


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

import pytest
from parameterized import parameterized, 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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]	)
class 	__lowercase					(	unittest.TestCase	):





      def 							_lowercase					(		self    :       int							)     -> List[Any]:






         """simple docstring"""







         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 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Dict							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  {
             """enabled""": True,
             """processes_per_host""": 8,
         }
         UpperCAmelCase      					=  {
             """enabled""": True,
             """parameters""": {
                 """microbatches""": 4,
                 """placement_strategy""": """spread""",
                 """pipeline""": """interleaved""",
                 """optimize""": """speed""",
                 """partitions""": 4,
                 """ddp""": True,
             },
         }

         UpperCAmelCase      					=  {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

         UpperCAmelCase      					=  """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
         # 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}-{instance_count}-smp-{name_extension}"""       ,							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,
                 """max_steps""": 5_0_0,
             }       ,							metric_definitions=self.env.metric_definitions       ,							distribution=__lowerCamelCase       ,							py_version="""py36"""       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       str							)     -> Optional[int]:






         """simple docstring"""







         TrainingJobAnalytics(__lowerCamelCase							).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""							)





      @parameterized.expand([(1,)]							)
      def 							_lowercase					(		self    :       Dict       ,							__lowerCamelCase    :       int							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.create_estimator(__lowerCamelCase							)

         # run training
         estimator.fit()

         # result dataframe
         UpperCAmelCase      					=  TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

         # extract kpis
         UpperCAmelCase      					=  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]							)
         UpperCAmelCase      					=  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]							)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         UpperCAmelCase      					=  (
             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							)







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

      return sequence[n]
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  2

   while digits < n:
      index += 1
      UpperCAmelCase      					=  len(str(fibonacci(lowerCAmelCase_ ) ) )

   return index
def 	_UpperCamelCase					(    lowerCAmelCase_ = 1_0_0_0 )    ->int:
   return fibonacci_digits_index(lowerCAmelCase_ )


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



