




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


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		str		     =   {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}







class 				UpperCAmelCase(					UpperCAmelCase_					):



	"""simple docstring"""






	a     : List[Any]	  =					"""mobilevit"""

	def __init__(					self	,				lowerCamelCase=3	,				lowerCamelCase=256	,				lowerCamelCase=2	,				lowerCamelCase=[144, 192, 240]	,				lowerCamelCase=[16, 32, 64, 96, 128, 160, 640]	,				lowerCamelCase=4	,				lowerCamelCase=2.0	,				lowerCamelCase=4.0	,				lowerCamelCase="silu"	,				lowerCamelCase=3	,				lowerCamelCase=32	,				lowerCamelCase=0.1	,				lowerCamelCase=0.0	,				lowerCamelCase=0.1	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-5	,				lowerCamelCase=True	,				lowerCamelCase=256	,				lowerCamelCase=[6, 12, 18]	,				lowerCamelCase=0.1	,				lowerCamelCase=255	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(**__UpperCamelCase    )

								lowercase__  :	Dict        =							num_channels
								lowercase__  :	Union[str, Any]        =							image_size
								lowercase__  :	Dict        =							patch_size
								lowercase__  :	Union[str, Any]        =							hidden_sizes
								lowercase__  :	List[str]        =							neck_hidden_sizes
								lowercase__  :	int        =							num_attention_heads
								lowercase__  :	Union[str, Any]        =							mlp_ratio
								lowercase__  :	Optional[Any]        =							expand_ratio
								lowercase__  :	Optional[int]        =							hidden_act
								lowercase__  :	int        =							conv_kernel_size
								lowercase__  :	Dict        =							output_stride
								lowercase__  :	Dict        =							hidden_dropout_prob
								lowercase__  :	str        =							attention_probs_dropout_prob
								lowercase__  :	int        =							classifier_dropout_prob
								lowercase__  :	int        =							initializer_range
								lowercase__  :	Optional[int]        =							layer_norm_eps
								lowercase__  :	List[Any]        =							qkv_bias

								# decode head attributes for semantic segmentation
								lowercase__  :	str        =							aspp_out_channels
								lowercase__  :	Dict        =							atrous_rates
								lowercase__  :	Tuple        =							aspp_dropout_prob
								lowercase__  :	Optional[int]        =							semantic_loss_ignore_index







class 				UpperCAmelCase(					UpperCAmelCase_					):



	"""simple docstring"""






	a     : List[Any]	  =					version.parse("""1.11"""					)

	@property
	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]    )

	@property
	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]    )
								else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]    )







	@property
	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								return 1E-4





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