


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


snake_case			:      Optional[Any] 							=   logging.get_logger(__name__)

snake_case			:      List[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    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Tuple        =						"mobilevit"





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

											_SCREAMING_SNAKE_CASE      = num_channels
											_SCREAMING_SNAKE_CASE      = image_size
											_SCREAMING_SNAKE_CASE      = patch_size
											_SCREAMING_SNAKE_CASE      = hidden_sizes
											_SCREAMING_SNAKE_CASE      = neck_hidden_sizes
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = mlp_ratio
											_SCREAMING_SNAKE_CASE      = expand_ratio
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = conv_kernel_size
											_SCREAMING_SNAKE_CASE      = output_stride
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = classifier_dropout_prob
											_SCREAMING_SNAKE_CASE      = initializer_range
											_SCREAMING_SNAKE_CASE      = layer_norm_eps
											_SCREAMING_SNAKE_CASE      = qkv_bias

											# decode head attributes for semantic segmentation
											_SCREAMING_SNAKE_CASE      = aspp_out_channels
											_SCREAMING_SNAKE_CASE      = atrous_rates
											_SCREAMING_SNAKE_CASE      = aspp_dropout_prob
											_SCREAMING_SNAKE_CASE      = semantic_loss_ignore_index







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Tuple        =						version.parse("1.11"							)





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





							@property
							def 		__magic_name__			(    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 		__magic_name__			(    self	)->	float:
											return 1e-4








from __future__ import annotations

from collections import Counter


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = [True] * n
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = True

				for i in range(3   ,int(n**0.5 + 1							)   ,2							):
								_SCREAMING_SNAKE_CASE      = i * 2
								while index < n:
												_SCREAMING_SNAKE_CASE      = False
												_SCREAMING_SNAKE_CASE      = index + i

				_SCREAMING_SNAKE_CASE      = [2]

				for i in range(3   ,UpperCAmelCase__   ,2							):
								if is_prime[i]:
												primes.append(UpperCAmelCase__							)

				return primes


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = str(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = []
				_SCREAMING_SNAKE_CASE      = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

				for duplicate in Counter(UpperCAmelCase__							) - Counter(set(UpperCAmelCase__							)							):
								_SCREAMING_SNAKE_CASE      = [int(number_str.replace(UpperCAmelCase__   ,UpperCAmelCase__							)							) for digit in digits]
								replacements.append(UpperCAmelCase__							)

				return replacements


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__ = 8							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()

				# Filter primes with less than 3 replaceable digits
				_SCREAMING_SNAKE_CASE      = {
				    x for x in set(prime_sieve(100_0000							)							) if len(str(UpperCAmelCase__							)							) - len(set(str(UpperCAmelCase__							)							)							) >= 3
				}

				for prime in primes:
								if prime in numbers_checked:
												continue

								_SCREAMING_SNAKE_CASE      = digit_replacements(UpperCAmelCase__							)

								for family in replacements:
												numbers_checked.update(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = primes.intersection(UpperCAmelCase__							)

												if len(UpperCAmelCase__							) != family_length:
																continue

												return min(UpperCAmelCase__							)

				return -1


if __name__ == "__main__":
							print(solution())
