





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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							: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=snake_case      ,   )
        assert hasattr(self      ,   "env"	)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




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

        A_       :					List[Any]        =      {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

        A_       :					Tuple        =      "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=snake_case      ,   instance_type=self.instance_type      ,   debugger_hook_config=snake_case      ,   hyperparameters={
                **self.env.hyperparameters,
                "model_name_or_path": self.model_name_or_path,
                "max_steps": 500,
            }      ,   metric_definitions=self.env.metric_definitions      ,   distribution=snake_case      ,   py_version="py36"      ,   )



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:str	):





        '''simple docstring'''




        TrainingJobAnalytics(snake_case	).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"	)





     @parameterized.expand([(1,)]	)
     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.create_estimator(snake_case	)

        # run training
        estimator.fit()

        # result dataframe
        A_       :					Any        =      TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

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







from __future__ import annotations

from cmath import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int						)			->    tuple[complex, complex]:

   if a == 0:
      raise ValueError("Coefficient 'a' must not be zero."						)
   A_       :					Tuple        =      b * b - 4 * a * c

   A_       :					Any        =      (-b + sqrt(_lowerCAmelCase						)) / (2 * a)
   A_       :					Union[str, Any]        =      (-b - sqrt(_lowerCAmelCase						)) / (2 * a)

   return (
       root_a.real if not root_a.imag else root_a,
       root_a.real if not root_a.imag else root_a,
   )

def   __snake_case    (       )			->    List[Any]:
   A_   ,    A_       :					Optional[Any]        =      quadratic_roots(a=5					,					b=6					,					c=1						)
   print(f"The solutions are: {solutiona} and {solutiona}"						)


if __name__ == "__main__":
 main()
