





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						WavaVecaForSequenceClassification.from_pretrained(A_ ,						config=A_	)
      SCREAMING_SNAKE_CASE		=						downstream_dict['''projector.weight''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''projector.bias''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.post_net.linear.weight''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.post_net.linear.bias''']
      return model



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						WavaVecaForAudioFrameClassification.from_pretrained(A_ ,						config=A_	)
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.linear.weight''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.linear.bias''']
      return model



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						WavaVecaForXVector.from_pretrained(A_ ,						config=A_	)
      SCREAMING_SNAKE_CASE		=						downstream_dict['''connector.weight''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
            SCREAMING_SNAKE_CASE		=						downstream_dict[
                F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
            ]
            SCREAMING_SNAKE_CASE		=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      SCREAMING_SNAKE_CASE		=						downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						torch.load(A_ ,						map_location="""cpu"""	)

      SCREAMING_SNAKE_CASE		=						checkpoint['''Downstream''']

      SCREAMING_SNAKE_CASE		=						WavaVecaConfig.from_pretrained(A_	)
      SCREAMING_SNAKE_CASE		=						WavaVecaFeatureExtractor.from_pretrained(
          A_ ,						return_attention_mask=A_ ,						do_normalize=A_	)

      SCREAMING_SNAKE_CASE		=						hf_config.architectures[0]
      if arch.endswith("""ForSequenceClassification"""	):
            SCREAMING_SNAKE_CASE		=						convert_classification(A_ ,						A_ ,						A_	)
      elif arch.endswith("""ForAudioFrameClassification"""	):
            SCREAMING_SNAKE_CASE		=						convert_diarization(A_ ,						A_ ,						A_	)
      elif arch.endswith("""ForXVector"""	):
            SCREAMING_SNAKE_CASE		=						convert_xvector(A_ ,						A_ ,						A_	)
      else:
            raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""	)

      if hf_config.use_weighted_layer_sum:
            SCREAMING_SNAKE_CASE		=						checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(A_	)
      hf_model.save_pretrained(A_	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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.""")
      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





"""simple docstring"""





import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							__UpperCamelCase  :      Tuple							      =		'''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

							print('''Googling.....''')

							__UpperCamelCase  :      Optional[int]							      =		F'''https://www.google.com/search?q={query}&num=100'''

							__UpperCamelCase  :      Optional[Any]							      =		requests.get(
							    url,
							    headers={'''User-Agent''': str(UserAgent().random)},
							)

							try:
														__UpperCamelCase  :      Union[str, Any]							      =		(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''yuRUbf'''})
														    .find('''a''')
														    .get('''href''')
														)

							except AttributeError:
														__UpperCamelCase  :      str							      =		parse_qs(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''kCrYT'''})
														    .find('''a''')
														    .get('''href''')
														)['''url'''][0]

							webbrowser.open(link)




