


'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
A				:					Dict    						=			logging.get_logger(__name__)






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Any       ):
       _A					:     str     = WavaVecaForSequenceClassification.from_pretrained(lowerCamelCase  ,config=lowerCamelCase       )
       _A					:     Dict     = downstream_dict['projector.weight']
       _A					:     Tuple     = downstream_dict['projector.bias']
       _A					:     List[Any]     = downstream_dict['model.post_net.linear.weight']
       _A					:     List[Any]     = downstream_dict['model.post_net.linear.bias']
       return model






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      Dict  ,lowerCamelCase					:      str       ):
       _A					:     str     = WavaVecaForAudioFrameClassification.from_pretrained(lowerCamelCase  ,config=lowerCamelCase       )
       _A					:     Tuple     = downstream_dict['model.linear.weight']
       _A					:     str     = downstream_dict['model.linear.bias']
       return model






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      List[str]       ):
       _A					:     Tuple     = WavaVecaForXVector.from_pretrained(lowerCamelCase  ,config=lowerCamelCase       )
       _A					:     Tuple     = downstream_dict['connector.weight']
       _A					:     Optional[Any]     = downstream_dict['connector.bias']
       for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
              _A					:     Any     = downstream_dict[
                  F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              _A					:     Optional[Any]     = downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       _A					:     int     = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
       _A					:     List[Any]     = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
       _A					:     Optional[Any]     = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
       _A					:     Tuple     = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
       _A					:     Union[str, Any]     = downstream_dict['objective.W']
       return model






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Union[str, Any]       ):
       _A					:     Tuple     = torch.load(lowerCamelCase  ,map_location='cpu'       )

       _A					:     str     = checkpoint['Downstream']

       _A					:     Union[str, Any]     = WavaVecaConfig.from_pretrained(lowerCamelCase       )
       _A					:     Any     = WavaVecaFeatureExtractor.from_pretrained(
           lowerCamelCase  ,return_attention_mask=lowerCamelCase  ,do_normalize=lowerCamelCase       )

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

       if hf_config.use_weighted_layer_sum:
              _A					:     Union[str, Any]     = checkpoint['Featurizer']['weights']

       hf_feature_extractor.save_pretrained(lowerCamelCase       )
       hf_model.save_pretrained(lowerCamelCase       )


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










'''simple docstring'''







A				:					Dict    						=			'''Alexander Joslin'''

import operator as op

from .stack import Stack






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     Dict     = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

       _A					:     Stack[int]     = Stack()
       _A					:     Stack[str]     = Stack()

       for i in equation:
              if i.isdigit():
                     # RULE 1
                     operand_stack.push(int(lowerCamelCase       )       )
              elif i in operators:
                     # RULE 2
                     operator_stack.push(lowerCamelCase       )
              elif i == ")":
                     # RULE 4
                     _A					:     List[str]     = operator_stack.peek()
                     operator_stack.pop()
                     _A					:     Dict     = operand_stack.peek()
                     operand_stack.pop()
                     _A					:     Optional[int]     = operand_stack.peek()
                     operand_stack.pop()

                     _A					:     Dict     = operators[opr](lowerCamelCase  ,lowerCamelCase       )
                     operand_stack.push(lowerCamelCase       )

    # RULE 5
       return operand_stack.peek()


if __name__ == "__main__":
   A				:					Dict    						=			'''(5 + ((4 * 2) * (2 + 3)))'''
   # answer = 45
   print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






