




'''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 						snake_case__    ( lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Optional[Any]			)   ->   List[str]:
					# Initialize PyTorch model
					A_ :     Dict =							CanineConfig()
					A_ :     Optional[int] =							CanineModel(lowerCamelCase__			)
					model.eval()

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

					# Load weights from tf checkpoint
					load_tf_weights_in_canine(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
					A_ :     str =							CanineTokenizer()
					print(f'Save tokenizer files to {pytorch_dump_path}'			)
					tokenizer.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
							snake_case__          =						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.""",
							)
							snake_case__          =						parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   int			)   ->   bool:
					if not isinstance(lowerCamelCase__			,		lowerCamelCase__			):
										A_ :     int =							f'Input value of [number={number}] must be an integer'
										raise TypeError(lowerCamelCase__			)
					if number < 0 or number % 2 == 1:
										return False
					A_ :     Dict =							int(number**0.5			)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



