





"""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,
)
A	     =					logging.getLogger(__name__)

def       __A (     a_  :str)						->						List[Any]:
				__a	:  Tuple         =					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       __A (     a_  :Dict)						->						List[str]:
				if params.n_gpu <= 0:
								__a	:  Optional[Any]         =					0
								__a	:  Any         =					-1
								__a	:  Optional[Any]         =					True
								__a	:  Optional[Any]         =					False
								return

				assert torch.cuda.is_available()

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

								__a	:  Optional[Any]         =					int(os.environ['''WORLD_SIZE'''])
								__a	:  Any         =					int(os.environ['''N_GPU_NODE'''])
								__a	:  Optional[Any]         =					int(os.environ['''RANK'''])

								# number of nodes / node ID
								__a	:  List[Any]         =					params.world_size // params.n_gpu_per_node
								__a	:  List[Any]         =					params.global_rank // params.n_gpu_per_node
								__a	:  Tuple         =					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	:  Optional[Any]         =					1
								__a	:  Optional[int]         =					0
								__a	:  Optional[Any]         =					0
								__a	:  List[str]         =					0
								__a	:  List[Any]         =					1
								__a	:  Any         =					1
								__a	:  List[Any]         =					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	:  Any         =					params.node_id == 0 and params.local_rank == 0
				__a	:  Any         =					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       __A (     a_  :Optional[int])						->						List[Any]:
				np.random.seed(args.seed)
				torch.manual_seed(args.seed)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed)






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A	     =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					['''MvpTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MvpForCausalLM''',
		    '''MvpForConditionalGeneration''',
		    '''MvpForQuestionAnswering''',
		    '''MvpForSequenceClassification''',
		    '''MvpModel''',
		    '''MvpPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
		from .tokenization_mvp import MvpTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mvp_fast import MvpTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mvp import (
				    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MvpForCausalLM,
				    MvpForConditionalGeneration,
				    MvpForQuestionAnswering,
				    MvpForSequenceClassification,
				    MvpModel,
				    MvpPreTrainedModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)