






"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
A__					: List[Any]=	logging.get_logger(__name__)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE ,	config=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    downstream_dict['projector.weight']
    UpperCamelCase__         =    downstream_dict['projector.bias']
    UpperCamelCase__         =    downstream_dict['model.post_net.linear.weight']
    UpperCamelCase__         =    downstream_dict['model.post_net.linear.bias']
    return model







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE ,	config=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    downstream_dict['model.linear.weight']
    UpperCamelCase__         =    downstream_dict['model.linear.bias']
    return model







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE ,	config=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    downstream_dict['connector.weight']
    UpperCamelCase__         =    downstream_dict['connector.bias']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
        UpperCamelCase__         =    downstream_dict[
            F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
        ]
        UpperCamelCase__         =    downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
    UpperCamelCase__         =    downstream_dict['objective.W']
    return model







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Any:
    """simple docstring"""

    UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE ,	map_location='cpu'					)

    UpperCamelCase__         =    checkpoint['Downstream']

    UpperCamelCase__         =    WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    WavaVecaFeatureExtractor.from_pretrained(
        SCREAMING_SNAKE_CASE ,	return_attention_mask=SCREAMING_SNAKE_CASE ,	do_normalize=SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    hf_config.architectures[0]
    if arch.endswith('ForSequenceClassification'					):
        UpperCamelCase__         =    convert_classification(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    elif arch.endswith('ForAudioFrameClassification'					):
        UpperCamelCase__         =    convert_diarization(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    elif arch.endswith('ForXVector'					):
        UpperCamelCase__         =    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:
        UpperCamelCase__         =    checkpoint['Featurizer']['weights']

    hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE					)
    hf_model.save_pretrained(SCREAMING_SNAKE_CASE					)


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












"""simple docstring"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(SCREAMING_SNAKE_CASE					)					)
        elif i in operators:
            # RULE 2
            operator_stack.push(SCREAMING_SNAKE_CASE					)
        elif i == ")":
            # RULE 4
            UpperCamelCase__         =    operator_stack.peek()
            operator_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  A__					: int=	"""(5 + ((4 * 2) * (2 + 3)))"""
  # answer = 45
  print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")




