





'''simple docstring'''




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,
)
__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.getLogger(__name__)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       Tuple:


     '''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 	UpperCAmelCase_							(					__lowercase							:      Tuple       )		->       Tuple:


     '''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 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]       )		->       str:


     '''simple docstring'''



     np.random.seed(args.seed       )
     torch.manual_seed(args.seed       )
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed       )











'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




