




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_				:			int   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		str:

       """simple docstring"""

       a_ :		Dict							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Union[str, Any]							=  parser.parse_args()
       return args.f
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      None:
             a_ :		List[str]							=  logging.StreamHandler(sys.stdout					)
             logger.addHandler(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      Any:
             a_ :		List[Any]							=  get_gpu_count()

             if n_gpu > 1:
                    pass
                    # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
                    # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
                    # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
                    # cmd = [sys.executable] + distributed_args + args
                    # execute_subprocess_async(cmd, env=self.get_env())
                    # XXX: test the results - need to save them first into .json file
             else:
                    args.insert(0					,							'run_glue_deebert.py'					)
                    with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                           a_ :		Optional[Any]							=  run_glue_deebert.main()
                           for value in result.values():
                                  self.assertGreaterEqual(SCREAMING_SNAKE_CASE__					,							0.666					)

      @slow
      @require_torch_non_multi_gpu
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[str]:
             a_ :		Optional[int]							=  '\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
             self.run_and_check(SCREAMING_SNAKE_CASE__					)

             a_ :		Union[str, Any]							=  '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
             self.run_and_check(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[Any]							=  '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
             self.run_and_check(SCREAMING_SNAKE_CASE__					)






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       if n == 1 or not isinstance(__A  ,   __A    ):
              return 0
       elif n == 2:
              return 1
       else:
              a_ :		int							=  [0, 1]
              for i in range(2  ,   n + 1    ):
                     sequence.append(sequence[i - 1] + sequence[i - 2]    )

              return sequence[n]







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       a_ :		Any							=  0
       a_ :		Optional[Any]							=  2

       while digits < n:
              index += 1
              a_ :		List[Any]							=  len(str(fibonacci(__A    )    )    )

       return index







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int = 10_00    )     ->		int:

       """simple docstring"""

       return fibonacci_digits_index(__A    )


if __name__ == "__main__":
   print(solution(int(str(input()).strip())))


