





import argparse

import torch

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


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


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		lowerCamelCase :			List[Any]    		=WavLMForSequenceClassification.from_pretrained(a__				,							config=a__		)
		lowerCamelCase :			Optional[Any]    		=downstream_dict['''projector.weight''']
		lowerCamelCase :			int    		=downstream_dict['''projector.bias''']
		lowerCamelCase :			Union[str, Any]    		=downstream_dict['''model.post_net.linear.weight''']
		lowerCamelCase :			List[Any]    		=downstream_dict['''model.post_net.linear.bias''']
		return model


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		lowerCamelCase :			List[Any]    		=WavLMForAudioFrameClassification.from_pretrained(a__				,							config=a__		)
		lowerCamelCase :			List[Any]    		=downstream_dict['''model.linear.weight''']
		lowerCamelCase :			List[str]    		=downstream_dict['''model.linear.bias''']
		return model


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		lowerCamelCase :			Union[str, Any]    		=WavLMForXVector.from_pretrained(a__				,							config=a__		)
		lowerCamelCase :			Any    		=downstream_dict['''connector.weight''']
		lowerCamelCase :			List[Any]    		=downstream_dict['''connector.bias''']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
				lowerCamelCase :			int    		=downstream_dict[
				    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
				]
				lowerCamelCase :			List[Any]    		=downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

		lowerCamelCase :			Optional[Any]    		=downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
		lowerCamelCase :			Any    		=downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
		lowerCamelCase :			Tuple    		=downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
		lowerCamelCase :			Dict    		=downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
		lowerCamelCase :			Union[str, Any]    		=downstream_dict['''objective.W''']
		return model


@torch.no_grad()
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		lowerCamelCase :			Union[str, Any]    		=torch.load(a__				,							map_location='''cpu'''		)

		lowerCamelCase :			List[Any]    		=checkpoint['''Downstream''']

		lowerCamelCase :			Tuple    		=WavLMConfig.from_pretrained(a__		)
		lowerCamelCase :			Tuple    		=WavaVecaFeatureExtractor.from_pretrained(
		    a__				,							return_attention_mask=a__				,							do_normalize=a__		)

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

		if hf_config.use_weighted_layer_sum:
				lowerCamelCase :			Union[str, Any]    		=checkpoint['''Featurizer''']['''weights''']

		hf_feature_extractor.save_pretrained(a__		)
		hf_model.save_pretrained(a__		)


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



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            __UpperCamelCase  ,						split=__UpperCamelCase  ,						features=__UpperCamelCase  ,						cache_dir=__UpperCamelCase  ,						keep_in_memory=__UpperCamelCase  ,						streaming=__UpperCamelCase  ,						num_proc=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							path_or_paths if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else {self.split: path_or_paths}
        UpperCamelCase_											=							Csv(
            cache_dir=__UpperCamelCase  ,						data_files=__UpperCamelCase  ,						features=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self.streaming:
               UpperCamelCase_											=							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None

               self.builder.download_and_prepare(
                   download_config=__UpperCamelCase  ,						download_mode=__UpperCamelCase  ,						verification_mode=__UpperCamelCase  ,						base_path=__UpperCamelCase  ,						num_proc=self.num_proc  ,						)
               UpperCamelCase_											=							self.builder.as_dataset(
                   split=self.split  ,						verification_mode=__UpperCamelCase  ,						in_memory=self.keep_in_memory  )
        return dataset



class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        if num_proc is not None and num_proc <= 0:
               raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''  )

        UpperCamelCase_											=							dataset
        UpperCamelCase_											=							path_or_buf
        UpperCamelCase_											=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCamelCase_											=							num_proc
        UpperCamelCase_											=							"""utf-8"""
        UpperCamelCase_											=							to_csv_kwargs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.to_csv_kwargs.pop("""path_or_buf"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""header"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""index"""  ,						__UpperCamelCase  )

        if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)  ):
               with open(self.path_or_buf  ,						"""wb+"""  ) as buffer:
                      UpperCamelCase_											=							self._write(file_obj=__UpperCamelCase  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        else:
               UpperCamelCase_											=							self._write(file_obj=self.path_or_buf  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        return written

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							args

        UpperCamelCase_											=							query_table(
            table=self.dataset.data  ,						key=slice(__UpperCamelCase  ,						offset + self.batch_size  )  ,						indices=self.dataset._indices  ,						)
        UpperCamelCase_											=							batch.to_pandas().to_csv(
            path_or_buf=__UpperCamelCase  ,						header=header if (offset == 0) else False  ,						index=__UpperCamelCase  ,						**__UpperCamelCase  )
        return csv_str.encode(self.encoding  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							0

        if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0  ,						len(self.dataset  )  ,						self.batch_size  )  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                      UpperCamelCase_											=							self._batch_csv((offset, header, index, to_csv_kwargs)  )
                      written += file_obj.write(__UpperCamelCase  )

        else:
               UpperCamelCase_							,					UpperCamelCase_											=							len(self.dataset  ), self.batch_size
               with multiprocessing.Pool(self.num_proc  ) as pool:
                      for csv_str in logging.tqdm(
                          pool.imap(
                              self._batch_csv  ,						[(offset, header, index, to_csv_kwargs) for offset in range(0  ,						__UpperCamelCase  ,						__UpperCamelCase  )]  ,						)  ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                             written += file_obj.write(__UpperCamelCase  )

        return written



