






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": 6_50, "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": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]	)
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """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=UpperCamelCase_			,						)
         assert hasattr(self			,						"""env"""					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      List[Any]=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=UpperCamelCase_			,						instance_type=self.instance_type			,						debugger_hook_config=UpperCamelCase_			,						hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}			,						metric_definitions=self.env.metric_definitions			,						py_version="""py36"""			,						)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



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





     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						self.create_estimator()

         # run training
         estimator.fit()

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

         # extract kpis
         __A       =						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]					)
         __A       =						list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]					)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         __A       =						(
             Session().describe_training_job(estimator.latest_training_job.name					).get("""TrainingTimeInSeconds"""			,						999_999					)
         )

         # 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}			,						UpperCamelCase_					)












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




