"""simple docstring"""


import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_:       Optional[Any]			     =       logging.get_logger(__name__)







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				WavLMForSequenceClassification.from_pretrained(lowercase_     ,					config=lowercase_   )
     lowercase__								=				downstream_dict["projector.weight"]
     lowercase__								=				downstream_dict["projector.bias"]
     lowercase__								=				downstream_dict["model.post_net.linear.weight"]
     lowercase__								=				downstream_dict["model.post_net.linear.bias"]
     return model







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



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







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				WavLMForXVector.from_pretrained(lowercase_     ,					config=lowercase_   )
     lowercase__								=				downstream_dict["connector.weight"]
     lowercase__								=				downstream_dict["connector.bias"]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase__								=				downstream_dict[
              f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase__								=				downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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







@torch.no_grad()
def        __a (		A     ,					A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				torch.load(lowercase_     ,					map_location="cpu"   )

     lowercase__								=				checkpoint["Downstream"]

     lowercase__								=				WavLMConfig.from_pretrained(lowercase_   )
     lowercase__								=				WavaVecaFeatureExtractor.from_pretrained(
         lowercase_     ,					return_attention_mask=lowercase_     ,					do_normalize=lowercase_   )

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

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

     hf_feature_extractor.save_pretrained(lowercase_   )
     hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 lowerCAmelCase_:       Optional[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_:       Dict			     =       parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





"""simple docstring"""







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'	)
      __lowerCamelCase    		=					'Hugging Face大法好，谁用谁知道。'
      __lowerCamelCase    		=					'Hugging Face大法好,谁用谁知道。<unk>'
      __lowerCamelCase    		=					'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

      __lowerCamelCase    		=					tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokens + [tokenizer.unk_token]

      __lowerCamelCase    		=					[13789, 13283, 1421, 8, 10, 1164, 13608, 16528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokenizer.decode(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)






