




"""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 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       int				      =       CanineConfig()
						snake_case__							:       Union[str, Any]				      =       CanineModel(SCREAMING_SNAKE_CASE__		)
						model.eval()

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

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

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

						# Save tokenizer files
						snake_case__							:       Any				      =       CanineTokenizer()
						print(f'''Save tokenizer files to {pytorch_dump_path}'''		)
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__		)


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






def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=int((lower_bound + upper_bound) // 2							)
     if array[r] == value:
          return r
     if lower_bound >= upper_bound:
          return -1
     if array[r] < value:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	r + 1     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
     else:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	r - 1     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   list							):
     __UpperCamelCase       				=0
     if matrix[index][0] == value:
          return [index, 0]
     while index < len(SCREAMING_SNAKE_CASE__							) and matrix[index][0] < value:
          __UpperCamelCase       				=binary_search(matrix[index]     ,	0     ,	len(matrix[index]							) - 1     ,	SCREAMING_SNAKE_CASE__							)
          if r != -1:
               return [index, r]
          index += 1
     return [-1, -1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





