


'''simple docstring'''







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


a__    :   List[str]								=			logging.get_logger(__name__)

a__    :   Optional[int]								=			{
    '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    lowercase_	(       _snake_case  ):
    __UpperCAmelCase       =      '''yoso'''


    def __init__(	self    ,   a=5_02_65    ,   a=7_68    ,   a=12    ,   a=12    ,   a=30_72    ,   a="gelu"    ,   a=0.1    ,   a=0.1    ,   a=40_96    ,   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    ,   ):
          super().__init__(pad_token_id=_a    ,   bos_token_id=_a    ,   eos_token_id=_a    ,   **_a       )

          UpperCamelCase__      =							vocab_size
          UpperCamelCase__      =							max_position_embeddings
          UpperCamelCase__      =							hidden_size
          UpperCamelCase__      =							num_hidden_layers
          UpperCamelCase__      =							num_attention_heads
          UpperCamelCase__      =							intermediate_size
          UpperCamelCase__      =							hidden_act
          UpperCamelCase__      =							hidden_dropout_prob
          UpperCamelCase__      =							attention_probs_dropout_prob
          UpperCamelCase__      =							initializer_range
          UpperCamelCase__      =							type_vocab_size
          UpperCamelCase__      =							layer_norm_eps
          UpperCamelCase__      =							position_embedding_type
          UpperCamelCase__      =							use_expectation
          UpperCamelCase__      =							hash_code_len
          UpperCamelCase__      =							num_hash
          UpperCamelCase__      =							conv_window
          UpperCamelCase__      =							use_fast_hash
          UpperCamelCase__      =							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)




