




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__ :   Union[str, Any]						=							logging.get_logger(__name__)





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     List[str]  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_  ,							config=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      downstream_dict['projector.weight']
			SCREAMING_SNAKE_CASE_   : Tuple					  =      downstream_dict['projector.bias']
			SCREAMING_SNAKE_CASE_   : List[str]					  =      downstream_dict['model.post_net.linear.weight']
			SCREAMING_SNAKE_CASE_   : Dict					  =      downstream_dict['model.post_net.linear.bias']
			return model





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Optional[int]  )       ->  Optional[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_  ,							config=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      downstream_dict['model.linear.weight']
			SCREAMING_SNAKE_CASE_   : str					  =      downstream_dict['model.linear.bias']
			return model





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      UniSpeechSatForXVector.from_pretrained(lowerCamelCase_  ,							config=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      downstream_dict['connector.weight']
			SCREAMING_SNAKE_CASE_   : Any					  =      downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
						SCREAMING_SNAKE_CASE_   : Any					  =      downstream_dict[
						    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
						]
						SCREAMING_SNAKE_CASE_   : Dict					  =      downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

			SCREAMING_SNAKE_CASE_   : Dict					  =      downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			SCREAMING_SNAKE_CASE_   : str					  =      downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			SCREAMING_SNAKE_CASE_   : Dict					  =      downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      downstream_dict['objective.W']
			return model





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Any  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.load(lowerCamelCase_  ,							map_location='cpu'  )

			SCREAMING_SNAKE_CASE_   : Tuple					  =      checkpoint['Downstream']

			SCREAMING_SNAKE_CASE_   : List[Any]					  =      UniSpeechSatConfig.from_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_  ,							return_attention_mask=lowerCamelCase_  ,							do_normalize=lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : int					  =      hf_config.architectures[0]
			if arch.endswith('ForSequenceClassification'  ):
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      convert_classification(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			elif arch.endswith('ForAudioFrameClassification'  ):
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      convert_diarization(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			elif arch.endswith('ForXVector'  ):
						SCREAMING_SNAKE_CASE_   : Any					  =      convert_xvector(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			else:
						raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'  )

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

			hf_feature_extractor.save_pretrained(lowerCamelCase_  )
			hf_model.save_pretrained(lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[int]						=							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.''')
						UpperCamelCase__ :   List[str]						=							parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__A			:					List[Any]						=			{'UserAgent': UserAgent().random}





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   script.contents[0]
    SCREAMING_SNAKE_CASE										=   json.loads(data[data.find("{\"config\""       ) : -1]       )
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			List[str]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   f"https://www.instagram.com/{username}/"
       SCREAMING_SNAKE_CASE										=   self.get_json()





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   requests.get(self.url     , headers=__lowerCamelCase			).text
       SCREAMING_SNAKE_CASE										=   BeautifulSoup(__lowerCamelCase     , "html.parser"			).find_all("script"			)
       try:
           return extract_user_profile(scripts[4]			)
       except (json.decoder.JSONDecodeError, KeyError):
           return extract_user_profile(scripts[3]			)





   def __repr__(       self					:			Union[str, Any]			):
       return f"{self.__class__.__name__}('{self.username}')"





   def __str__(       self					:			str			):
       return f"{self.fullname} ({self.username}) is {self.biography}"





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.user_data["username"]





   @property
   def      _snake_case		(       self					:			List[Any]			):
       return self.user_data["full_name"]





   @property
   def      _snake_case		(       self					:			List[str]			):
       return self.user_data["biography"]





   @property
   def      _snake_case		(       self					:			Tuple			):
       return self.user_data["business_email"]





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return self.user_data["external_url"]





   @property
   def      _snake_case		(       self					:			int			):
       return self.user_data["edge_followed_by"]["count"]





   @property
   def      _snake_case		(       self					:			List[str]			):
       return self.user_data["edge_follow"]["count"]





   @property
   def      _snake_case		(       self					:			List[Any]			):
       return self.user_data["edge_owner_to_timeline_media"]["count"]





   @property
   def      _snake_case		(       self					:			Any			):
       return self.user_data["profile_pic_url_hd"]





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.user_data["is_verified"]





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.user_data["is_private"]





def 	__a     (      A__			:  str = "github"       ):
    import os

    if os.environ.get("CI"       ):
        return  # test failing on GitHub Actions
    SCREAMING_SNAKE_CASE										=   InstagramUser(A__       )
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data			,  A__       )
    assert instagram_user.username == username
    if username != "github":
        return
    assert instagram_user.fullname == "GitHub"
    assert instagram_user.biography == "Built for developers."
    assert instagram_user.number_of_posts > 150
    assert instagram_user.number_of_followers > 120000
    assert instagram_user.number_of_followings > 15
    assert instagram_user.email == "support@github.com"
    assert instagram_user.website == "https://github.com/readme"
    assert instagram_user.profile_picture_url.startswith("https://instagram."       )
    assert instagram_user.is_verified is True
    assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    __A			:					Dict						=			InstagramUser('github')
    print(instagram_user)
    print(f'{instagram_user.number_of_posts = }')
    print(f'{instagram_user.number_of_followers = }')
    print(f'{instagram_user.number_of_followings = }')
    print(f'{instagram_user.email = }')
    print(f'{instagram_user.website = }')
    print(f'{instagram_user.profile_picture_url = }')
    print(f'{instagram_user.is_verified = }')
    print(f'{instagram_user.is_private = }')