




from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__lowerCAmelCase	:       List[str]     =	TypeVar('KEY')
__lowerCAmelCase	:       str     =	TypeVar('VAL')




@dataclass(frozen=_A					, slots=_A     )
class 					snake_case__							(Generic[KEY, VAL]     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[Any]											=      42
				SCREAMING_SNAKE_CASE_ :							Dict											=      42




class 					snake_case__							(_Item     ):
				"""simple docstring"""



				def __init__(   self       :  List[str]							)      ->  None:
							super().__init__(UpperCamelCase__      ,   UpperCamelCase__							)





				def __bool__(   self       :  Union[str, Any]							)      ->  bool:
							return False

__lowerCAmelCase	:       str     =	_DeletedItem()




class 					snake_case__							(MutableMapping[KEY, VAL]     ):
				"""simple docstring"""



				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  int = 8      ,   __lowerCamelCase       :  float = 0.75							)      ->  None:
							a					=    initial_block_size
							a					=    [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							a					=    capacity_factor
							a					=    0





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  KEY							)      ->  int:
							return hash(UpperCamelCase__							) % len(self._buckets							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  int							)      ->  int:
							return (ind + 1) % len(self._buckets							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  KEY      ,   __lowerCamelCase       :  VAL							)      ->  bool:
							a					=    self._buckets[ind]
							if not stored:
										a					=    _Item(UpperCamelCase__      ,   UpperCamelCase__							)
										self._len += 1
										return True
							elif stored.key == key:
										a					=    _Item(UpperCamelCase__      ,   UpperCamelCase__							)
										return True
							else:
										return False





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  bool:
							a					=    len(self._buckets							) * self._capacity_factor
							return len(self							) >= int(UpperCamelCase__							)





				def 							__UpperCAmelCase							(   self       :  int							)      ->  bool:






							if len(self._buckets							) <= self._initial_block_size:
										return False
							a					=    len(self._buckets							) * self._capacity_factor / 2
							return len(self							) < limit





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  int							)      ->  None:
							a					=    self._buckets
							a					=    [None] * new_size
							a					=    0
							for item in old_buckets:
										if item:
													self._add_item(item.key      ,   item.val							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  None:
							self._resize(len(self._buckets							) * 2							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  None:
							self._resize(len(self._buckets							) // 2							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  KEY							)      ->  Iterator[int]:
							a					=    self._get_bucket_index(UpperCamelCase__							)
							for _ in range(len(self._buckets							)							):
										yield ind
										a					=    self._get_next_ind(UpperCamelCase__							)





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  KEY      ,   __lowerCamelCase       :  VAL							)      ->  None:






							for ind in self._iterate_buckets(UpperCamelCase__							):
										if self._try_set(UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__							):
													break





				def __setitem__(   self       :  str      ,   __lowerCamelCase       :  KEY      ,   __lowerCamelCase       :  VAL							)      ->  None:






							if self._is_full():
										self._size_up()

							self._add_item(UpperCamelCase__      ,   UpperCamelCase__							)





				def __delitem__(   self       :  int      ,   __lowerCamelCase       :  KEY							)      ->  None:






							for ind in self._iterate_buckets(UpperCamelCase__							):
										a					=    self._buckets[ind]
										if item is None:
													raise KeyError(UpperCamelCase__							)
										if item is _deleted:
													continue
										if item.key == key:
													a					=    _deleted
													self._len -= 1
													break
							if self._is_sparse():
										self._size_down()





				def __getitem__(   self       :  Tuple      ,   __lowerCamelCase       :  KEY							)      ->  VAL:






							for ind in self._iterate_buckets(UpperCamelCase__							):
										a					=    self._buckets[ind]
										if item is None:
													break
										if item is _deleted:
													continue
										if item.key == key:
													return item.val
							raise KeyError(UpperCamelCase__							)





				def __len__(   self       :  Optional[int]							)      ->  int:
							return self._len





				def __iter__(   self       :  int							)      ->  Iterator[KEY]:
							yield from (item.key for item in self._buckets if item)





				def __repr__(   self       :  str							)      ->  str:
							a					=    " ,".join(
							    f"""{item.key}: {item.val}""" for item in self._buckets if item							)
							return f"""HashMap({val_string})"""












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


__lowerCAmelCase	:       List[Any]     =	logging.get_logger(__name__)


__lowerCAmelCase	:       List[Any]     =	TypeVar('DatasetType', Dataset, IterableDataset)




def     __magic_name__       (				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:
									a					,	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     __magic_name__       (				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:
									a					,	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     )






