
"""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


_A            = logging.get_logger(__name__)

_A            = {
    '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        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	'mobilevit'




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

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

			# decode head attributes for semantic segmentation
			lowerCamelCase     :			Any		=    aspp_out_channels
			lowerCamelCase     :			int		=    atrous_rates
			lowerCamelCase     :			Optional[Any]		=    aspp_dropout_prob
			lowerCamelCase     :			int		=    semantic_loss_ignore_index






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	version.parse('1.11'		)




	@property
	def 			_UpperCamelCase    ( self					)		->							Mapping[str, Mapping[int, str]]:
			return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]					)




	@property
	def 			_UpperCamelCase    ( self					)		->							Mapping[str, Mapping[int, str]]:
			if self.task == "image-classification":
					return OrderedDict([('logits', {0: 'batch'})]					)
			else:
					return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]					)




	@property
	def 			_UpperCamelCase    ( self					)		->							float:
			return 1E-4








"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		for r in range(1,						a_			):
				lowerCamelCase     :			Tuple		=    []
				for x in range(n - 1			):
						lowerCamelCase     :			List[str]		=    pow(a_,						a_,						a_			)
						if val in li:
								break
						li.append(a_			)
				else:
						return r
		return None


if __name__ == "__main__":
					_A            = int(input('Enter a prime number q: '))
					_A            = find_primitive(q)
					if a is None:
										print(F"""Cannot find the primitive for the value: {a!r}""")
					else:
										_A            = int(input('Enter private key of A: '))
										_A            = pow(a, a_private, q)
										_A            = int(input('Enter private key of B: '))
										_A            = pow(a, b_private, q)

										_A            = pow(b_public, a_private, q)
										_A            = pow(a_public, b_private, q)

										print('The key value generated by A is: ', a_secret)
										print('The key value generated by B is: ', b_secret)






