class     _lowercase					:


 '''simple docstring'''






 def __init__(				self       :     List[str]							)  ->   List[str]:
  __lowerCAmelCase    							=						[0, 1]





 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     Any							)  ->   Any:


  if (difference := index - (len(self.sequence							) - 2)) >= 1:
   for _ in range(snake_case__							):
    self.sequence.append(self.sequence[-1] + self.sequence[-2]							)
  return self.sequence[:index]







def 					UpperCamelCase_						( ) ->					Optional[int]:






 '''simple docstring'''

 print(
     """Fibonacci Series Using Dynamic Programming\n"""						,		"""Enter the index of the Fibonacci number you want to calculate """						,		"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""						,		sep=""""""						,		)
 __lowerCAmelCase    							=						Fibonacci()

 while True:
  __lowerCAmelCase    							=						input(""">> """				)
  if prompt in {"exit", "quit"}:
   break

  try:
   __lowerCAmelCase    							=						int(_lowerCAmelCase				)
  except ValueError:
   print("""Enter a number or 'exit'"""				)
   continue

  print(fibonacci.get(_lowerCAmelCase				)				)


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 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

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

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

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

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


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






