



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					:				Dict   	=logging.get_logger(__name__)

lowerCAmelCase					:				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 	_a			(      snake_case_  ):
			_UpperCamelCase:   Any	     =		"mobilevit"






			def __init__(    self    ,  lowercase_=3    ,  lowercase_=256    ,  lowercase_=2    ,  lowercase_=[144, 192, 240]    ,  lowercase_=[16, 32, 64, 96, 128, 160, 640]    ,  lowercase_=4    ,  lowercase_=2.0    ,  lowercase_=4.0    ,  lowercase_="silu"    ,  lowercase_=3    ,  lowercase_=32    ,  lowercase_=0.1    ,  lowercase_=0.0    ,  lowercase_=0.1    ,  lowercase_=0.0_2    ,  lowercase_=1e-5    ,  lowercase_=True    ,  lowercase_=256    ,  lowercase_=[6, 12, 18]    ,  lowercase_=0.1    ,  lowercase_=255    ,  **lowercase_    ,  )	->					List[str]:
									super().__init__(**lowercase_ )

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

									# decode head attributes for semantic segmentation
									lowerCAmelCase	:   Union[str, Any]					     =					aspp_out_channels
									lowerCAmelCase	:   Optional[int]					     =					atrous_rates
									lowerCAmelCase	:   Tuple					     =					aspp_dropout_prob
									lowerCAmelCase	:   List[str]					     =					semantic_loss_ignore_index






class 	_a			(      snake_case_  ):
			_UpperCamelCase:   List[str]	     =		version.parse("1.11"  )






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






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






import bsa
import requests







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						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  (				SCREAMING_SNAKE_CASE__ = 5  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					int(float(SCREAMING_SNAKE_CASE__  )  )
						if num_movies < 1:
												return ()
						lowerCAmelCase	:   List[str]					     =					(
						    """https://www.imdb.com/search/title?title_type="""
						    F"""feature&sort=num_votes,desc&count={num_movies}"""
						)
						lowerCAmelCase	:   Optional[Any]					     =					bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__  ).content			,"""html.parser"""  )
						return tuple(
						    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__  )
						    for movie in source.find_all("""div"""			,class_="""lister-item mode-advanced"""  )  )


if __name__ == "__main__":
		import json

		lowerCAmelCase					:				Union[str, Any]   	=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)
		    )
		)

