
"""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


lowerCamelCase__           =	logging.get_logger(__name__)


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

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

"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'beit'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=81_92 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=2_24 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[3, 5, 7, 11] ,	_SCREAMING_SNAKE_CASE=[1, 2, 3, 6] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.4 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_55 ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   vocab_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		Dict		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_act
			__lowerCAmelCase    :		List[Any]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   initializer_range
			__lowerCAmelCase    :		Optional[Any]		    =   layer_norm_eps

			__lowerCAmelCase    :		Any		    =   image_size
			__lowerCAmelCase    :		Union[str, Any]		    =   patch_size
			__lowerCAmelCase    :		List[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   use_mask_token
			__lowerCAmelCase    :		Any		    =   use_absolute_position_embeddings
			__lowerCAmelCase    :		int		    =   use_relative_position_bias
			__lowerCAmelCase    :		List[str]		    =   use_shared_relative_position_bias
			__lowerCAmelCase    :		Tuple		    =   layer_scale_init_value
			__lowerCAmelCase    :		int		    =   drop_path_rate
			__lowerCAmelCase    :		Dict		    =   use_mean_pooling
			# decode head attributes (semantic segmentation)
			__lowerCAmelCase    :		Optional[Any]		    =   out_indices
			__lowerCAmelCase    :		Optional[Any]		    =   pool_scales
			# auxiliary head attributes (semantic segmentation)
			__lowerCAmelCase    :		Union[str, Any]		    =   use_auxiliary_head
			__lowerCAmelCase    :		Optional[int]		    =   auxiliary_loss_weight
			__lowerCAmelCase    :		Optional[Any]		    =   auxiliary_channels
			__lowerCAmelCase    :		List[str]		    =   auxiliary_num_convs
			__lowerCAmelCase    :		List[str]		    =   auxiliary_concat_input
			__lowerCAmelCase    :		Optional[int]		    =   semantic_loss_ignore_index




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]				)







		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-4