






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,
)
lowerCamelCase__							=     logging.getLogger(__name__)






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						git.Repo(search_parent_directories=snake_case__  )
     __snake_case			:str          =						{
         """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   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






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

     assert torch.cuda.is_available()

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

          __snake_case			:Union[str, Any]          =						int(os.environ["""WORLD_SIZE"""]  )
          __snake_case			:Dict          =						int(os.environ["""N_GPU_NODE"""]  )
          __snake_case			:Optional[int]          =						int(os.environ["""RANK"""]  )

          # number of nodes / node ID
          __snake_case			:Tuple          =						params.world_size // params.n_gpu_per_node
          __snake_case			:int          =						params.global_rank // params.n_gpu_per_node
          __snake_case			:int          =						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			:str          =						1
          __snake_case			:Optional[Any]          =						0
          __snake_case			:str          =						0
          __snake_case			:Tuple          =						0
          __snake_case			:List[Any]          =						1
          __snake_case			:Any          =						1
          __snake_case			: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
     __snake_case			:Optional[int]          =						params.node_id == 0 and params.local_rank == 0
     __snake_case			:str          =						params.n_nodes > 1

     # summary
     __snake_case			:Optional[Any]          =						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   UpperCamelCase  (       snake_case__     :   Optional[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_tokenizers_available, is_torch_available


lowerCamelCase__							=     {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CanineForMultipleChoice""",
         """CanineForQuestionAnswering""",
         """CanineForSequenceClassification""",
         """CanineForTokenClassification""",
         """CanineLayer""",
         """CanineModel""",
         """CaninePreTrainedModel""",
         """load_tf_weights_in_canine""",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

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


