





from __future__ import annotations

from collections.abc import MutableSequence



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Any ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		MutableSequence[float]					)							->						None:
     if len(lowerCAmelCase_					) != degree + 1:
        raise ValueError(
            'The number of coefficients should be equal to the degree + 1.'					)

     __lowerCAmelCase							     =     list(lowerCAmelCase_					)
     __lowerCAmelCase							     =     degree






  def __add__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Polynomial					)							->						Polynomial:

     if self.degree > polynomial_a.degree:
        __lowerCAmelCase							     =     self.coefficients[:]
        for i in range(polynomial_a.degree + 1					):
           coefficients[i] += polynomial_a.coefficients[i]
        return Polynomial(self.degree ,  lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     polynomial_a.coefficients[:]
        for i in range(self.degree + 1					):
           coefficients[i] += self.coefficients[i]
        return Polynomial(polynomial_a.degree ,  lowerCAmelCase_					)






  def __sub__(    self			:		Any ,  lowerCAmelCase_			:		Polynomial					)							->						Polynomial:
     return self + polynomial_a * Polynomial(0 ,  [-1]					)






  def __neg__(    self			:		List[str]					)							->						Polynomial:
     return Polynomial(self.degree ,  [-c for c in self.coefficients]					)






  def __mul__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Polynomial					)							->						Polynomial:
     __lowerCAmelCase							     =     [0] * (self.degree + polynomial_a.degree + 1)
     for i in range(self.degree + 1					):
        for j in range(polynomial_a.degree + 1					):
           coefficients[i + j] += (
               self.coefficients[i] * polynomial_a.coefficients[j]
           )

     return Polynomial(self.degree + polynomial_a.degree ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		int | float					)							->						int | float:
     __lowerCAmelCase							     =     0
     for i in range(self.degree + 1					):
        result += self.coefficients[i] * (substitution**i)
     return result






  def __str__(    self			:		List[str]					)							->						str:
     __lowerCAmelCase							     =     ''
     for i in range(self.degree ,  -1 ,  -1					):
        if self.coefficients[i] == 0:
           continue
        elif self.coefficients[i] > 0:
           if polynomial:
              polynomial += " + "
        else:
           polynomial += " - "

        if i == 0:
           polynomial += str(abs(self.coefficients[i]					)					)
        elif i == 1:
           polynomial += str(abs(self.coefficients[i]					)					) + "x"
        else:
           polynomial += str(abs(self.coefficients[i]					)					) + "x^" + str(lowerCAmelCase_					)

     return polynomial






  def __repr__(    self			:		Union[str, Any]					)							->						str:
     return self.__str__()






  def     lowercase		(    self			:		int					)							->						Polynomial:
     __lowerCAmelCase							     =     [0] * self.degree
     for i in range(self.degree					):
        __lowerCAmelCase							     =     self.coefficients[i + 1] * (i + 1)
     return Polynomial(self.degree - 1 ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		int | float = 0					)							->						Polynomial:
     __lowerCAmelCase							     =     [0] * (self.degree + 2)
     __lowerCAmelCase							     =     constant
     for i in range(self.degree + 1					):
        __lowerCAmelCase							     =     self.coefficients[i] / (i + 1)
     return Polynomial(self.degree + 1 ,  lowerCAmelCase_					)






  def __eq__(    self			:		Tuple ,  lowerCAmelCase_			:		object					)							->						bool:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return False

     if self.degree != polynomial_a.degree:
        return False

     for i in range(self.degree + 1					):
        if self.coefficients[i] != polynomial_a.coefficients[i]:
           return False

     return True






  def __ne__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		object					)							->						bool:
     return not self.__eq__(lowerCAmelCase_					)








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   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     False
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     36.4519
      __lowerCAmelCase							     =     0.90_3421
      __lowerCAmelCase							     =     222.088
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     0.76_3141

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   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(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  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.'
     )
     _snake_case  :   Optional[Any]						   =  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,
     )

