from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						=		logging.get_logger(__name__)

_UpperCAmelCase						=		{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class UpperCAmelCase						(					__a					,       __a  ):


				'''simple docstring'''






				lowerCamelCase_    =					'''bit'''
				lowerCamelCase_    =					['''preactivation''', '''bottleneck''']
				lowerCamelCase_    =					['''SAME''', '''VALID''']
				def __init__(						self		,	lowercase=3		,	lowercase=6_4		,	lowercase=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]		,	lowercase=[3, 4, 6, 3]		,	lowercase="preactivation"		,	lowercase="relu"		,	lowercase=None		,	lowercase=3_2		,	lowercase=0.0		,	lowercase=False		,	lowercase=3_2		,	lowercase=1		,	lowercase=None		,	lowercase=None		,	**lowercase		,	):



						"""simple docstring"""





						super().__init__(**_UpperCamelCase					)
						if layer_type not in self.layer_types:
								raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types					)}'''					)
						if global_padding is not None:
								if global_padding.upper() in self.supported_padding:
										A_		:				Union[str, Any]     =      global_padding.upper()
								else:
										raise ValueError(F'''Padding strategy {global_padding} not supported'''					)
						A_		:				List[str]     =      num_channels
						A_		:				List[Any]     =      embedding_size
						A_		:				int     =      hidden_sizes
						A_		:				Dict     =      depths
						A_		:				int     =      layer_type
						A_		:				Dict     =      hidden_act
						A_		:				str     =      global_padding
						A_		:				Optional[Any]     =      num_groups
						A_		:				Tuple     =      drop_path_rate
						A_		:				Optional[int]     =      embedding_dynamic_padding
						A_		:				Optional[int]     =      output_stride
						A_		:				Dict     =      width_factor

						A_		:				Optional[int]     =      ['stem'] + [F'''stage{idx}''' for idx in range(1		,	len(_UpperCamelCase					) + 1					)]
						A_   ,			A_		:				List[str]     =      get_aligned_output_features_output_indices(
						    out_features=_UpperCamelCase		,	out_indices=_UpperCamelCase		,	stage_names=self.stage_names					)








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    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  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(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(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(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									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(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


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



