

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	:     str		       = 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_flax_available, is_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Any		       = [
					    """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LongT5EncoderModel""",
					    """LongT5ForConditionalGeneration""",
					    """LongT5Model""",
					    """LongT5PreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Optional[int]		       = [
					    """FlaxLongT5ForConditionalGeneration""",
					    """FlaxLongT5Model""",
					    """FlaxLongT5PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longta import (
										    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongTaEncoderModel,
										    LongTaForConditionalGeneration,
										    LongTaModel,
										    LongTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_longta import (
										    FlaxLongTaForConditionalGeneration,
										    FlaxLongTaModel,
										    FlaxLongTaPreTrainedModel,
										)


else:
					import sys

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




