





'''simple docstring'''







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_   :       str       =     logging.get_logger(__name__)






def 					_lowerCamelCase							(					lowercase		: int       ,				lowercase		: List[Any]       ,				lowercase		: List[str]		)      ->      List[str]:
				_a				      =					WavaVecaForSequenceClassification.from_pretrained(A__       ,				config=A__		)
				_a				      =					downstream_dict["""projector.weight"""]
				_a				      =					downstream_dict["""projector.bias"""]
				_a				      =					downstream_dict["""model.post_net.linear.weight"""]
				_a				      =					downstream_dict["""model.post_net.linear.bias"""]
				return model






def 					_lowerCamelCase							(					lowercase		: Dict       ,				lowercase		: Any       ,				lowercase		: List[Any]		)      ->      Dict:
				_a				      =					WavaVecaForAudioFrameClassification.from_pretrained(A__       ,				config=A__		)
				_a				      =					downstream_dict["""model.linear.weight"""]
				_a				      =					downstream_dict["""model.linear.bias"""]
				return model






def 					_lowerCamelCase							(					lowercase		: List[str]       ,				lowercase		: Any       ,				lowercase		: int		)      ->      Any:
				_a				      =					WavaVecaForXVector.from_pretrained(A__       ,				config=A__		)
				_a				      =					downstream_dict["""connector.weight"""]
				_a				      =					downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
								_a				      =					downstream_dict[
								    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
								]
								_a				      =					downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

				_a				      =					downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				_a				      =					downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				_a				      =					downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				_a				      =					downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				_a				      =					downstream_dict["""objective.W"""]
				return model






@torch.no_grad()
def 					_lowerCamelCase							(					lowercase		: int       ,				lowercase		: int       ,				lowercase		: str       ,				lowercase		: Union[str, Any]		)      ->      List[str]:
				_a				      =					torch.load(A__       ,				map_location="cpu"		)

				_a				      =					checkpoint["""Downstream"""]

				_a				      =					WavaVecaConfig.from_pretrained(A__		)
				_a				      =					WavaVecaFeatureExtractor.from_pretrained(
				    A__       ,				return_attention_mask=A__       ,				do_normalize=A__		)

				_a				      =					hf_config.architectures[0]
				if arch.endswith("ForSequenceClassification"		):
								_a				      =					convert_classification(A__       ,				A__       ,				A__		)
				elif arch.endswith("ForAudioFrameClassification"		):
								_a				      =					convert_diarization(A__       ,				A__       ,				A__		)
				elif arch.endswith("ForXVector"		):
								_a				      =					convert_xvector(A__       ,				A__       ,				A__		)
				else:
								raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'		)

				if hf_config.use_weighted_layer_sum:
								_a				      =					checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(A__		)
				hf_model.save_pretrained(A__		)


if __name__ == "__main__":
					lowerCAmelCase_   :       List[Any]       =     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.')
					lowerCAmelCase_   :       Tuple       =     parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




'''simple docstring'''






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


__A   :    str											= logging.get_logger(__name__)

__A   :    int											= {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roc_bert'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Optional[Any]=3_05_22       ,		lowerCamelCase       :    List[Any]=7_68       ,		lowerCamelCase       :    int=12       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=30_72       ,		lowerCamelCase       :    List[Any]="gelu"       ,		lowerCamelCase       :    Optional[int]=0.1       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    str=1E-12       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    List[Any]="absolute"       ,		lowerCamelCase       :    str=None       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    str=7_68       ,		lowerCamelCase       :    str=9_10       ,		lowerCamelCase       :    Tuple=5_12       ,		lowerCamelCase       :    List[str]=2_48_58       ,		lowerCamelCase       :    Dict=True       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  vocab_size
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : Any     =  hidden_size
      lowerCAmelCase_     : int     =  num_hidden_layers
      lowerCAmelCase_     : Tuple     =  num_attention_heads
      lowerCAmelCase_     : List[Any]     =  intermediate_size
      lowerCAmelCase_     : Tuple     =  hidden_act
      lowerCAmelCase_     : List[Any]     =  hidden_dropout_prob
      lowerCAmelCase_     : Dict     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  initializer_range
      lowerCAmelCase_     : Any     =  type_vocab_size
      lowerCAmelCase_     : Optional[int]     =  layer_norm_eps
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : List[Any]     =  enable_pronunciation
      lowerCAmelCase_     : List[Any]     =  enable_shape
      lowerCAmelCase_     : List[str]     =  pronunciation_embed_dim
      lowerCAmelCase_     : List[str]     =  pronunciation_vocab_size
      lowerCAmelCase_     : Dict     =  shape_embed_dim
      lowerCAmelCase_     : Optional[Any]     =  shape_vocab_size
      lowerCAmelCase_     : List[Any]     =  concat_input
      lowerCAmelCase_     : Any     =  position_embedding_type
      lowerCAmelCase_     : List[str]     =  classifier_dropout
      super().__init__(pad_token_id=lowerCamelCase       ,		**lowerCamelCase						)



