






"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "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": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]				)
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[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=lowercase_		,			)
          assert hasattr(self		,			'''env''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{
              '''enabled''': True,
              '''processes_per_host''': 8,
          }
          SCREAMING_SNAKE_CASE_   :						Any							=	{
              '''enabled''': True,
              '''parameters''': {
                  '''microbatches''': 4,
                  '''placement_strategy''': '''spread''',
                  '''pipeline''': '''interleaved''',
                  '''optimize''': '''speed''',
                  '''partitions''': 4,
                  '''ddp''': True,
              },
          }

          SCREAMING_SNAKE_CASE_   :						Dict							=	{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''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=lowercase_		,			instance_type=self.instance_type		,			debugger_hook_config=lowercase_		,			hyperparameters={
                  **self.env.hyperparameters,
                  '''model_name_or_path''': self.model_name_or_path,
                  '''max_steps''': 500,
              }		,			metric_definitions=self.env.metric_definitions		,			distribution=lowercase_		,			py_version='''py36'''		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						int):




          '''simple docstring'''


          TrainingJobAnalytics(lowercase_).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv')



      @parameterized.expand([(1,)])
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.create_estimator(lowercase_)

          # run training
          estimator.fit()

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

          # extract kpis
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
          SCREAMING_SNAKE_CASE_   :						int							=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          SCREAMING_SNAKE_CASE_   :						int							=	(
              Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''		,			999999)
          )

          # 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}		,			lowercase_)










"""simple docstring"""






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	9, 14  # noqa: F841
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    SCREAMING_SNAKE_CASE_   :						Dict							=	defaultdict(__a							)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]							)
        adjancency[nodea].append([nodea, cost]							)

    SCREAMING_SNAKE_CASE_   :						Any							=	mst(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        SCREAMING_SNAKE_CASE_   :						Dict							=	tuple(answer[:2]							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tuple(edge[::-1]							)
        assert edge in result or reverse in result


