import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[Any]						,  lowercase_:		str						,  lowercase_:		Optional[Any]			):
       _UpperCamelCase       :    Dict					  =					WavLMForSequenceClassification.from_pretrained(lowercase_						,  config=lowercase_			)
       _UpperCamelCase       :    List[str]					  =					downstream_dict['projector.weight']
       _UpperCamelCase       :    Optional[int]					  =					downstream_dict['projector.bias']
       _UpperCamelCase       :    Optional[int]					  =					downstream_dict['model.post_net.linear.weight']
       _UpperCamelCase       :    Optional[Any]					  =					downstream_dict['model.post_net.linear.bias']
       return model


def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Union[str, Any]						,  lowercase_:		str			):
       _UpperCamelCase       :    List[Any]					  =					WavLMForAudioFrameClassification.from_pretrained(lowercase_						,  config=lowercase_			)
       _UpperCamelCase       :    str					  =					downstream_dict['model.linear.weight']
       _UpperCamelCase       :    Dict					  =					downstream_dict['model.linear.bias']
       return model


def SCREAMING_SNAKE_CASE			(       lowercase_:		Union[str, Any]						,  lowercase_:		Optional[int]						,  lowercase_:		Union[str, Any]			):
       _UpperCamelCase       :    List[str]					  =					WavLMForXVector.from_pretrained(lowercase_						,  config=lowercase_			)
       _UpperCamelCase       :    Union[str, Any]					  =					downstream_dict['connector.weight']
       _UpperCamelCase       :    Tuple					  =					downstream_dict['connector.bias']
       for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
              _UpperCamelCase       :    int					  =					downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              _UpperCamelCase       :    Tuple					  =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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


@torch.no_grad()
def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		List[str]						,  lowercase_:		Union[str, Any]						,  lowercase_:		Dict			):
       _UpperCamelCase       :    int					  =					torch.load(lowercase_						,  map_location='cpu'			)

       _UpperCamelCase       :    str					  =					checkpoint['Downstream']

       _UpperCamelCase       :    List[str]					  =					WavLMConfig.from_pretrained(lowercase_			)
       _UpperCamelCase       :    Tuple					  =					WavaVecaFeatureExtractor.from_pretrained(
           lowercase_						,  return_attention_mask=lowercase_						,  do_normalize=lowercase_			)

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

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

       hf_feature_extractor.save_pretrained(lowercase_			)
       hf_model.save_pretrained(lowercase_			)


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




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



