
"""simple docstring"""

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

SCREAMING_SNAKE_CASE_			    =  ['''UtteranceLevel''']




@torch.no_grad()
def 			lowercase__  (    lowerCAmelCase :    Any       , lowerCAmelCase :    Optional[int]       , lowerCAmelCase :    Dict       , lowerCAmelCase :    Any       )       ->					Optional[Any]:






    """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__":
   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 torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (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
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

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



