"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
				from sagemaker import Session, TrainingJobAnalytics
				from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""",   """False"""      )      ) is not True,   reason="""Skipping test because should only be run when releasing minor transformers version""",   )
@pytest.mark.usefixtures("""sm_env"""      )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]      )
class 	UpperCAmelCase__					(     unittest.TestCase      ):


	"""simple docstring"""







	def    A     (      self			)     ->						Optional[Any]:
							if self.framework == "pytorch":
													subprocess.run(
													    f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()							,  encoding="utf-8"							,  check=__lowerCAmelCase							,  )
							assert hasattr(self							,  "env"			)







	def    A     (      self							,  _SCREAMING_SNAKE_CASE=1			)     ->						int:
							# creates estimator
							return HuggingFace(
							    entry_point=self.script							,  source_dir=self.env.test_path							,  role=self.env.role							,  image_uri=self.env.image_uri							,  base_job_name=f'''{self.env.base_job_name}-single'''							,  instance_count=__lowerCAmelCase							,  instance_type=self.instance_type							,  debugger_hook_config=__lowerCAmelCase							,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}							,  metric_definitions=self.env.metric_definitions							,  py_version="py36"							,  )







	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						List[Any]:
							TrainingJobAnalytics(__lowerCAmelCase			).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''			)







	def    A     (      self			)     ->						Optional[int]:
							# create estimator
							a_				:      Union[str, Any]  		=       self.create_estimator()

							# run training
							estimator.fit()

							# result dataframe
							a_				:      Optional[int]  		=       TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

							# extract kpis
							a_				:      int  		=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
							a_				:      List[Any]  		=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							a_				:      str  		=       (
							    Session().describe_training_job(estimator.latest_training_job.name			).get("TrainingTimeInSeconds"							,  9_9_9_9_9_9			)
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy			)
							assert all(t <= self.results["eval_loss"] for t in eval_loss			)

							# dump tests result into json file to share in PR
							with open(f'''{estimator.latest_training_job.name}.json'''							,  "w"			) as outfile:
													json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}							,  __lowerCAmelCase			)












'''simple docstring'''

def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

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

            return sequence[n]



def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: Any            =				0
      UpperCamelCase_					: Union[str, Any]            =				2

      while digits < n:
            index += 1
            UpperCamelCase_					: Any            =				len(str(fibonacci(a_						)						)						)

      return index



def 				snake_case       (  a_				:			int = 1_000						)							->					int:





      """simple docstring"""

      return fibonacci_digits_index(a_						)


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






