






'''simple docstring'''



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 							lowercase_				(    __A :       Tuple					,      __A :       Optional[Any]					,      __A :       Tuple					,      __A :       Tuple					,      __A :       str			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   Tuple				  =TapasConfig.from_json_file(__A			)
 # set absolute/relative position embeddings parameter
 lowercase					:   List[str]				  =reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  lowercase					:   int				  =TapasForQuestionAnswering(config=__A			)
 elif task == "WTQ":
  # run_task_main.py hparams
  lowercase					:   Union[str, Any]				  =4
  lowercase					:   List[Any]				  =True
  # hparam_utils.py hparams
  lowercase					:   Optional[Any]				  =0.664694
  lowercase					:   str				  =0.207951
  lowercase					:   Tuple				  =0.121194
  lowercase					:   Union[str, Any]				  =True
  lowercase					:   Union[str, Any]				  =True
  lowercase					:   Union[str, Any]				  =False
  lowercase					:   List[Any]				  =0.0352513

  lowercase					:   Any				  =TapasForQuestionAnswering(config=__A			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  lowercase					:   List[str]				  =4
  lowercase					:   Tuple				  =False
  # hparam_utils.py hparams
  lowercase					:   Tuple				  =36.4519
  lowercase					:   Any				  =0.903421
  lowercase					:   List[str]				  =222.088
  lowercase					:   List[Any]				  =True
  lowercase					:   Optional[int]				  =True
  lowercase					:   List[str]				  =True
  lowercase					:   List[str]				  =0.763141

  lowercase					:   Tuple				  =TapasForQuestionAnswering(config=__A			)
 elif task == "TABFACT":
  lowercase					:   Any				  =TapasForSequenceClassification(config=__A			)
 elif task == "MLM":
  lowercase					:   int				  =TapasForMaskedLM(config=__A			)
 elif task == "INTERMEDIATE_PRETRAINING":
  lowercase					:   Optional[int]				  =TapasModel(config=__A			)
 else:
  raise ValueError(F'Task {task} not supported.'			)

 print(F'Building PyTorch model from configuration: {config}'			)
 # Load weights from tf checkpoint
 load_tf_weights_in_tapas(__A					,      __A					,      __A			)

 # Save pytorch-model (weights and configuration)
 print(F'Save PyTorch model to {pytorch_dump_path}'			)
 model.save_pretrained(__A			)

 # Save tokenizer files
 print(F'Save tokenizer files to {pytorch_dump_path}'			)
 lowercase					:   Tuple				  =TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt'''					,      model_max_length=5_1_2			)
 tokenizer.save_pretrained(__A			)

 print('''Used relative position embeddings:'''					,      model.config.reset_position_index_per_cell			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
      )
      parser.add_argument(
          '--reset_position_index_per_cell',
          default=False,
          action='store_true',
          help='Whether to use relative position embeddings or not. Defaults to True.',
      )
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--tapas_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained TAPAS model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.task,
          args.reset_position_index_per_cell,
          args.tf_checkpoint_path,
          args.tapas_config_file,
          args.pytorch_dump_path,
      )











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




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

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

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


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



