



"""simple docstring"""






def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  if len(UpperCamelCase__    ) == 0 or not all(len(UpperCamelCase__    ) == 2 for pair in coordinates    ):
    raise ValueError('''The program cannot work out a fitting polynomial.'''    )

  if len({tuple(UpperCamelCase__    ) for pair in coordinates}    ) != len(UpperCamelCase__    ):
    raise ValueError('''The program cannot work out a fitting polynomial.'''    )

  _SCREAMING_SNAKE_CASE        =			{x for x, _ in coordinates}
  if len(UpperCamelCase__    ) == 1:
    return F'''x={coordinates[0][0]}'''

  if len(UpperCamelCase__    ) != len(UpperCamelCase__    ):
    raise ValueError('''The program cannot work out a fitting polynomial.'''    )

  _SCREAMING_SNAKE_CASE        =			len(UpperCamelCase__    )

  _SCREAMING_SNAKE_CASE        =			0
  _SCREAMING_SNAKE_CASE        =			[]
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    _SCREAMING_SNAKE_CASE        =			0
    _SCREAMING_SNAKE_CASE        =			coordinates[count_of_line][0]
    _SCREAMING_SNAKE_CASE        =			[]
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))    )
      count_in_line += 1
    matrix.append(UpperCamelCase__    )
    count_of_line += 1

  _SCREAMING_SNAKE_CASE        =			0
  # put the y values into a vector
  _SCREAMING_SNAKE_CASE        =			[]
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]    )
    count_of_line += 1

  _SCREAMING_SNAKE_CASE        =			0

  while count < x:
    _SCREAMING_SNAKE_CASE        =			0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      _SCREAMING_SNAKE_CASE        =			matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]    ):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  _SCREAMING_SNAKE_CASE        =			0
  # make solutions
  _SCREAMING_SNAKE_CASE        =			[]
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]    )    )
    count += 1

  _SCREAMING_SNAKE_CASE        =			0
  _SCREAMING_SNAKE_CASE        =			'''f(x)='''

  while count < x:
    _SCREAMING_SNAKE_CASE        =			solution[count].split('''E'''    )
    if len(UpperCamelCase__    ) > 1:
      _SCREAMING_SNAKE_CASE        =			F'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += F'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))





"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

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

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
