





import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase       :   Optional[Any]				       =							logging.get_logger(__name__)







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    str:
  __snake_case:						int   =						UniSpeechSatForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__					,					config=SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]   =						downstream_dict["""projector.weight"""]
  __snake_case:						Optional[int]   =						downstream_dict["""projector.bias"""]
  __snake_case:						List[str]   =						downstream_dict["""model.post_net.linear.weight"""]
  __snake_case:						Optional[Any]   =						downstream_dict["""model.post_net.linear.bias"""]
  return model







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    int:
  __snake_case:						List[Any]   =						UniSpeechSatForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__					,					config=SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]   =						downstream_dict["""model.linear.weight"""]
  __snake_case:						List[Any]   =						downstream_dict["""model.linear.bias"""]
  return model







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

  __snake_case:						Union[str, Any]   =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  __snake_case:						Any   =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  __snake_case:						str   =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  __snake_case:						List[Any]   =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  __snake_case:						Any   =						downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
  __snake_case:						Tuple   =						torch.load(SCREAMING_SNAKE_CASE__					,					map_location="""cpu""")

  __snake_case:						List[Any]   =						checkpoint["""Downstream"""]

  __snake_case:						List[Any]   =						UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[int]   =						WavaVecaFeatureExtractor.from_pretrained(
      SCREAMING_SNAKE_CASE__					,					return_attention_mask=SCREAMING_SNAKE_CASE__					,					do_normalize=SCREAMING_SNAKE_CASE__)

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

  if hf_config.use_weighted_layer_sum:
    __snake_case:						Union[str, Any]   =						checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__)
  hf_model.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase       :   List[str]				       =							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       :   Tuple				       =							parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase       :   Union[str, Any]				       =							{}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase       :   str				       =							["BartphoTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bartpho import BartphoTokenizer

else:
      import sys

      __UpperCAmelCase       :   Optional[Any]				       =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






