



'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
lowercase_									=    logging.get_logger(__name__)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    UniSpeechSatForSequenceClassification.from_pretrained(__snake_case    ,      config=__snake_case)
     _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 lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    UniSpeechSatForAudioFrameClassification.from_pretrained(__snake_case    ,      config=__snake_case)
     _a           =    downstream_dict['''model.linear.weight''']
     _a           =    downstream_dict['''model.linear.bias''']
     return model




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    UniSpeechSatForXVector.from_pretrained(__snake_case    ,      config=__snake_case)
     _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 lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    torch.load(__snake_case    ,      map_location='''cpu''')

     _a           =    checkpoint['''Downstream''']

     _a           =    UniSpeechSatConfig.from_pretrained(__snake_case)
     _a           =    WavaVecaFeatureExtractor.from_pretrained(
         __snake_case    ,      return_attention_mask=__snake_case    ,      do_normalize=__snake_case)

     _a           =    hf_config.architectures[0]
     if arch.endswith('''ForSequenceClassification'''):
          _a           =    convert_classification(__snake_case    ,      __snake_case    ,      __snake_case)
     elif arch.endswith('''ForAudioFrameClassification'''):
          _a           =    convert_diarization(__snake_case    ,      __snake_case    ,      __snake_case)
     elif arch.endswith('''ForXVector'''):
          _a           =    convert_xvector(__snake_case    ,      __snake_case    ,      __snake_case)
     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(__snake_case)
     hf_model.save_pretrained(__snake_case)


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











import os
import sys
import unittest


__a:     Tuple   					=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__a:     Tuple   					=					os.path.join(git_repo_path, '''src''', '''transformers''')

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

         _UpperCAmelCase							=    find_backend("""    if not is_tokenizers_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tokenizers"""							)

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers_and_vision"""							)


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn("""torch"""       ,				lowerCamelCase							)
         self.assertIn("""tensorflow_text"""       ,				lowerCamelCase							)
         self.assertIn("""sentencepiece_and_tokenizers"""       ,				lowerCamelCase							)

         # Likewise, we can't assert on the exact content of a key
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertModel"""       ,				objects["""tf"""]							)
         self.assertIn("""FlaxBertModel"""       ,				objects["""flax"""]							)
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertTokenizer"""       ,				objects["""tensorflow_text"""]							)
         self.assertIn("""convert_slow_tokenizer"""       ,				objects["""sentencepiece_and_tokenizers"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    create_dummy_object("""CONSTANT"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				"""\nCONSTANT = None\n"""							)

         _UpperCAmelCase							=    create_dummy_object("""function"""       ,				"""'torch'"""							)
         self.assertEqual(
             lowerCamelCase       ,				"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
         _UpperCAmelCase							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)