




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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						git.Repo(search_parent_directories=lowercase_      )
    __UpperCamelCase  		=						{
        '''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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    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   (  lowercase_      )       ->	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 pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Any    =					""
      lowerCAmelCase__			: Any    =					"hf-legacy"  # "hf://"" is reserved for hffs







      def __init__(     self					: str ,       snake_case					: Optional[DatasetInfo] = None ,       snake_case					: Optional[str] = None ,       **snake_case					: List[Any] ,       ):
          super().__init__(self ,       **snake_case				)
          __UpperCamelCase  		=						repo_info
          __UpperCamelCase  		=						token
          __UpperCamelCase  		=						None







      def       snake_case	(     self					: List[Any]				):
          if self.dir_cache is None:
              __UpperCamelCase  		=						{}
              for hf_file in self.repo_info.siblings:
                  # TODO(QL): add sizes
                  __UpperCamelCase  		=						{
                      '''name''': hf_file.rfilename,
                      '''size''': None,
                      '''type''': '''file''',
                  }
                  self.dir_cache.update(
                      {
                          str(snake_case				): {'''name''': str(snake_case				), '''size''': None, '''type''': '''directory'''}
                          for d in list(PurePosixPath(hf_file.rfilename				).parents				)[:-1]
                      }				)







      def       snake_case	(     self					: Dict ,       snake_case					: str ,       snake_case					: str = "rb" ,       **snake_case					: Union[str, Any] ,       ):
          if not isinstance(self.repo_info ,       snake_case				):
              raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"				)
          __UpperCamelCase  		=						hf_hub_url(self.repo_info.id ,       snake_case ,       revision=self.repo_info.sha				)
          return fsspec.open(
              snake_case ,       mode=snake_case ,       headers=get_authentication_headers_for_url(snake_case ,       use_auth_token=self.token				) ,       client_kwargs={'''trust_env''': True} ,       ).open()







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: Tuple ,       **snake_case					: List[Any]				):
          self._get_dirs()
          __UpperCamelCase  		=						self._strip_protocol(snake_case				)
          if path in self.dir_cache:
              return self.dir_cache[path]
          else:
              raise FileNotFoundError(snake_case				)







      def       snake_case	(     self					: List[str] ,       snake_case					: int ,       snake_case					: Tuple=False ,       **snake_case					: Dict				):
          self._get_dirs()
          __UpperCamelCase  		=						PurePosixPath(path.strip('''/'''				)				)
          __UpperCamelCase  		=						{}
          for p, f in self.dir_cache.items():
              __UpperCamelCase  		=						PurePosixPath(p.strip('''/'''				)				)
              __UpperCamelCase  		=						p.parent
              if root == path:
                  __UpperCamelCase  		=						f
          __UpperCamelCase  		=						list(paths.values()				)
          if detail:
              return out
          else:
              return sorted(f['''name'''] for f in out				)






