






"""simple docstring"""


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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




def       snake_case  (A_				:List[str]     ,      A_				:Optional[int]     ,      A_				:Any				):







  '''simple docstring'''

  a					:      Optional[Any]    =       WavaVecaForSequenceClassification.from_pretrained(lowercase_     ,      config=lowercase_				)
  a					:      str    =       downstream_dict['projector.weight']
  a					:      Optional[Any]    =       downstream_dict['projector.bias']
  a					:      Any    =       downstream_dict['model.post_net.linear.weight']
  a					:      List[str]    =       downstream_dict['model.post_net.linear.bias']
  return model




def       snake_case  (A_				:List[str]     ,      A_				:int     ,      A_				:Optional[int]				):







  '''simple docstring'''

  a					:      List[str]    =       WavaVecaForAudioFrameClassification.from_pretrained(lowercase_     ,      config=lowercase_				)
  a					:      str    =       downstream_dict['model.linear.weight']
  a					:      Any    =       downstream_dict['model.linear.bias']
  return model




def       snake_case  (A_				:List[Any]     ,      A_				:str     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      Optional[Any]    =       WavaVecaForXVector.from_pretrained(lowercase_     ,      config=lowercase_				)
  a					:      Optional[Any]    =       downstream_dict['connector.weight']
  a					:      int    =       downstream_dict['connector.bias']
  for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
    a					:      int    =       downstream_dict[
        f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
    ]
    a					:      Dict    =       downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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




@torch.no_grad()
def       snake_case  (A_				:List[str]     ,      A_				:Tuple     ,      A_				:Union[str, Any]     ,      A_				:Tuple				):







  '''simple docstring'''

  a					:      Tuple    =       torch.load(lowercase_     ,      map_location='cpu'				)

  a					:      Dict    =       checkpoint['Downstream']

  a					:      List[str]    =       WavaVecaConfig.from_pretrained(lowercase_				)
  a					:      Dict    =       WavaVecaFeatureExtractor.from_pretrained(
      lowercase_     ,      return_attention_mask=lowercase_     ,      do_normalize=lowercase_				)

  a					:      str    =       hf_config.architectures[0]
  if arch.endswith('ForSequenceClassification'				):
    a					:      int    =       convert_classification(lowercase_     ,      lowercase_     ,      lowercase_				)
  elif arch.endswith('ForAudioFrameClassification'				):
    a					:      Dict    =       convert_diarization(lowercase_     ,      lowercase_     ,      lowercase_				)
  elif arch.endswith('ForXVector'				):
    a					:      List[str]    =       convert_xvector(lowercase_     ,      lowercase_     ,      lowercase_				)
  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(lowercase_				)
  hf_model.save_pretrained(lowercase_				)


if __name__ == "__main__":
   _UpperCamelCase    :   Union[str, 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.')
   _UpperCamelCase    :   List[str]   			= parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












"""simple docstring"""


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




