

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "vit_msn"


							def __init__(     self			,				_a=7_6_8			,				_a=1_2			,				_a=1_2			,				_a=3_0_7_2			,				_a="gelu"			,				_a=0.0			,				_a=0.0			,				_a=0.02			,				_a=1e-0_6			,				_a=2_2_4			,				_a=1_6			,				_a=3			,				_a=True			,				**_a			,				)       ->       int:
												super().__init__(**_a			)

												_a      :       List[str]   =						hidden_size
												_a      :       int   =						num_hidden_layers
												_a      :       Tuple   =						num_attention_heads
												_a      :       Optional[Any]   =						intermediate_size
												_a      :       List[str]   =						hidden_act
												_a      :       int   =						hidden_dropout_prob
												_a      :       List[str]   =						attention_probs_dropout_prob
												_a      :       Optional[Any]   =						initializer_range
												_a      :       List[str]   =						layer_norm_eps
												_a      :       Optional[int]   =						image_size
												_a      :       List[Any]   =						patch_size
												_a      :       Optional[Any]   =						num_channels
												_a      :       str   =						qkv_bias








"""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						)





