
'''simple docstring'''






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.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ] )
class  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       Tuple     ):
          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=__A,)
          assert hasattr(self,"env"     )



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       int     ):
          _lowerCamelCase				:   Union[str, Any]								=   f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
          # distributed data settings
          _lowerCamelCase				:   Tuple								=   {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

          # 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=__A,instance_count=__A,instance_type=self.instance_type,debugger_hook_config=__A,hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path},metric_definitions=self.env.metric_definitions,distribution=__A,py_version="py36",)



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Optional[Any]     ):
          TrainingJobAnalytics(__A     ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'     )



    @parameterized.expand([(2,)]     )
    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Dict     ):
          # create estimator
          _lowerCamelCase				:   Any								=   self.create_estimator(__A     )

          # run training
          estimator.fit()

          # result dataframe
          _lowerCamelCase				:   List[str]								=   TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

          # extract kpis
          _lowerCamelCase				:   Optional[int]								=   list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
          _lowerCamelCase				:   Optional[int]								=   list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          _lowerCamelCase				:   Union[str, Any]								=   (
              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},__A     )


'''simple docstring'''






import math







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   math.loga(math.sqrt(4 * positive_integer + 1							) / 2 + 1 / 2							)

      return exponent == int(_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    float = 1 / 12345							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   int								=   0

      _lowerCamelCase				:   List[str]								=   3
      while True:
            _lowerCamelCase				:   List[Any]								=   (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(_lowerCAmelCase							):
                  _lowerCamelCase				:   Any								=   int(_lowerCAmelCase							)
                  total_partitions += 1
                  if check_partition_perfect(_lowerCAmelCase							):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(_lowerCAmelCase							)
            integer += 1


if __name__ == "__main__":
 print(f'''{solution() = }''')