




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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


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

    __UpperCamelCase			=    checkpoint['''Downstream''']

    __UpperCamelCase			=    HubertConfig.from_pretrained(lowercase_   )
    __UpperCamelCase			=    HubertForSequenceClassification.from_pretrained(lowercase_       ,  config=lowercase_   )
    __UpperCamelCase			=    WavaVecaFeatureExtractor.from_pretrained(
        lowercase_       ,  return_attention_mask=lowercase_       ,  do_normalize=lowercase_   )

    if hf_congfig.use_weighted_layer_sum:
        __UpperCamelCase			=    checkpoint['''Featurizer''']['''weights''']

    __UpperCamelCase			=    downstream_dict['''projector.weight''']
    __UpperCamelCase			=    downstream_dict['''projector.bias''']
    __UpperCamelCase			=    downstream_dict['''model.post_net.linear.weight''']
    __UpperCamelCase			=    downstream_dict['''model.post_net.linear.bias''']

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


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)














"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





