





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	str:
     a  :    int				=						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase_						,      config=lowerCAmelCase_       )
     a  :    Optional[int]				=						downstream_dict['''projector.weight''']
     a  :    Union[str, Any]				=						downstream_dict['''projector.bias''']
     a  :    Dict				=						downstream_dict['''model.post_net.linear.weight''']
     a  :    List[Any]				=						downstream_dict['''model.post_net.linear.bias''']
     return model







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	Tuple:
     a  :    Any				=						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase_						,      config=lowerCAmelCase_       )
     a  :    List[Any]				=						downstream_dict['''model.linear.weight''']
     a  :    Optional[int]				=						downstream_dict['''model.linear.bias''']
     return model







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	Tuple:
     a  :    str				=						WavLMForXVector.from_pretrained(lowerCAmelCase_						,      config=lowerCAmelCase_       )
     a  :    Dict				=						downstream_dict['''connector.weight''']
     a  :    Tuple				=						downstream_dict['''connector.bias''']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
          a  :    List[str]				=						downstream_dict[
              f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
          ]
          a  :    List[str]				=						downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

     a  :    Optional[int]				=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
     a  :    int				=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
     a  :    Dict				=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
     a  :    List[Any]				=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
     a  :    str				=						downstream_dict['''objective.W''']
     return model







@torch.no_grad()
def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     a  :    Tuple				=						torch.load(lowerCAmelCase_						,      map_location='''cpu'''       )

     a  :    Any				=						checkpoint['''Downstream''']

     a  :    Any				=						WavLMConfig.from_pretrained(lowerCAmelCase_       )
     a  :    Optional[int]				=						WavaVecaFeatureExtractor.from_pretrained(
         lowerCAmelCase_						,      return_attention_mask=lowerCAmelCase_						,      do_normalize=lowerCAmelCase_       )

     a  :    Tuple				=						hf_config.architectures[0]
     if arch.endswith('''ForSequenceClassification'''       ):
          a  :    Dict				=						convert_classification(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     elif arch.endswith('''ForAudioFrameClassification'''       ):
          a  :    Union[str, Any]				=						convert_diarization(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     elif arch.endswith('''ForXVector'''       ):
          a  :    str				=						convert_xvector(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     else:
          raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""       )

     if hf_config.use_weighted_layer_sum:
          a  :    List[str]				=						checkpoint['''Featurizer''']['''weights''']

     hf_feature_extractor.save_pretrained(lowerCAmelCase_       )
     hf_model.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
  UpperCAmelCase							   =      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.""")
  UpperCAmelCase							   =      parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        a  :    List[Any]				=						kdim
        a  :    List[str]				=						vdim
        a  :    int				=						self_attention
        a  :    List[Any]				=						bias

        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )