

'''simple docstring'''

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


A_      =							logging.get_logger(__name__)

A_      =							{
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 							_snake_case	(    _a  ):
   _A    :       Tuple      				=   '''nystromformer'''
   def __init__(	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=30_000       ,SCREAMING_SNAKE_CASE__     : List[str]=768       ,SCREAMING_SNAKE_CASE__     : str=12       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=12       ,SCREAMING_SNAKE_CASE__     : List[Any]=3_072       ,SCREAMING_SNAKE_CASE__     : Any="gelu_new"       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=0.1       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=0.1       ,SCREAMING_SNAKE_CASE__     : Dict=510       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=2       ,SCREAMING_SNAKE_CASE__     : str=64       ,SCREAMING_SNAKE_CASE__     : int=64       ,SCREAMING_SNAKE_CASE__     : str=65       ,SCREAMING_SNAKE_CASE__     : Tuple=False       ,SCREAMING_SNAKE_CASE__     : Dict=0.02       ,SCREAMING_SNAKE_CASE__     : List[str]=1e-5       ,SCREAMING_SNAKE_CASE__     : Optional[int]=1       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=0       ,SCREAMING_SNAKE_CASE__     : Any=2       ,**SCREAMING_SNAKE_CASE__     : List[str]       ,):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   vocab_size
     SCREAMING_SNAKE_CASE:Optional[int]   =   max_position_embeddings
     SCREAMING_SNAKE_CASE:Optional[int]   =   hidden_size
     SCREAMING_SNAKE_CASE:Any   =   num_hidden_layers
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   num_attention_heads
     SCREAMING_SNAKE_CASE:Optional[Any]   =   intermediate_size
     SCREAMING_SNAKE_CASE:Any   =   hidden_act
     SCREAMING_SNAKE_CASE:int   =   hidden_dropout_prob
     SCREAMING_SNAKE_CASE:Optional[Any]   =   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE:List[Any]   =   initializer_range
     SCREAMING_SNAKE_CASE:Tuple   =   type_vocab_size
     SCREAMING_SNAKE_CASE:Tuple   =   segment_means_seq_len
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   num_landmarks
     SCREAMING_SNAKE_CASE:str   =   conv_kernel_size
     SCREAMING_SNAKE_CASE:Dict   =   inv_coeff_init_option
     SCREAMING_SNAKE_CASE:List[str]   =   layer_norm_eps
     super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__       ,bos_token_id=SCREAMING_SNAKE_CASE__       ,eos_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)









'''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 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(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}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

  if hf_congfig.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Featurizer"]["weights"]

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(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)






