

"""simple docstring"""





import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_a				   =							{'UserAgent': UserAgent().random}
_a				   =							'https://www.mywaifulist.moe/random'




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : str)			->		None:
   '''simple docstring'''


   __lowercase			=		requests.get(UpperCamelCase_,    headers=UpperCamelCase_)
   with open(UpperCamelCase_,    "wb") as file:
      file.write(image.content)




def 						_A						(						)			->		tuple[str, str, str]:
   '''simple docstring'''


   __lowercase			=		BeautifulSoup(requests.get(UpperCamelCase_,    headers=UpperCamelCase_).text,    "html.parser")
   __lowercase			=		soup.find("meta",    attrs={"property": "og:title"}).attrs["content"]
   __lowercase			=		soup.find("meta",    attrs={"property": "og:image"}).attrs["content"]
   __lowercase			=		soup.find("p",    id="description").get_text()
   __lowercase	,__lowercase			=		os.path.splitext(os.path.basename(UpperCamelCase_))
   __lowercase			=		title.strip().replace(" ",    "_")
   __lowercase			=		F"""{image_title}{image_extension}"""
   save_image(UpperCamelCase_,    UpperCamelCase_)
   return (title, description, image_title)


if __name__ == "__main__":
   _a   ,		_a   ,		_a				   =							random_anime_character()
   print(F"{title}\n\n{desc}\n\nImage saved : {image_title}")







"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   _a				   =							parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




