


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 _snake_case       (						self					):






     """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 _snake_case       (						self		,							__A=1					):






     """simple docstring"""





     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 _snake_case       (						self		,							__A					):






     """simple docstring"""





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






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Optional[int]       =							self.create_estimator()

     # run training
     estimator.fit()

     # result dataframe
     lowerCamelCase					:   List[Any]       =							TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

     # extract kpis
     lowerCamelCase					:   List[str]       =							list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
     lowerCamelCase					:   Dict       =							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"		,							99_9999					)
     )

     # 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 	__a     (      A__			:  int = 1000       ):
    SCREAMING_SNAKE_CASE										=   3
    SCREAMING_SNAKE_CASE										=   0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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