





from __future__ import annotations

from collections.abc import Callable

_snake_case  :   Optional[Any]						   =  list[list[float | int]]

def   a_   (			lowerCAmelCase_				:      Matrix,     lowerCAmelCase_				:      Matrix     ):
   __lowerCAmelCase							     =     len(lowerCAmelCase_     )
   __lowerCAmelCase							     =     [[0 for _ in range(size + 1     )] for _ in range(lowerCAmelCase_     )]
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42

   for row in range(lowerCAmelCase_     ):
      for col in range(lowerCAmelCase_     ):
         __lowerCAmelCase							     =     matrix[row][col]

      __lowerCAmelCase							     =     vector[row][0]

   __lowerCAmelCase							     =     0
   __lowerCAmelCase							     =     0
   while row < size and col < size:
      # pivoting
      __lowerCAmelCase							     =     max((abs(augmented[rowa][col]     ), rowa) for rowa in range(lowerCAmelCase_,     lowerCAmelCase_     )     )[
          1
      ]
      if augmented[pivot_row][col] == 0:
         col += 1
         continue
      else:
         __lowerCAmelCase     ,     __lowerCAmelCase							     =     augmented[pivot_row], augmented[row]

      for rowa in range(row + 1,     lowerCAmelCase_     ):
         __lowerCAmelCase							     =     augmented[rowa][col] / augmented[row][col]
         __lowerCAmelCase							     =     0
         for cola in range(col + 1,     size + 1     ):
            augmented[rowa][cola] -= augmented[row][cola] * ratio

      row += 1
      col += 1

   # back substitution
   for col in range(1,     lowerCAmelCase_     ):
      for row in range(lowerCAmelCase_     ):
         __lowerCAmelCase							     =     augmented[row][col] / augmented[col][col]
         for cola in range(lowerCAmelCase_,     size + 1     ):
            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
   return [
       [round(augmented[row][size] / augmented[row][row],     10     )] for row in range(lowerCAmelCase_     )
   ]

def   a_   (			lowerCAmelCase_				:      list[int]     ):
   __lowerCAmelCase							     =     len(lowerCAmelCase_     )
   __lowerCAmelCase							     =     [[0 for _ in range(lowerCAmelCase_     )] for _ in range(lowerCAmelCase_     )]
   __lowerCAmelCase							     =     [[0] for _ in range(lowerCAmelCase_     )]
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42

   for x_val, y_val in enumerate(lowerCAmelCase_     ):
      for col in range(lowerCAmelCase_     ):
         __lowerCAmelCase							     =     (x_val + 1) ** (size - col - 1)
      __lowerCAmelCase							     =     y_val

   __lowerCAmelCase							     =     solve(lowerCAmelCase_,     lowerCAmelCase_     )

   def interpolated_func(lowerCAmelCase_				:      int     ) -> int:
      return sum(
          round(coeffs[x_val][0]     ) * (var ** (size - x_val - 1))
          for x_val in range(lowerCAmelCase_     )     )

   return interpolated_func

def   a_   (			lowerCAmelCase_				:      int     ):
   return (
       1
       - variable
       + variable**2
       - variable**3
       + variable**4
       - variable**5
       + variable**6
       - variable**7
       + variable**8
       - variable**9
       + variable**10
   )

def   a_   (			lowerCAmelCase_				:      Callable[[int], int] = question_function,     lowerCAmelCase_				:      int = 10     ):
   __lowerCAmelCase							     =     [func(lowerCAmelCase_     ) for x_val in range(1,     order + 1     )]

   __lowerCAmelCase							     =     [
       interpolate(data_points[:max_coeff]     ) for max_coeff in range(1,     order + 1     )
   ]

   __lowerCAmelCase							     =     0
   __lowerCAmelCase							     =     42
   __lowerCAmelCase							     =     42

   for poly in polynomials:
      __lowerCAmelCase							     =     1
      while func(lowerCAmelCase_     ) == poly(lowerCAmelCase_     ):
         x_val += 1

      ret += poly(lowerCAmelCase_     )

   return ret


if __name__ == "__main__":
 print(F"""{solution() = }""")








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,
     )

