



"""simple docstring"""






def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			1
  _SCREAMING_SNAKE_CASE        =			0
  _SCREAMING_SNAKE_CASE        =			1
  _SCREAMING_SNAKE_CASE        =			1
  _SCREAMING_SNAKE_CASE        =			3
  _SCREAMING_SNAKE_CASE        =			3
  _SCREAMING_SNAKE_CASE        =			limit
  _SCREAMING_SNAKE_CASE        =			0

  _SCREAMING_SNAKE_CASE        =			''''''
  while counter != decimal + 1:
    if 4 * q + r - t < n * t:
      result += str(UpperCamelCase__    )
      if counter == 0:
        result += "."

      if decimal == counter:
        break

      counter += 1
      _SCREAMING_SNAKE_CASE        =			10 * (r - n * t)
      _SCREAMING_SNAKE_CASE        =			((10 * (3 * q + r)) // t) - 10 * n
      q *= 10
      _SCREAMING_SNAKE_CASE        =			nr
    else:
      _SCREAMING_SNAKE_CASE        =			(2 * q + r) * l
      _SCREAMING_SNAKE_CASE        =			(q * (7 * k) + 2 + (r * l)) // (t * l)
      q *= k
      t *= l
      l += 2
      k += 1
      _SCREAMING_SNAKE_CASE        =			nn
      _SCREAMING_SNAKE_CASE        =			nr
  return result


def 							A__ (     ):


  '''simple docstring'''


  print(F'''{calculate_pi(50    ) = }'''    )
  import doctest

  doctest.testmod()


if __name__ == "__main__":
    main()





"""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)
