




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:     Tuple									= logging.getLogger(__name__)


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

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


def 					_a							(					lowerCAmelCase )->	Optional[int]:
		if params.n_gpu <= 0:
				SCREAMING_SNAKE_CASE_					      =							0
				SCREAMING_SNAKE_CASE_					      =							-1
				SCREAMING_SNAKE_CASE_					      =							True
				SCREAMING_SNAKE_CASE_					      =							False
				return

		assert torch.cuda.is_available()

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

				SCREAMING_SNAKE_CASE_					      =							int(os.environ['WORLD_SIZE'] )
				SCREAMING_SNAKE_CASE_					      =							int(os.environ['N_GPU_NODE'] )
				SCREAMING_SNAKE_CASE_					      =							int(os.environ['RANK'] )

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

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

		# summary
		SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase )->	Dict:
		np.random.seed(args.seed )
		torch.manual_seed(args.seed )
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE:     Any									= {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     List[Any]									= [
						    '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''PegasusXForConditionalGeneration''',
						    '''PegasusXModel''',
						    '''PegasusXPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus_x import (
												    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusXForConditionalGeneration,
												    PegasusXModel,
												    PegasusXPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     List[Any]									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)