'''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    :     int         =       logging.get_logger(__name__)


_UpperCAmelCase    :     Optional[int]         =       TypeVar('''DatasetType''', Dataset, IterableDataset)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			int = None   ,		__snake_case							:			List[Any] = None   ,		__snake_case							:			Tuple = None   ,		__snake_case							:			Dict = None   ,		__snake_case							:			Optional[Any] = "first_exhausted"   ,		):
     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(A_				):
          if not isinstance(A_   ,		(Dataset, IterableDataset)				):
               if isinstance(A_   ,		(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(A_				)}\n'
                        F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A_				)				)}\']'				)
               raise ValueError(
                   F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A_				).__name__}.'				)
          if i == 0:
               _A    		=  (
                   (Dataset, IterableDataset) if isinstance(A_   ,		A_				) else (IterableDataset, Dataset)
               )
          elif not isinstance(A_   ,		A_				):
               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(
              A_   ,		A_   ,		A_   ,		info=A_   ,		split=A_   ,		stopping_strategy=A_				)
     else:
          return _interleave_iterable_datasets(
              A_   ,		A_   ,		A_   ,		info=A_   ,		split=A_   ,		stopping_strategy=A_				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Tuple = None   ,		__snake_case							:			List[Any] = None   ,		__snake_case							:			Union[str, Any] = 0   ,		):

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






"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




