






'''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       		=      {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"mobilenet_v2"






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=3  ,						__SCREAMING_SNAKE_CASE      :			Any=224  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=1.0  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=8  ,						__SCREAMING_SNAKE_CASE      :			str=8  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=6  ,						__SCREAMING_SNAKE_CASE      :			str=32  ,						__SCREAMING_SNAKE_CASE      :			Any=True  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=True  ,						__SCREAMING_SNAKE_CASE      :			Any="relu6"  ,						__SCREAMING_SNAKE_CASE      :			Tuple=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0.8  ,						__SCREAMING_SNAKE_CASE      :			str=0.02  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=0.001  ,						__SCREAMING_SNAKE_CASE      :			str=255  ,						**__SCREAMING_SNAKE_CASE      :			Tuple  ,						)			->			int:
							super().__init__(**__SCREAMING_SNAKE_CASE					)

							if depth_multiplier <= 0:
											raise ValueError('''depth_multiplier must be greater than zero.'''					)

							a_		:  List[str]											=      num_channels
							a_		:  Optional[Any]											=      image_size
							a_		:  List[str]											=      depth_multiplier
							a_		:  Any											=      depth_divisible_by
							a_		:  Optional[int]											=      min_depth
							a_		:  List[str]											=      expand_ratio
							a_		:  Union[str, Any]											=      output_stride
							a_		:  Any											=      first_layer_is_expansion
							a_		:  Union[str, Any]											=      finegrained_output
							a_		:  Tuple											=      hidden_act
							a_		:  Optional[Any]											=      tf_padding
							a_		:  Optional[int]											=      classifier_dropout_prob
							a_		:  Optional[Any]											=      initializer_range
							a_		:  Union[str, Any]											=      layer_norm_eps
							a_		:  Any											=      semantic_loss_ignore_index





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				version.parse("1.11"       )






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Mapping[str, Mapping[int, str]]:
							return OrderedDict([('''pixel_values''', {0: '''batch'''})]					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			float:
							return 1e-4












'''simple docstring'''







import requests
from bsa import BeautifulSoup





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   dict      ):
				a_		:  Tuple											=      BeautifulSoup(requests.get(__A						,		params=__A      ).content						,		'''html.parser'''      )
				a_		:  List[str]											=      soup.find('''div'''						,		attrs={'''class''': '''gs_ri'''}      )
				a_		:  List[str]											=      div.find('''div'''						,		attrs={'''class''': '''gs_fl'''}      ).find_all('''a'''      )
				return anchors[2].get_text()


if __name__ == "__main__":
		__lowerCAmelCase       		=      {
		    'title': (
		        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
		        'capacitance, volumetric capacitance, and energy density'
		    ),
		    'journal': 'Chem. Mater.',
		    'volume': 30,
		    'pages': '3979-3990',
		    'year': 2_018,
		    'hl': 'en',
		}
		print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




