


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,
)
snake_case__      			=    logging.getLogger(__name__)


def 					lowerCamelCase__						(   a						: str					)     ->						List[str]:





			"""simple docstring"""
			a__			:Optional[Any]    	=     git.Repo(search_parent_directories=__lowercase					)
			a__			:List[str]    	=     {
			    "repo_id": str(__lowercase					),
			    "repo_sha": str(repo.head.object.hexsha					),
			    "repo_branch": str(repo.active_branch					),
			}

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


def 					lowerCamelCase__						(   a						: str					)     ->						List[Any]:





			"""simple docstring"""

			if params.n_gpu <= 0:
						a__			:Tuple    	=     0
						a__			:Union[str, Any]    	=     -1
						a__			:Optional[Any]    	=     True
						a__			:Optional[int]    	=     False
						return

			assert torch.cuda.is_available()

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

						a__			:List[Any]    	=     int(os.environ["WORLD_SIZE"]					)
						a__			:List[Any]    	=     int(os.environ["N_GPU_NODE"]					)
						a__			:Dict    	=     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__			: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__			:Optional[Any]    	=     1
						a__			:Optional[int]    	=     0
						a__			:List[Any]    	=     0
						a__			:Union[str, Any]    	=     0
						a__			:Tuple    	=     1
						a__			:Union[str, 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__			:int    	=     params.node_id == 0 and params.local_rank == 0
			a__			:Optional[Any]    	=     params.n_nodes > 1

			# summary
			a__			: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 					lowerCamelCase__						(   a						: str					)     ->						int:





			"""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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a   :							Optional[int]						= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Tuple						= ["XLNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							List[str]						= ["XLNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Any						= [
      "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XLNetForMultipleChoice",
      "XLNetForQuestionAnswering",
      "XLNetForQuestionAnsweringSimple",
      "XLNetForSequenceClassification",
      "XLNetForTokenClassification",
      "XLNetLMHeadModel",
      "XLNetModel",
      "XLNetPreTrainedModel",
      "load_tf_weights_in_xlnet",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFXLNetForMultipleChoice",
      "TFXLNetForQuestionAnsweringSimple",
      "TFXLNetForSequenceClassification",
      "TFXLNetForTokenClassification",
      "TFXLNetLMHeadModel",
      "TFXLNetMainLayer",
      "TFXLNetModel",
      "TFXLNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




