

"""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	:       Any        =						logging.get_logger(__name__)

__A	:       str        =						{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       "mobilevitv2"


		def __init__(self					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Dict=3	,	__SCREAMING_SNAKE_CASE					:						Dict=2_5_6	,	__SCREAMING_SNAKE_CASE					:						List[Any]=2	,	__SCREAMING_SNAKE_CASE					:						Tuple=2.0	,	__SCREAMING_SNAKE_CASE					:						Any="swish"	,	__SCREAMING_SNAKE_CASE					:						List[Any]=3	,	__SCREAMING_SNAKE_CASE					:						Tuple=3_2	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=0.1	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=0.0_2	,	__SCREAMING_SNAKE_CASE					:						Dict=1E-5	,	__SCREAMING_SNAKE_CASE					:						Tuple=5_1_2	,	__SCREAMING_SNAKE_CASE					:						List[Any]=[6, 1_2, 1_8]	,	__SCREAMING_SNAKE_CASE					:						Tuple=0.1	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=2_5_5	,	__SCREAMING_SNAKE_CASE					:						List[str]=[2, 4, 3]	,	__SCREAMING_SNAKE_CASE					:						List[str]=[1_2_8, 1_9_2, 2_5_6]	,	__SCREAMING_SNAKE_CASE					:						Any=1.0	,	__SCREAMING_SNAKE_CASE					:						Any=2	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=0.0	,	__SCREAMING_SNAKE_CASE					:						int=0.0	,	**__SCREAMING_SNAKE_CASE					:						Tuple	,	):
							super().__init__(**__SCREAMING_SNAKE_CASE)

							A     =   num_channels
							A     =   image_size
							A     =   patch_size
							A     =   expand_ratio
							A     =   hidden_act
							A     =   conv_kernel_size
							A     =   output_stride
							A     =   initializer_range
							A     =   layer_norm_eps
							A     =   n_attn_blocks
							A     =   base_attn_unit_dims
							A     =   width_multiplier
							A     =   ffn_multiplier
							A     =   ffn_dropout
							A     =   attn_dropout
							A     =   classifier_dropout_prob

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





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       version.parse("1.11"		)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							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					:						List[str]):
							return 1E-4










'''simple docstring'''

import bsa
import requests







def     __UpperCamelCase					(				_lowercase       )			->      dict[str, str]:
	return {
	    "name": soup.ha.a.text,
	    "genre": soup.find('span',					class_='genre'       ).text.strip(),
	    "rating": soup.strong.text,
	    "page_link": f'''https://www.imdb.com{soup.a.get("href"       )}''',
	}







def     __UpperCamelCase					(				_lowercase = 5       )			->      tuple:
	_lowercase       :							List[Any]  	=		int(float(_lowercase       )       )
	if num_movies < 1:
		return ()
	_lowercase       :							Any  	=		(
	    'https://www.imdb.com/search/title?title_type='
	    f'''feature&sort=num_votes,desc&count={num_movies}'''
	)
	_lowercase       :							Tuple  	=		bsa.BeautifulSoup(requests.get(_lowercase       ).content,					'html.parser'       )
	return tuple(
	    get_movie_data_from_soup(_lowercase       )
	    for movie in source.find_all('div',					class_='lister-item mode-advanced'       )       )


if __name__ == "__main__":
		import json

		_A					:     Optional[int]      =int(input('''How many movies would you like to see? '''))
		print(
		    ''', '''.join(
		        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
		    )
		)
