



import argparse

import torch

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


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


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

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


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

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


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

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

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


@torch.no_grad()
def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       torch.load(snake_case      ,		map_location="""cpu"""     )

					_lowerCAmelCase         =       checkpoint["""Downstream"""]

					_lowerCAmelCase         =       WavLMConfig.from_pretrained(snake_case     )
					_lowerCAmelCase         =       WavaVecaFeatureExtractor.from_pretrained(
					    snake_case      ,		return_attention_mask=snake_case      ,		do_normalize=snake_case     )

					_lowerCAmelCase         =       hf_config.architectures[0]
					if arch.endswith("""ForSequenceClassification"""     ):
										_lowerCAmelCase         =       convert_classification(snake_case      ,		snake_case      ,		snake_case     )
					elif arch.endswith("""ForAudioFrameClassification"""     ):
										_lowerCAmelCase         =       convert_diarization(snake_case      ,		snake_case      ,		snake_case     )
					elif arch.endswith("""ForXVector"""     ):
										_lowerCAmelCase         =       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:
										_lowerCAmelCase         =       checkpoint["""Featurizer"""]["""weights"""]

					hf_feature_extractor.save_pretrained(snake_case     )
					hf_model.save_pretrained(snake_case     )


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)











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 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    List[str] 	=     HerbertTokenizer
     __lowerCamelCase       :    Union[str, Any] 	=     HerbertTokenizerFast
     __lowerCamelCase       :    Union[str, Any] 	=     True





     def 						lowerCamelCase							(	self  )		->				int:
        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:
           lowerCamelCase									=f_data.read().replace("""\n\n"""							, """\n"""  ).strip()

        lowerCamelCase									=[
            """<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>""",
        ]
        lowerCamelCase									=dict(zip(_UpperCAmelCase							, range(len(_UpperCAmelCase  )  )  )  )
        lowerCamelCase									=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

        lowerCamelCase									=os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""vocab_file"""]  )
        lowerCamelCase									=os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""merges_file"""]  )
        with open(self.vocab_file							, """w"""  ) as fp:
           fp.write(json.dumps(_UpperCAmelCase  )  )
        with open(self.merges_file							, """w"""  ) as fp:
           fp.write("""\n""".join(_UpperCAmelCase  )  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Optional[Any]:
        lowerCamelCase									="""lower newer"""
        lowerCamelCase									="""lower newer"""
        return input_text, output_text





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.tokenizer_class(vocab_file=self.vocab_file							, merges_file=self.merges_file  )

        lowerCamelCase									="""lower"""
        lowerCamelCase									=["""low""", """er</w>"""]
        lowerCamelCase									=tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=tokens + ["""<unk>"""]
        lowerCamelCase									=[16, 17, 23]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        if not self.test_rust_tokenizer:
           return

        lowerCamelCase									=self.get_tokenizer()
        lowerCamelCase									=self.get_rust_tokenizer()

        lowerCamelCase									="""lower,newer"""

        lowerCamelCase									=tokenizer.tokenize(_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.tokenize(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=tokenizer.encode(_UpperCAmelCase							, add_special_tokens=_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.encode(_UpperCAmelCase							, add_special_tokens=_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )

        lowerCamelCase									=self.get_rust_tokenizer()
        lowerCamelCase									=tokenizer.encode(_UpperCAmelCase  )
        lowerCamelCase									=rust_tokenizer.encode(_UpperCAmelCase  )
        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased"""  )

        lowerCamelCase									=tokenizer.encode("""konstruowanie sekwencji"""							, add_special_tokens=_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.encode("""konstruowanie wielu sekwencji"""							, add_special_tokens=_UpperCAmelCase  )

        lowerCamelCase									=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase							, _UpperCAmelCase  )

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