

"""simple docstring"""






import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast






@dataclass
class      _UpperCAmelCase      (		datasets.BuilderConfig):
			__a			:							Optional[datasets.Features]					       =				None






class      _UpperCAmelCase      (		datasets.ArrowBasedBuilder):
			__a			:							int					       =				PandasConfig
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				return datasets.DatasetInfo(features=self.config.features  )
			def 				__snake_case			(		self   ,		_A  )     ->     Tuple:






				'''simple docstring'''







				if not self.config.data_files:
					raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''  )
				_UpperCAmelCase     :				Union[str, Any]          =       dl_manager.download_and_extract(self.config.data_files  )
				if isinstance(_A   ,		(str, list, tuple)  ):
					_UpperCAmelCase     :				Dict          =       data_files
					if isinstance(_A   ,		_A  ):
						_UpperCAmelCase     :				List[Any]          =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					_UpperCAmelCase     :				Optional[int]          =       [dl_manager.iter_files(_A  ) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,		gen_kwargs={"""files""": files}  )]
				_UpperCAmelCase     :				Optional[Any]          =       []
				for split_name, files in data_files.items():
					if isinstance(_A   ,		_A  ):
						_UpperCAmelCase     :				int          =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					_UpperCAmelCase     :				Dict          =       [dl_manager.iter_files(_A  ) for file in files]
					splits.append(datasets.SplitGenerator(name=_A   ,		gen_kwargs={"""files""": files}  )  )
				return splits
			def 				__snake_case			(		self   ,		_A  )     ->     List[str]:






				'''simple docstring'''







				if self.config.features is not None:
					# more expensive cast to support nested features with keys in a different order
					# allows str <-> int/float or str to Audio for example
					_UpperCAmelCase     :				Tuple          =       table_cast(_A   ,		self.config.features.arrow_schema  )
				return pa_table
			def 				__snake_case			(		self   ,		_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				for i, file in enumerate(itertools.chain.from_iterable(_A  )  ):
					with open(_A   ,		"""rb"""  ) as f:
						_UpperCAmelCase     :				int          =       pa.Table.from_pandas(pd.read_pickle(_A  )  )
						yield i, self._cast_table(_A  )













'''simple docstring'''
import argparse

import torch

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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


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




