





"""simple docstring"""
from __future__ import annotations

from typing import Any



def 					__lowerCamelCase       (							a_					:		list							)      ->				int:






      if not postfix_notation:
            return 0

      __SCREAMING_SNAKE_CASE       :Dict		    =  {"+", "-", "*", "/"}
      __SCREAMING_SNAKE_CASE       :list[Any]		    =  []

      for token in postfix_notation:
            if token in operations:
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  stack.pop(), stack.pop()
                  if token == "+":
                        stack.append(a + b							)
                  elif token == "-":
                        stack.append(a - b							)
                  elif token == "*":
                        stack.append(a * b							)
                  else:
                        if a * b < 0 and a % b != 0:
                              stack.append(a // b + 1							)
                        else:
                              stack.append(a // b							)
            else:
                  stack.append(int(_lowercase							)							)

      return stack.pop()


if __name__ == "__main__":
  import doctest

  doctest.testmod()






"""simple docstring"""
import argparse

import torch

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


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



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[int]			,       a_					:		Any							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForSequenceClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict['''projector.weight''']
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  downstream_dict['''projector.bias''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.post_net.linear.weight''']
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.post_net.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		List[Any]			,       a_					:		List[Any]							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForAudioFrameClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.linear.weight''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[Any]			,       a_					:		int							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  UniSpeechSatForXVector.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''connector.weight''']
      __SCREAMING_SNAKE_CASE       :Tuple		    =  downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      __SCREAMING_SNAKE_CASE       :Any		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      __SCREAMING_SNAKE_CASE       :Dict		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Union[str, Any]			,       a_					:		Any			,       a_					:		Union[str, Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  torch.load(a_			,       map_location='''cpu'''							)

      __SCREAMING_SNAKE_CASE       :str		    =  checkpoint['''Downstream''']

      __SCREAMING_SNAKE_CASE       :str		    =  UniSpeechSatConfig.from_pretrained(a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  WavaVecaFeatureExtractor.from_pretrained(
          a_			,       return_attention_mask=a_			,       do_normalize=a_							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification'''							):
            __SCREAMING_SNAKE_CASE       :str		    =  convert_classification(a_			,       a_			,       a_							)
      elif arch.endswith('''ForAudioFrameClassification'''							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  convert_diarization(a_			,       a_			,       a_							)
      elif arch.endswith('''ForXVector'''							):
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  convert_xvector(a_			,       a_			,       a_							)
      else:
            raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''							)

      if hf_config.use_weighted_layer_sum:
            __SCREAMING_SNAKE_CASE       :Dict		    =  checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(a_							)
      hf_model.save_pretrained(a_							)


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