




import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__			:       Union[str, Any]         =							logging.get_logger(__name__)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Optional[int]							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatForSequenceClassification.from_pretrained(__UpperCAmelCase      ,      config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['projector.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['projector.bias']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.post_net.linear.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.post_net.linear.bias']
      return model

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Tuple							) ->			int:
      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCAmelCase      ,      config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.linear.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.linear.bias']
      return model

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     int							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatForXVector.from_pretrained(__UpperCAmelCase      ,      config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['connector.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['connector.bias']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            SCREAMING_SNAKE_CASE_     						=      downstream_dict[
                f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
            ]
            SCREAMING_SNAKE_CASE_     						=      downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['objective.W']
      return model

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Optional[int]							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase      ,      map_location='cpu'							)

      SCREAMING_SNAKE_CASE_     						=      checkpoint['Downstream']

      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatConfig.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      WavaVecaFeatureExtractor.from_pretrained(
          __UpperCAmelCase      ,      return_attention_mask=__UpperCAmelCase      ,      do_normalize=__UpperCAmelCase							)

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

      if hf_config.use_weighted_layer_sum:
            SCREAMING_SNAKE_CASE_     						=      checkpoint['Featurizer']['weights']

      hf_feature_extractor.save_pretrained(__UpperCAmelCase							)
      hf_model.save_pretrained(__UpperCAmelCase							)


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





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Tuple=768						):
       super().__init__(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      proj_size

       SCREAMING_SNAKE_CASE_     						=      CLIPVisionModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      PaintByExampleMapper(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      nn.LayerNorm(config.hidden_size						)
       SCREAMING_SNAKE_CASE_     						=      nn.Linear(config.hidden_size    ,    self.proj_size						)

       # uncondition for scaling
       SCREAMING_SNAKE_CASE_     						=      nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)




 def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int=False						):
       SCREAMING_SNAKE_CASE_     						=      self.model(pixel_values=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      clip_output.pooler_output
       SCREAMING_SNAKE_CASE_     						=      self.mapper(latent_states[:, None]						)
       SCREAMING_SNAKE_CASE_     						=      self.final_layer_norm(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.proj_out(_lowerCAmelCase						)
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       super().__init__()
       SCREAMING_SNAKE_CASE_     						=      (config.num_hidden_layers + 1) // 5
       SCREAMING_SNAKE_CASE_     						=      config.hidden_size
       SCREAMING_SNAKE_CASE_     						=      1
       SCREAMING_SNAKE_CASE_     						=      nn.ModuleList(
           [
               BasicTransformerBlock(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    activation_fn='gelu'    ,    attention_bias=_lowerCAmelCase						)
               for _ in range(_lowerCAmelCase						)
           ]						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for block in self.blocks:
             SCREAMING_SNAKE_CASE_     						=      block(_lowerCAmelCase						)

       return hidden_states