

from __future__ import annotations

from collections.abc import MutableSequence





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if len(lowerCAmelCase      ) != degree + 1:
              raise ValueError(
                  'The number of coefficients should be equal to the degree + 1.'      )

        _lowercase			 =list(lowerCAmelCase      )
        _lowercase			 =degree




  def __add__(       self							,     lowerCAmelCase      )						->				Polynomial:
        '''simple docstring'''






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




  def __sub__(       self							,     lowerCAmelCase      )						->				Polynomial:
        '''simple docstring'''






        return self + polynomial_a * Polynomial(0							,     [-1]      )




  def __neg__(       self      )						->				Polynomial:
        '''simple docstring'''






        return Polynomial(self.degree							,     [-c for c in self.coefficients]      )




  def __mul__(       self							,     lowerCAmelCase      )						->				Polynomial:
        '''simple docstring'''






        _lowercase			 =[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 						A__  (       self							,     lowerCAmelCase      )						->				int | float:
        '''simple docstring'''






        _lowercase			 =0
        for i in range(self.degree + 1      ):
              result += self.coefficients[i] * (substitution**i)
        return result




  def __str__(       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =''
        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      )						->				str:
        '''simple docstring'''






        return self.__str__()




  def 						A__  (       self      )						->				Polynomial:
        '''simple docstring'''






        _lowercase			 =[0] * self.degree
        for i in range(self.degree      ):
              _lowercase			 =self.coefficients[i + 1] * (i + 1)
        return Polynomial(self.degree - 1							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase = 0      )						->				Polynomial:
        '''simple docstring'''






        _lowercase			 =[0] * (self.degree + 2)
        _lowercase			 =constant
        for i in range(self.degree + 1      ):
              _lowercase			 =self.coefficients[i] / (i + 1)
        return Polynomial(self.degree + 1							,     lowerCAmelCase      )




  def __eq__(       self							,     lowerCAmelCase      )						->				bool:
        '''simple docstring'''






        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							,     lowerCAmelCase      )						->				bool:
        '''simple docstring'''






        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   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =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			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

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


if __name__ == "__main__":
  lowercase_             =     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.'
  )
  lowercase_             =     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,
  )


