
"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

a         =      list[list[float | int]]






def 						_snake_case					( _snake_case							: Matrix    ,  _snake_case							: Matrix							)   -> Tuple:






      '''simple docstring'''


      _A        =       len(__a							)
      _A        =       [[0 for _ in range(size + 1							)] for _ in range(__a							)]
      _A        =       42
      _A        =       42
      _A        =       42
      _A        =       42
      _A        =       42
      _A        =       42

      for row in range(__a							):
            for col in range(__a							):
                  _A        =       matrix[row][col]

            _A        =       vector[row][0]

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

            for rowa in range(row + 1    ,  __a							):
                  _A        =       augmented[rowa][col] / augmented[row][col]
                  _A        =       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    ,  __a							):
            for row in range(__a							):
                  _A        =       augmented[row][col] / augmented[col][col]
                  for cola in range(__a    ,  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(__a							)
      ]






def 						_snake_case					( _snake_case							: list[int]							)   -> int:






      '''simple docstring'''


      _A        =       len(__a							)
      _A        =       [[0 for _ in range(__a							)] for _ in range(__a							)]
      _A        =       [[0] for _ in range(__a							)]
      _A        =       42
      _A        =       42
      _A        =       42
      _A        =       42

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

      _A        =       solve(__a    ,  __a							)

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

      return interpolated_func






def 						_snake_case					( _snake_case							: int							)   -> Tuple:






      '''simple docstring'''


      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**10
      )






def 						_snake_case					( _snake_case							: Callable[[int], int] = question_function    ,  _snake_case							: int = 10							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       [func(__a							) for x_val in range(1    ,  order + 1							)]

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

      _A        =       0
      _A        =       42
      _A        =       42

      for poly in polynomials:
            _A        =       1
            while func(__a							) == poly(__a							):
                  x_val += 1

            ret += poly(__a							)

      return ret


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







"""simple docstring"""

import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


a         =      {
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       XLNetConfig.from_json_file(_snake_case							)

      _A        =       finetuning_task.lower() if finetuning_task is not None else ''
      if finetuning_task in GLUE_TASKS_NUM_LABELS:
            print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''							)
            _A        =       finetuning_task
            _A        =       GLUE_TASKS_NUM_LABELS[finetuning_task]
            _A        =       XLNetForSequenceClassification(_snake_case							)
      elif "squad" in finetuning_task:
            _A        =       finetuning_task
            _A        =       XLNetForQuestionAnswering(_snake_case							)
      else:
            _A        =       XLNetLMHeadModel(_snake_case							)

      # Load weights from tf checkpoint
      load_tf_weights_in_xlnet(_snake_case    ,  _snake_case    ,  _snake_case							)

      # Save pytorch-model
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      print(F'''Save PyTorch model to {os.path.abspath(_snake_case							)}'''							)
      torch.save(model.state_dict()    ,  _snake_case							)
      print(F'''Save configuration file to {os.path.abspath(_snake_case							)}'''							)
      with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
            f.write(config.to_json_string()							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--xlnet_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained XLNet model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--finetuning_task''',
           default=None,
           type=str,
           help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
       )
       a         =      parser.parse_args()
       print(args)

       convert_xlnet_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
       )

