

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		:       Union[str, Any]						    =	logging.getLogger(__name__)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   git.Repo(search_parent_directories=UpperCAmelCase__				)
							snake_case_          =   {
							    '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__				):







							'''simple docstring'''






							if params.n_gpu <= 0:
														snake_case_          =   0
														snake_case_          =   -1
														snake_case_          =   True
														snake_case_          =   False
														return

							assert torch.cuda.is_available()

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

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

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

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

							# summary
							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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							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


_UpperCAmelCase		:       Tuple						    =	{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       int						    =	[
					    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """XLMRobertaXLForCausalLM""",
					    """XLMRobertaXLForMaskedLM""",
					    """XLMRobertaXLForMultipleChoice""",
					    """XLMRobertaXLForQuestionAnswering""",
					    """XLMRobertaXLForSequenceClassification""",
					    """XLMRobertaXLForTokenClassification""",
					    """XLMRobertaXLModel""",
					    """XLMRobertaXLPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[Any]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)




