





"""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_       (							_lowercase ,		_lowercase     ):
						# Initialize PyTorch model
						_UpperCamelCase    :      Union[str, Any]						     =					CanineConfig()
						_UpperCamelCase    :      List[Any]						     =					CanineModel(_lowercase     )
						model.eval()

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

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

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

						# Save tokenizer files
						_UpperCamelCase    :      List[Any]						     =					CanineTokenizer()
						print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
						tokenizer.save_pretrained(_lowercase     )


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










"""simple docstring"""




def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						return len_board * len_board_column * row + column





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		):

						if board[row][column] != word[word_index]:
												return False

						if word_index == len(_lowercase     ) - 1:
												return True

						_UpperCamelCase    :      Union[str, Any]						     =					[(0, 1), (0, -1), (-1, 0), (1, 0)]
						_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
						_UpperCamelCase    :      Optional[Any]						     =					len(board[0]     )
						for direction in traverts_directions:
												_UpperCamelCase    :      Union[str, Any]						     =					row + direction[0]
												_UpperCamelCase    :      List[Any]						     =					column + direction[1]
												if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
																		continue

												_UpperCamelCase    :      Optional[Any]						     =					get_point_key(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
												if key in visited_points_set:
																		continue

												visited_points_set.add(_lowercase     )
												if exits_word(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		word_index + 1 ,		_lowercase     ):
																		return True

												visited_points_set.remove(_lowercase     )

						return False





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Optional[Any]						     =					(
						    '''The board should be a non empty matrix of single chars strings.'''
						)

						_UpperCamelCase    :      int						     =					len(_lowercase     )
						if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
												raise ValueError(_lowercase     )

						for row in board:
												if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
																		raise ValueError(_lowercase     )

												for item in row:
																		if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) != 1:
																								raise ValueError(_lowercase     )

    # Validate word
						if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
												raise ValueError(
												    '''The word parameter should be a string of length greater than 0.'''     )

						_UpperCamelCase    :      List[str]						     =					len(board[0]     )
						for i in range(_lowercase     ):
												for j in range(_lowercase     ):
																		if exits_word(
																		    _lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		0 ,		{get_point_key(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )}     ):
																								return True

						return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



