






"""simple docstring"""







import math

import flax.linen as nn
import jax.numpy as jnp






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = 1	,      lowerCAmelCase = 1	,      lowerCAmelCase = 1.0e4	,      lowerCAmelCase = False	,      lowerCAmelCase = 1.0	,      ):







       '''simple docstring'''
       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
       lowercase_	    =			float(embedding_dim // 2		)
       lowercase_	    =			math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
       lowercase_	    =			min_timescale * jnp.exp(jnp.arange(lowerCAmelCase	,      dtype=jnp.floataa		) * -log_timescale_increment		)
       lowercase_	    =			jnp.expand_dims(lowerCAmelCase	,      1		) * jnp.expand_dims(lowerCAmelCase	,      0		)

       # scale embeddings
       lowercase_	    =			scale * emb

       if flip_sin_to_cos:
              lowercase_	    =			jnp.concatenate([jnp.cos(lowerCAmelCase		), jnp.sin(lowerCAmelCase		)]	,      axis=1		)
       else:
              lowercase_	    =			jnp.concatenate([jnp.sin(lowerCAmelCase		), jnp.cos(lowerCAmelCase		)]	,      axis=1		)
       lowercase_	    =			jnp.reshape(lowerCAmelCase	,      [jnp.shape(lowerCAmelCase		)[0], embedding_dim]		)
       return signal





class   UpperCamelCase_ (       nn.Module  ):
  _A       :					int				=	32
  _A       :					jnp.dtype				=	jnp.floataa



  @nn.compact
  def __call__(      self ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         lowercase_	    =			nn.Dense(self.time_embed_dim ,		dtype=self.dtype ,		name="""linear_1"""				)(snake_case__				)
         lowercase_	    =			nn.silu(snake_case__				)
         lowercase_	    =			nn.Dense(self.time_embed_dim ,		dtype=self.dtype ,		name="""linear_2"""				)(snake_case__				)
         return temb





class   UpperCamelCase_ (       nn.Module  ):
  _A       :					int				=	32
  _A       :					bool				=	False
  _A       :					float				=	1



  @nn.compact
  def __call__(      self ,		snake_case__				)			->			Any:



         """simple docstring"""





         return get_sinusoidal_embeddings(
             snake_case__ ,		embedding_dim=self.dim ,		flip_sin_to_cos=self.flip_sin_to_cos ,		freq_shift=self.freq_shift				)








"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""projector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""projector.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""model.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForXVector.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""connector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              UpperCAmelCase	    =			downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCAmelCase	    =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)

       UpperCAmelCase	    =			checkpoint["""Downstream"""]

       UpperCAmelCase	    =			UniSpeechSatConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(
           lowerCAmelCase	,      return_attention_mask=lowerCAmelCase	,      do_normalize=lowerCAmelCase		)

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

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

       hf_feature_extractor.save_pretrained(lowerCAmelCase		)
       hf_model.save_pretrained(lowerCAmelCase		)


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_	:    str            =      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
