






"""simple docstring"""







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





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



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			nn.Conv(
             self.out_channels ,		kernel_size=(3, 3) ,		strides=(1, 1) ,		padding=((1, 1), (1, 1)) ,		dtype=self.dtype ,		)



  def __call__(      self ,		snake_case__				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			hidden_states.shape
         UpperCAmelCase	    =			jax.image.resize(
             snake_case__ ,		shape=(batch, height * 2, width * 2, channels) ,		method="""nearest""" ,		)
         UpperCAmelCase	    =			self.conv(snake_case__				)
         return hidden_states





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



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			nn.Conv(
             self.out_channels ,		kernel_size=(3, 3) ,		strides=(2, 2) ,		padding=((1, 1), (1, 1)) ,		dtype=self.dtype ,		)



  def __call__(      self ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.conv(snake_case__				)
         return hidden_states





class   UpperCamelCase_ (       nn.Module  ):
  _A       :					int
  _A       :					int				=	None
  _A       :					float				=	0.0
  _A       :					bool				=	None
  _A       :					jnp.dtype				=	jnp.floataa



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.in_channels if self.out_channels is None else self.out_channels

         UpperCAmelCase	    =			nn.GroupNorm(num_groups=32 ,		epsilon=1e-5				)
         UpperCAmelCase	    =			nn.Conv(
             snake_case__ ,		kernel_size=(3, 3) ,		strides=(1, 1) ,		padding=((1, 1), (1, 1)) ,		dtype=self.dtype ,		)

         UpperCAmelCase	    =			nn.Dense(snake_case__ ,		dtype=self.dtype				)

         UpperCAmelCase	    =			nn.GroupNorm(num_groups=32 ,		epsilon=1e-5				)
         UpperCAmelCase	    =			nn.Dropout(self.dropout_prob				)
         UpperCAmelCase	    =			nn.Conv(
             snake_case__ ,		kernel_size=(3, 3) ,		strides=(1, 1) ,		padding=((1, 1), (1, 1)) ,		dtype=self.dtype ,		)

         UpperCAmelCase	    =			self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

         UpperCAmelCase	    =			None
         if use_nin_shortcut:
                UpperCAmelCase	    =			nn.Conv(
                    snake_case__ ,		kernel_size=(1, 1) ,		strides=(1, 1) ,		padding="""VALID""" ,		dtype=self.dtype ,		)



  def __call__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__=True				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			hidden_states
         UpperCAmelCase	    =			self.norma(snake_case__				)
         UpperCAmelCase	    =			nn.swish(snake_case__				)
         UpperCAmelCase	    =			self.conva(snake_case__				)

         UpperCAmelCase	    =			self.time_emb_proj(nn.swish(snake_case__				)				)
         UpperCAmelCase	    =			jnp.expand_dims(jnp.expand_dims(snake_case__ ,		1				) ,		1				)
         UpperCAmelCase	    =			hidden_states + temb

         UpperCAmelCase	    =			self.norma(snake_case__				)
         UpperCAmelCase	    =			nn.swish(snake_case__				)
         UpperCAmelCase	    =			self.dropout(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			self.conva(snake_case__				)

         if self.conv_shortcut is not None:
                UpperCAmelCase	    =			self.conv_shortcut(snake_case__				)

         return hidden_states + residual








"""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)
