


def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Optional[int]							= 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(__lowercase      ,							r + 1      ,							__lowercase      ,							__lowercase							)
   else:
      return binary_search(__lowercase      ,							__lowercase      ,							r - 1      ,							__lowercase							)
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Union[str, Any]							= 0
   if matrix[index][0] == value:
      return [index, 0]
   while index < len(__lowercase							) and matrix[index][0] < value:
      UpperCAmelCase_				:   str							= binary_search(matrix[index]      ,							0      ,							len(matrix[index]							) - 1      ,							__lowercase							)
      if r != -1:
         return [index, r]
      index += 1
   return [-1, -1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()



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_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   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					:	int          =				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					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)