






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :Union[str, Any]	,							a :List[str]	,							a :List[str] )							->			int:
 a		  =      UniSpeechSatForSequenceClassification.from_pretrained(a	,							config=a )
 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 						_a    (      a :Any	,							a :str	,							a :List[Any] )							->			Tuple:
 a		  =      UniSpeechSatForAudioFrameClassification.from_pretrained(a	,							config=a )
 a		  =      downstream_dict['''model.linear.weight''']
 a		  =      downstream_dict['''model.linear.bias''']
 return model
def 						_a    (      a :Dict	,							a :int	,							a :Union[str, Any] )							->			Dict:
 a		  =      UniSpeechSatForXVector.from_pretrained(a	,							config=a )
 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 						_a    (      a :str	,							a :List[Any]	,							a :Optional[int]	,							a :Any )							->			str:
 a		  =      torch.load(a	,							map_location='''cpu''' )

 a		  =      checkpoint['''Downstream''']

 a		  =      UniSpeechSatConfig.from_pretrained(a )
 a		  =      WavaVecaFeatureExtractor.from_pretrained(
     a	,							return_attention_mask=a	,							do_normalize=a )

 a		  =      hf_config.architectures[0]
 if arch.endswith('''ForSequenceClassification''' ):
  a		  =      convert_classification(a	,							a	,							a )
 elif arch.endswith('''ForAudioFrameClassification''' ):
  a		  =      convert_diarization(a	,							a	,							a )
 elif arch.endswith('''ForXVector''' ):
  a		  =      convert_xvector(a	,							a	,							a )
 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(a )
 hf_model.save_pretrained(a )


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)






"""simple docstring"""




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='Translation'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def __call__(  self      ):
				return pa.struct({lang: pa.string() for lang in sorted(self.languages      )}      )





		def 			__UpperCAmelCase       (  self      ):
				from .features import Value

				return {k: Value('''string'''      ) for k in sorted(self.languages      )}







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[List]										=				None
		__UpperCAmelCase		:			Optional[int]										=				None
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='TranslationVariableLanguages'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			sorted(set(self.languages      )      ) if self.languages else None
				__a           =			len(self.languages      ) if self.languages else None

		def __call__(  self      ):
				return pa.struct({'''language''': pa.list_(pa.string()      ), '''translation''': pa.list_(pa.string()      )}      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			set(self.languages      )
				if self.languages and set(_a      ) - lang_set:
						raise ValueError(
						    f'''Some languages in example ({', '.join(sorted(set(_a      ) - lang_set      )      )}) are not in valid set ({', '.join(_a      )}).'''      )

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				__a           =			[]
				for lang, text in translation_dict.items():
						if isinstance(_a							, _a      ):
								translation_tuples.append((lang, text)      )
						else:
								translation_tuples.extend([(lang, el) for el in text]      )

        # Ensure translations are in ascending order by language code.
				__a      ,     __a           =			zip(*sorted(_a      )      )

				return {"language": languages, "translation": translations}





		def 			__UpperCAmelCase       (  self      ):
				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''      )      ),
				    "translation": Sequence(Value('''string'''      )      ),
				}






