

import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu



class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		inspect.getfile(accelerate.test_utils    )
      snake_case							:		Optional[int]        =		os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_script.py"""]    )
      snake_case							:		Union[str, Any]        =		os.path.sep.join(inspect.getfile(self.__class__    ).split(os.path.sep    )[:-1]    )





  @require_tpu
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		F"""
            {self.test_dir}/xla_spawn.py
            --num_cores 8
            {self.test_file_path}
        """.split()
      snake_case							:		Dict        =		[sys.executable] + distributed_args
      execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()    )








"""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


a_							=    logging.get_logger(__name__)


a_							=    TypeVar("""DatasetType""", Dataset, IterableDataset)

def     UpperCAmelCase_					(				__a   :			List[DatasetType]      ,							__a   :			Optional[List[float]] = None      ,							__a   :			Optional[int] = None      ,							__a   :			Optional[DatasetInfo] = None      ,							__a   :			Optional[NamedSplit] = None      ,							__a   :			Literal["first_exhausted", "all_exhausted"] = "first_exhausted"      ,							):




			'''simple docstring'''



			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:
									_lowerCamelCase       ,							_lowerCamelCase					:					Tuple	       =    (
									    (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     UpperCAmelCase_					(				__a   :			List[DatasetType]      ,							__a   :			Optional[DatasetInfo] = None      ,							__a   :			Optional[NamedSplit] = None      ,							__a   :			int = 0      ,							):




			'''simple docstring'''



			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:
									_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    (
									    (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						)





