




'''simple docstring'''







import argparse

import torch

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


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





def 						snake_case_							(_a     :							Dict   ,       _a     :							Tuple   ,       _a     :							Optional[Any]					):
    UpperCAmelCase      	=		WavLMForSequenceClassification.from_pretrained(_a   ,       config=_a					)
    UpperCAmelCase      	=		downstream_dict['''projector.weight''']
    UpperCAmelCase      	=		downstream_dict['''projector.bias''']
    UpperCAmelCase      	=		downstream_dict['''model.post_net.linear.weight''']
    UpperCAmelCase      	=		downstream_dict['''model.post_net.linear.bias''']
    return model





def 						snake_case_							(_a     :							int   ,       _a     :							Tuple   ,       _a     :							int					):
    UpperCAmelCase      	=		WavLMForAudioFrameClassification.from_pretrained(_a   ,       config=_a					)
    UpperCAmelCase      	=		downstream_dict['''model.linear.weight''']
    UpperCAmelCase      	=		downstream_dict['''model.linear.bias''']
    return model





def 						snake_case_							(_a     :							Any   ,       _a     :							str   ,       _a     :							int					):
    UpperCAmelCase      	=		WavLMForXVector.from_pretrained(_a   ,       config=_a					)
    UpperCAmelCase      	=		downstream_dict['''connector.weight''']
    UpperCAmelCase      	=		downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
        UpperCAmelCase      	=		downstream_dict[
            F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        UpperCAmelCase      	=		downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

    UpperCAmelCase      	=		downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    UpperCAmelCase      	=		downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    UpperCAmelCase      	=		downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    UpperCAmelCase      	=		downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    UpperCAmelCase      	=		downstream_dict['''objective.W''']
    return model





@torch.no_grad()
def 						snake_case_							(_a     :							Optional[Any]   ,       _a     :							List[str]   ,       _a     :							str   ,       _a     :							int					):
    UpperCAmelCase      	=		torch.load(_a   ,       map_location='''cpu'''					)

    UpperCAmelCase      	=		checkpoint['''Downstream''']

    UpperCAmelCase      	=		WavLMConfig.from_pretrained(_a					)
    UpperCAmelCase      	=		WavaVecaFeatureExtractor.from_pretrained(
        _a   ,       return_attention_mask=_a   ,       do_normalize=_a					)

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

    if hf_config.use_weighted_layer_sum:
        UpperCAmelCase      	=		checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(_a					)
    hf_model.save_pretrained(_a					)


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








'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					str           = HerbertTokenizer
       __a    :					Optional[Any]           = HerbertTokenizerFast
       __a    :					Any           = True
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           super().setUp()

           # Use a simpler test file without japanese/chinese characters
           with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"       ,    encoding='''utf-8'''    ) as f_data:
               UpperCAmelCase      	=		f_data.read().replace('''\n\n'''       ,    '''\n'''    ).strip()

           UpperCAmelCase      	=		[
               '''<s>''',
               '''</s>''',
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               ''',</w>''',
               '''<unk>''',
           ]
           UpperCAmelCase      	=		dict(zip(lowercase       ,    range(len(lowercase    )    )    )    )
           UpperCAmelCase      	=		['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''merges_file''']    )
           with open(self.vocab_file       ,    '''w'''    ) as fp:
               fp.write(json.dumps(lowercase    )    )
           with open(self.merges_file       ,    '''w'''    ) as fp:
               fp.write('''\n'''.join(lowercase    )    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		'''lower newer'''
           return input_text, output_text
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(vocab_file=self.vocab_file       ,    merges_file=self.merges_file    )

           UpperCAmelCase      	=		'''lower'''
           UpperCAmelCase      	=		['''low''', '''er</w>''']
           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokens + ['''<unk>''']
           UpperCAmelCase      	=		[16, 17, 23]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           UpperCAmelCase      	=		self.get_tokenizer()
           UpperCAmelCase      	=		self.get_rust_tokenizer()

           UpperCAmelCase      	=		'''lower,newer'''

           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		self.get_rust_tokenizer()
           UpperCAmelCase      	=		tokenizer.encode(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )







       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''    )

           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie sekwencji'''       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie wielu sekwencji'''       ,    add_special_tokens=lowercase    )

           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase    )
           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase       ,    lowercase    )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + text_a + [2]




