'''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": 6_5_0, "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": 6_0_0, "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": 6_0_0, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ] )
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[str]:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   Any						= F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
        # distributed data settings
        lowercase__    :   Dict						= {'''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=__lowerCAmelCase     ,       instance_count=__lowerCAmelCase     ,       instance_type=self.instance_type     ,       debugger_hook_config=__lowerCAmelCase     ,       hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}     ,       metric_definitions=self.env.metric_definitions     ,       distribution=__lowerCAmelCase     ,       py_version='''py36'''     ,       )

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        TrainingJobAnalytics(__lowerCAmelCase			).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""			)


       @parameterized.expand([(2,)]			)
       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        # create estimator
        lowercase__    :   List[Any]						= self.create_estimator(__lowerCAmelCase			)

        # run training
        estimator.fit()

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

        # extract kpis
        lowercase__    :   Any						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']			)
        lowercase__    :   Tuple						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']			)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        lowercase__    :   List[Any]						= (
            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}     ,       __lowerCAmelCase			)


'''simple docstring'''

class       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self			)  ->  List[str]:
        lowercase__    :   Dict						= {}

       def _lowerCAmelCase(						self			)  ->  None:
        print(self.vertex			)
        for i in self.vertex:
         print(__lowerCAmelCase     ,       ''' -> '''     ,       ''' -> '''.join([str(__lowerCAmelCase			) for j in self.vertex[i]]			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  None:
        # check if vertex is already present,
        if from_vertex in self.vertex:
         self.vertex[from_vertex].append(__lowerCAmelCase			)
        else:
         # else make a new vertex
         lowercase__    :   Union[str, Any]						= [to_vertex]

       def _lowerCAmelCase(						self			)  ->  None:
        # visited array for storing already visited nodes
        lowercase__    :   str						= [False] * len(self.vertex			)

        # call the recursive helper function
        for i in range(len(self.vertex			)			):
         if not visited[i]:
          self.dfs_recursive(__lowerCAmelCase     ,       __lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  None:
        # mark start vertex as visited
        lowercase__    :   List[str]						= True

        print(__lowerCAmelCase     ,       end=''' '''			)

        # Recur for all the vertices that are adjacent to this node
        for i in self.vertex:
         if not visited[i]:
          self.dfs_recursive(__lowerCAmelCase     ,       __lowerCAmelCase			)


if __name__ == "__main__":
  __a:			Optional[Any]	       =       Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3

