




import argparse

import torch

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


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





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      Optional[Any]   ,       UpperCAmelCase_	:      Optional[int]      )->			str:

       """simple docstring"""




       a			   =WavLMForSequenceClassification.from_pretrained(UpperCAmelCase_   ,       config=UpperCAmelCase_      )
       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    (							UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      Dict      )->			Optional[Any]:

       """simple docstring"""




       a			   =WavLMForAudioFrameClassification.from_pretrained(UpperCAmelCase_   ,       config=UpperCAmelCase_      )
       a			   =downstream_dict["""model.linear.weight"""]
       a			   =downstream_dict["""model.linear.bias"""]
       return model





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      Dict      )->			Union[str, Any]:

       """simple docstring"""




       a			   =WavLMForXVector.from_pretrained(UpperCAmelCase_   ,       config=UpperCAmelCase_      )
       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    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      Tuple   ,       UpperCAmelCase_	:      List[str]   ,       UpperCAmelCase_	:      Optional[int]      )->			int:

       """simple docstring"""




       a			   =torch.load(UpperCAmelCase_   ,       map_location="""cpu"""      )

       a			   =checkpoint["""Downstream"""]

       a			   =WavLMConfig.from_pretrained(UpperCAmelCase_      )
       a			   =WavaVecaFeatureExtractor.from_pretrained(
           UpperCAmelCase_   ,       return_attention_mask=UpperCAmelCase_   ,       do_normalize=UpperCAmelCase_      )

       a			   =hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""      ):
              a			   =convert_classification(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_      )
       elif arch.endswith("""ForAudioFrameClassification"""      ):
              a			   =convert_diarization(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_      )
       elif arch.endswith("""ForXVector"""      ):
              a			   =convert_xvector(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_      )
       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(UpperCAmelCase_      )
       hf_model.save_pretrained(UpperCAmelCase_      )


if __name__ == "__main__":
    _lowerCamelCase							=      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							=      parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






"""simple docstring"""

import copy

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


UpperCamelCase__			:     List[Any]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[str]			       =				{}
class      A__   (   lowercase_  ):
	A_				:					Dict      =   """fsmt_decoder"""




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=0   ,     lowercase	:						Dict=0						)       ->			str:
						super().__init__()
						__snake_case        =						vocab_size
						__snake_case        =						bos_token_id

class      A__   (   lowercase_  ):
	A_				:					Union[str, Any]      =   """fsmt"""
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Optional[Any]   ,     lowercase	:						Any=["en", "de"]   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Tuple="relu"   ,     lowercase	:						Tuple=1_024   ,     lowercase	:						Union[str, Any]=200   ,     lowercase	:						List[str]=1_024   ,     lowercase	:						Tuple=4_096   ,     lowercase	:						int=12   ,     lowercase	:						Any=16   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						str=4_096   ,     lowercase	:						str=12   ,     lowercase	:						Optional[int]=16   ,     lowercase	:						Dict=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						int=2   ,     lowercase	:						Any=True   ,     lowercase	:						int=True   ,     lowercase	:						int=False   ,     lowercase	:						List[str]=5   ,     lowercase	:						List[Any]=1.0   ,     lowercase	:						Union[str, Any]=False   ,     lowercase	:						Optional[Any]=True   ,     lowercase	:						int=1   ,     lowercase	:						Any=0   ,     lowercase	:						Optional[Any]=2   ,     lowercase	:						Any=2   ,     **lowercase	:						List[Any]   ,     )       ->			Union[str, Any]:
						__snake_case        =						langs
						__snake_case        =						src_vocab_size
						__snake_case        =						tgt_vocab_size
						__snake_case        =						d_model  # encoder_embed_dim and decoder_embed_dim

						__snake_case        =						encoder_ffn_dim
						__snake_case        =						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						max_position_embeddings
						__snake_case        =						init_std  # Normal(0, this parameter)
						__snake_case        =						activation_function

						__snake_case        =						DecoderConfig(vocab_size=lowercase   ,     bos_token_id=lowercase						)
						if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						dropout

						__snake_case        =						use_cache
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     decoder_start_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     tie_word_embeddings=lowercase   ,     forced_eos_token_id=lowercase   ,     max_length=lowercase   ,     num_beams=lowercase   ,     length_penalty=lowercase   ,     early_stopping=lowercase   ,     **lowercase   ,     )




	def 				a_		( self	:						Union[str, Any]						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.decoder.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


