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				Any:
	if params.n_gpu <= 0:
		__A :			Dict		    =			0
		__A :			Dict		    =			-1
		__A :			Dict		    =			True
		__A :			Dict		    =			False
		return

	assert torch.cuda.is_available()

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

		__A :			int		    =			int(os.environ["""WORLD_SIZE"""]       )
		__A :			Optional[int]		    =			int(os.environ["""N_GPU_NODE"""]       )
		__A :			Optional[int]		    =			int(os.environ["""RANK"""]       )

		# number of nodes / node ID
		__A :			int		    =			params.world_size // params.n_gpu_per_node
		__A :			Dict		    =			params.global_rank // params.n_gpu_per_node
		__A :			Union[str, Any]		    =			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 :			int		    =			1
		__A :			List[Any]		    =			0
		__A :			str		    =			0
		__A :			List[str]		    =			0
		__A :			Tuple		    =			1
		__A :			Optional[Any]		    =			1
		__A :			str		    =			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 :			Tuple		    =			params.node_id == 0 and params.local_rank == 0
	__A :			str		    =			params.n_nodes > 1

	# summary
	__A :			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 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				str:
	np.random.seed(args.seed       )
	torch.manual_seed(args.seed       )
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed       )








import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


if __name__ == "__main__":
			fire.Fire(calculate_rouge_path)





