



import argparse

import torch

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


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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       WavaVecaForAudioFrameClassification.from_pretrained(_UpperCAmelCase     ,				config=_UpperCAmelCase       )
    __a		 =       downstream_dict['''model.linear.weight''']
    __a		 =       downstream_dict['''model.linear.bias''']
    return model





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __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__":
 __snake_case		:List[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.''')
 __snake_case		:str   	=  parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class 					_A (			__UpperCAmelCase					):



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
           return Dataset.from_dict(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(dset.column_names  ,	['''col_1''', '''col_2'''])
           for i, r in enumerate(__SCREAMING_SNAKE_CASE):
               self.assertDictEqual(__SCREAMING_SNAKE_CASE  ,	example_records[i])



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           __a		 =       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
           self.assertEqual(dset.info  ,	dset_from_dict.info)



       def 	_lowerCamelCase    (	self							:  int):  # checks what happens with missing columns


           '''simple docstring'''

           __a		 =       [{'''col_1''': 1}, {'''col_2''': '''x'''}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertDictEqual(dset[0]  ,	{'''col_1''': 1})
           self.assertDictEqual(dset[1]  ,	{'''col_1''': None})  # NB: first record is used for columns



       def 	_lowerCamelCase    (	self							:  Optional[Any]):  # checks if the type can be inferred from the second record


           '''simple docstring'''

           __a		 =       [{'''col_1''': []}, {'''col_1''': [1, 2]}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertEqual(dset.info.features['''col_1''']  ,	Sequence(Value('''int64''')))





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       Dataset.from_list([])
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	0)
           self.assertListEqual(dset.column_names  ,	[])





