'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A										= logging.get_logger(__name__)

_A										= {
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[str, Any]			     =     '''yoso'''
   def __init__(self							,					_a=50_265							,					_a=768							,					_a=12							,					_a=12							,					_a=3_072							,					_a="gelu"							,					_a=0.1							,					_a=0.1							,					_a=4_096							,					_a=1							,					_a=0.02							,					_a=1e-12							,					_a="absolute"							,					_a=True							,					_a=9							,					_a=64							,					_a=None							,					_a=True							,					_a=True							,					_a=1							,					_a=0							,					_a=2							,					**_a							,					)     ->  int:
        super().__init__(pad_token_id=_a							,					bos_token_id=_a							,					eos_token_id=_a							,					**_a					)

        lowercase_       :							List[Any]  					=       vocab_size
        lowercase_       :							Optional[Any]  					=       max_position_embeddings
        lowercase_       :							Optional[Any]  					=       hidden_size
        lowercase_       :							Optional[Any]  					=       num_hidden_layers
        lowercase_       :							Dict  					=       num_attention_heads
        lowercase_       :							Optional[int]  					=       intermediate_size
        lowercase_       :							List[str]  					=       hidden_act
        lowercase_       :							Optional[Any]  					=       hidden_dropout_prob
        lowercase_       :							Optional[int]  					=       attention_probs_dropout_prob
        lowercase_       :							Any  					=       initializer_range
        lowercase_       :							List[Any]  					=       type_vocab_size
        lowercase_       :							List[Any]  					=       layer_norm_eps
        lowercase_       :							Optional[Any]  					=       position_embedding_type
        lowercase_       :							int  					=       use_expectation
        lowercase_       :							Tuple  					=       hash_code_len
        lowercase_       :							Optional[int]  					=       num_hash
        lowercase_       :							List[Any]  					=       conv_window
        lowercase_       :							Union[str, Any]  					=       use_fast_hash
        lowercase_       :							str  					=       lsh_backward





'''simple docstring'''




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

     lowercase_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


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)




