






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:     Any   					=					logging.getLogger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       _UpperCAmelCase							=    git.Repo(search_parent_directories=__snake_case )
       _UpperCAmelCase							=    {
           """repo_id""": str(__snake_case ),
           """repo_sha""": str(repo.head.object.hexsha ),
           """repo_branch""": str(repo.active_branch ),
       }

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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       if params.n_gpu <= 0:
              _UpperCAmelCase							=    0
              _UpperCAmelCase							=    -1
              _UpperCAmelCase							=    True
              _UpperCAmelCase							=    False
              return

       assert torch.cuda.is_available()

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

              _UpperCAmelCase							=    int(os.environ["""WORLD_SIZE"""] )
              _UpperCAmelCase							=    int(os.environ["""N_GPU_NODE"""] )
              _UpperCAmelCase							=    int(os.environ["""RANK"""] )

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

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

       # summary
       _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						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_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





