
"""simple docstring"""

from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCamelCase			:       Optional[Any]									=				logging.get_logger(__name__)


UpperCamelCase			:       Optional[int]									=				TypeVar("DatasetType", Dataset, IterableDataset)






def 							A		(       snake_case   :List[DatasetType]       ,      snake_case   :Optional[List[float]] = None       ,      snake_case   :Optional[int] = None       ,      snake_case   :Optional[DatasetInfo] = None       ,      snake_case   :Optional[NamedSplit] = None       ,      snake_case   :Literal["first_exhausted", "all_exhausted"] = "first_exhausted"       ,      )   ->			DatasetType:
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('Unable to interleave an empty list of datasets.'	)
 for i, dataset in enumerate(_UpperCamelCase	):
  if not isinstance(_UpperCamelCase       ,      (Dataset, IterableDataset)	):
   if isinstance(_UpperCamelCase       ,      (DatasetDict, IterableDatasetDict)	):
    if not dataset:
     raise ValueError(
         f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
         'is an empty dataset dictionary.'	)
    raise ValueError(
        f'Dataset at position {i} has at least one split: {list(_UpperCamelCase	)}\n'
        f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_UpperCamelCase	)	)}\']'	)
   raise ValueError(
       f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_UpperCamelCase	).__name__}.'	)
  if i == 0:
   __UpperCamelCase						,			__UpperCamelCase       						= (
       (Dataset, IterableDataset) if isinstance(_UpperCamelCase       ,      _UpperCamelCase	) else (IterableDataset, Dataset)
   )
  elif not isinstance(_UpperCamelCase       ,      _UpperCamelCase	):
   raise ValueError(
       f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'	)
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'	)
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase       ,      info=_UpperCamelCase       ,      split=_UpperCamelCase       ,      stopping_strategy=_UpperCamelCase	)
 else:
  return _interleave_iterable_datasets(
      _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase       ,      info=_UpperCamelCase       ,      split=_UpperCamelCase       ,      stopping_strategy=_UpperCamelCase	)






def 							A		(       snake_case   :List[DatasetType]       ,      snake_case   :Optional[DatasetInfo] = None       ,      snake_case   :Optional[NamedSplit] = None       ,      snake_case   :int = 0       ,      )   ->			DatasetType:






 if not dsets:
  raise ValueError('Unable to concatenate an empty list of datasets.'	)
 for i, dataset in enumerate(_UpperCamelCase	):
  if not isinstance(_UpperCamelCase       ,      (Dataset, IterableDataset)	):
   if isinstance(_UpperCamelCase       ,      (DatasetDict, IterableDatasetDict)	):
    if not dataset:
     raise ValueError(
         f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
         'is an empty dataset dictionary.'	)
    raise ValueError(
        f'Dataset at position {i} has at least one split: {list(_UpperCamelCase	)}\n'
        f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_UpperCamelCase	)	)}\']'	)
   raise ValueError(
       f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_UpperCamelCase	).__name__}.'	)
  if i == 0:
   __UpperCamelCase						,			__UpperCamelCase       						= (
       (Dataset, IterableDataset) if isinstance(_UpperCamelCase       ,      _UpperCamelCase	) else (IterableDataset, Dataset)
   )
  elif not isinstance(_UpperCamelCase       ,      _UpperCamelCase	):
   raise ValueError(
       f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'	)
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(_UpperCamelCase       ,      info=_UpperCamelCase       ,      split=_UpperCamelCase       ,      axis=_UpperCamelCase	)
 else:
  return _concatenate_iterable_datasets(_UpperCamelCase       ,      info=_UpperCamelCase       ,      split=_UpperCamelCase       ,      axis=_UpperCamelCase	)





"""simple docstring"""

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							A		(       )   ->			List[Any]:
 __UpperCamelCase       						= ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )	)

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores'       ,      type=snake_case       ,      default=1       ,      help='Number of TPU cores to use (1 or 8).'	)

 # positional
 parser.add_argument(
     'training_script'       ,      type=snake_case       ,      help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     )       ,      )

 # rest from the training program
 parser.add_argument('training_script_args'       ,      nargs=snake_case	)

 return parser.parse_args()






def 							A		(       )   ->			Optional[int]:
 __UpperCamelCase       						= parse_args()

 # Import training_script as a module.
 __UpperCamelCase       						= Path(args.training_script	)
 sys.path.append(str(script_fpath.parent.resolve()	)	)
 __UpperCamelCase       						= script_fpath.stem
 __UpperCamelCase       						= importlib.import_module(snake_case	)

 # Patch sys.argv
 __UpperCamelCase       						= [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores	)]

 xmp.spawn(mod._mp_fn       ,      args=()       ,      nprocs=args.num_cores	)


if __name__ == "__main__":
      main()



