


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A      :    List[str]		=					logging.get_logger(__name__)

A      :    str		=					{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class 			_lowercase (							__a):


       """simple docstring"""




       A__											=			"sew"





       def __init__(   self							:			Dict					,      __lowerCamelCase							:			List[str]=32					,      __lowerCamelCase							:			List[str]=768					,      __lowerCamelCase							:			List[str]=12					,      __lowerCamelCase							:			List[str]=12					,      __lowerCamelCase							:			List[str]=3072					,      __lowerCamelCase							:			List[str]=2					,      __lowerCamelCase							:			Tuple="gelu"					,      __lowerCamelCase							:			List[Any]=0.1					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			Union[str, Any]=0.1					,      __lowerCamelCase							:			Any=0.0					,      __lowerCamelCase							:			List[str]=0.1					,      __lowerCamelCase							:			int=0.1					,      __lowerCamelCase							:			Tuple=0.0_2					,      __lowerCamelCase							:			Union[str, Any]=1E-5					,      __lowerCamelCase							:			Optional[int]="group"					,      __lowerCamelCase							:			int="gelu"					,      __lowerCamelCase							:			int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)					,      __lowerCamelCase							:			int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,      __lowerCamelCase							:			str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,      __lowerCamelCase							:			str=False					,      __lowerCamelCase							:			Union[str, Any]=128					,      __lowerCamelCase							:			Optional[Any]=16					,      __lowerCamelCase							:			Optional[int]=True					,      __lowerCamelCase							:			str=0.0_5					,      __lowerCamelCase							:			Any=10					,      __lowerCamelCase							:			Union[str, Any]=2					,      __lowerCamelCase							:			int=0.0					,      __lowerCamelCase							:			List[str]=10					,      __lowerCamelCase							:			Tuple=0					,      __lowerCamelCase							:			List[str]="mean"					,      __lowerCamelCase							:			str=False					,      __lowerCamelCase							:			Any=False					,      __lowerCamelCase							:			List[str]=256					,      __lowerCamelCase							:			Dict=0					,      __lowerCamelCase							:			Tuple=1					,      __lowerCamelCase							:			Any=2					,      **__lowerCamelCase							:			List[Any]					,      ):


             '''simple docstring'''



             super().__init__(**_UpperCamelCase					,      pad_token_id=_UpperCamelCase					,      bos_token_id=_UpperCamelCase					,      eos_token_id=_UpperCamelCase       )
             lowerCamelCase__		:				List[str]    =  hidden_size
             lowerCamelCase__		:				Union[str, Any]    =  feat_extract_norm
             lowerCamelCase__		:				Optional[Any]    =  feat_extract_activation
             lowerCamelCase__		:				Optional[Any]    =  list(_UpperCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  list(_UpperCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  list(_UpperCamelCase       )
             lowerCamelCase__		:				List[str]    =  conv_bias
             lowerCamelCase__		:				int    =  num_conv_pos_embeddings
             lowerCamelCase__		:				Optional[int]    =  num_conv_pos_embedding_groups
             lowerCamelCase__		:				Any    =  len(self.conv_dim       )
             lowerCamelCase__		:				Dict    =  num_hidden_layers
             lowerCamelCase__		:				List[str]    =  intermediate_size
             lowerCamelCase__		:				List[Any]    =  squeeze_factor
             lowerCamelCase__		:				Optional[Any]    =  hidden_act
             lowerCamelCase__		:				Any    =  num_attention_heads
             lowerCamelCase__		:				Dict    =  hidden_dropout
             lowerCamelCase__		:				List[str]    =  attention_dropout
             lowerCamelCase__		:				int    =  activation_dropout
             lowerCamelCase__		:				str    =  feat_proj_dropout
             lowerCamelCase__		:				str    =  final_dropout
             lowerCamelCase__		:				Dict    =  layerdrop
             lowerCamelCase__		:				Optional[Any]    =  layer_norm_eps
             lowerCamelCase__		:				str    =  initializer_range
             lowerCamelCase__		:				int    =  vocab_size

             if (
                 (len(self.conv_stride       ) != self.num_feat_extract_layers)
                 or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
                 or (len(self.conv_dim       ) != self.num_feat_extract_layers)
             ):
                   raise ValueError(
                       "Configuration for convolutional layers is incorrect."
                       "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                       f"but is `len(config.conv_dim) = {len(self.conv_dim       )}`, `len(config.conv_stride)"
                       f"= {len(self.conv_stride       )}`, `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

             # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
             lowerCamelCase__		:				Optional[int]    =  apply_spec_augment
             lowerCamelCase__		:				Dict    =  mask_time_prob
             lowerCamelCase__		:				str    =  mask_time_length
             lowerCamelCase__		:				Tuple    =  mask_time_min_masks
             lowerCamelCase__		:				Any    =  mask_feature_prob
             lowerCamelCase__		:				int    =  mask_feature_length
             lowerCamelCase__		:				List[Any]    =  mask_feature_min_masks

             # ctc loss
             lowerCamelCase__		:				Optional[Any]    =  ctc_loss_reduction
             lowerCamelCase__		:				int    =  ctc_zero_infinity

             # sequence classification
             lowerCamelCase__		:				Any    =  use_weighted_layer_sum
             lowerCamelCase__		:				str    =  classifier_proj_size





       @property
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             return functools.reduce(operator.mul					,      self.conv_stride					,      1       )




from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = ""				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = "IMDb_Top_250_Movies.csv"				):




			"""simple docstring"""


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


if __name__ == "__main__":
			write_movies()



