



import argparse

import torch

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


logging.set_verbosity_info()
A_		:     Optional[int]        =     logging.get_logger(__name__)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :List[Any]					):






 '''simple docstring'''

 a						=		WavaVecaForSequenceClassification.from_pretrained(UpperCAmelCase__ ,					config=UpperCAmelCase__					)
 a						=		downstream_dict["projector.weight"]
 a						=		downstream_dict["projector.bias"]
 a						=		downstream_dict["model.post_net.linear.weight"]
 a						=		downstream_dict["model.post_net.linear.bias"]
 return model


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Dict					):






 '''simple docstring'''

 a						=		WavaVecaForAudioFrameClassification.from_pretrained(UpperCAmelCase__ ,					config=UpperCAmelCase__					)
 a						=		downstream_dict["model.linear.weight"]
 a						=		downstream_dict["model.linear.bias"]
 return model


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		WavaVecaForXVector.from_pretrained(UpperCAmelCase__ ,					config=UpperCAmelCase__					)
 a						=		downstream_dict["connector.weight"]
 a						=		downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
  a						=		downstream_dict[
      F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  a						=		downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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


@torch.no_grad()
def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :str ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		torch.load(UpperCAmelCase__ ,					map_location="cpu"					)

 a						=		checkpoint["Downstream"]

 a						=		WavaVecaConfig.from_pretrained(UpperCAmelCase__					)
 a						=		WavaVecaFeatureExtractor.from_pretrained(
     UpperCAmelCase__ ,					return_attention_mask=UpperCAmelCase__ ,					do_normalize=UpperCAmelCase__					)

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

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

 hf_feature_extractor.save_pretrained(UpperCAmelCase__					)
 hf_model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     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.''')
     A_		:     Any        =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(UpperCAmelCase__	),   '''Tatoeba directory does not exist.'''	)
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    self.resolver.convert_models(["heb-eng"]							)




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.resolver.write_model_card("opus-mt-he-en"							,   dry_run=__lowerCAmelCase							)
    assert mmeta["long_pair"] == "heb-eng"

