




import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__a  :		Dict		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class 				UpperCAmelCase(					lowercase_					):



	"""simple docstring"""






	a     : Optional[int]	  =					"""vit-hybrid"""

	def __init__(					self	,				lowerCamelCase=None	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.0	,				lowerCamelCase=0.0	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=224	,				lowerCamelCase=1	,				lowerCamelCase=3	,				lowerCamelCase=[1, 1024, 24, 24]	,				lowerCamelCase=True	,				**lowerCamelCase	,				) ->      Tuple:





								"""simple docstring"""


								super().__init__(**lowerCamelCase    )

								if backbone_config is None:
															logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."    )
															lowercase__  :	str        =							{
															    "global_padding": "same",
															    "layer_type": "bottleneck",
															    "depths": [3, 4, 9],
															    "out_features": ["stage3"],
															    "embedding_dynamic_padding": True,
															}

								if isinstance(lowerCamelCase	,				lowerCamelCase    ):
															if "model_type" in backbone_config:
																						lowercase__  :	Optional[int]        =							CONFIG_MAPPING[backbone_config["model_type"]]
															else:
																						logger.info(
																						    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."    )
																						lowercase__  :	Dict        =							BitConfig
															lowercase__  :	Optional[Any]        =							backbone_config_class(**lowerCamelCase    )

								lowercase__  :	Union[str, Any]        =							backbone_featmap_shape
								lowercase__  :	Union[str, Any]        =							backbone_config
								lowercase__  :	Any        =							hidden_size
								lowercase__  :	Union[str, Any]        =							num_hidden_layers
								lowercase__  :	Any        =							num_attention_heads
								lowercase__  :	Optional[Any]        =							intermediate_size
								lowercase__  :	Optional[int]        =							hidden_act
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	str        =							attention_probs_dropout_prob
								lowercase__  :	Dict        =							initializer_range
								lowercase__  :	Optional[int]        =							layer_norm_eps
								lowercase__  :	Optional[Any]        =							image_size
								lowercase__  :	Union[str, Any]        =							patch_size
								lowercase__  :	Union[str, Any]        =							num_channels
								lowercase__  :	Optional[Any]        =							qkv_bias







	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	int        =							copy.deepcopy(self.__dict__    )
								lowercase__  :	List[Any]        =							self.backbone_config.to_dict()
								lowercase__  :	Dict        =							self.__class__.model_type
								return output





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test