"""simple docstring"""
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = ""   )   ->			dict[str, float]:





						"""simple docstring"""






						snake_case        =     url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
						snake_case        =     BeautifulSoup(requests.get(_UpperCamelCase   ).text	,     'html.parser'   )
						snake_case        =     soup.find_all('td'	,     attrs='titleColumn'   )
						snake_case        =     soup.find_all('td'	,     class_='ratingColumn imdbRating'   )
						return {
						    title.a.text: float(rating.strong.text   )
						    for title, rating in zip(_UpperCamelCase	,     _UpperCamelCase   )
						}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = "IMDb_Top_250_Movies.csv"   )   ->			None:





						"""simple docstring"""






						snake_case        =     get_imdb_top_aaa_movies()
						with open(_UpperCamelCase	,     'w'	,     newline=''   ) as out_file:
												snake_case        =     csv.writer(_UpperCamelCase   )
												writer.writerow(['Movie title', 'IMDb rating']   )
												for title, rating in movies.items():
																		writer.writerow([title, rating]   )


if __name__ == "__main__":
					write_movies()



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  """swinv2"""

				_lowerCAmelCase       :      Any     					=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(			self						,      lowerCAmelCase=2_24						,      lowerCAmelCase=4						,      lowerCAmelCase=3						,      lowerCAmelCase=96						,      lowerCAmelCase=[2, 2, 6, 2]						,      lowerCAmelCase=[3, 6, 12, 24]						,      lowerCAmelCase=7						,      lowerCAmelCase=4.0						,      lowerCAmelCase=True						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.1						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=False						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=32						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     num_channels
										snake_case        =     embed_dim
										snake_case        =     depths
										snake_case        =     len(lowerCAmelCase      )
										snake_case        =     num_heads
										snake_case        =     window_size
										snake_case        =     mlp_ratio
										snake_case        =     qkv_bias
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     drop_path_rate
										snake_case        =     hidden_act
										snake_case        =     use_absolute_embeddings
										snake_case        =     layer_norm_eps
										snake_case        =     initializer_range
										snake_case        =     encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case        =     int(embed_dim * 2 ** (len(lowerCAmelCase      ) - 1)      )
										snake_case        =     (0, 0, 0, 0)


