





'''simple docstring'''






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 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   def 		__UpperCamelCase						(							self       ):
    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=__SCREAMING_SNAKE_CASE	,		)
    assert hasattr(self	,		"""env"""       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=1       ):
    # 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}-single"	,		instance_count=__SCREAMING_SNAKE_CASE	,		instance_type=self.instance_type	,		debugger_hook_config=__SCREAMING_SNAKE_CASE	,		hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}	,		metric_definitions=self.env.metric_definitions	,		py_version="""py36"""	,		)

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    TrainingJobAnalytics(__SCREAMING_SNAKE_CASE       ).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"       )


   def 		__UpperCamelCase						(							self       ):
    # create estimator
    snake_case__     :					Optional[int]    =  self.create_estimator()

    # run training
    estimator.fit()

    # result dataframe
    snake_case__     :					int    =  TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

    # extract kpis
    snake_case__     :					List[Any]    =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]       )
    snake_case__     :					Any    =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]       )
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    snake_case__     :					Union[str, Any]    =  (
        Session().describe_training_job(estimator.latest_training_job.name       ).get("""TrainingTimeInSeconds"""	,		9_9_9_9_9_9       )
    )

    # 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}	,		__SCREAMING_SNAKE_CASE       )








def    a__    (		snake_case__					:							Tuple     ):  # noqa: E741
  _UpperCAmelCase		:							Dict							=     len(snake_case__     )
  _UpperCAmelCase		:							Tuple							=     0
  _UpperCAmelCase		:							Union[str, Any]							=     [0] * n
  _UpperCAmelCase		:							Union[str, Any]							=     [False] * n
  _UpperCAmelCase		:							Tuple							=     [False] * n

  def dfs(snake_case__					:							List[str]						,   snake_case__					:							int						,   snake_case__					:							str						,   snake_case__					:							int     ):
    if parent == root:
      out_edge_count += 1
    _UpperCAmelCase		:							List[Any]							=     True
    _UpperCAmelCase		:							List[str]							=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _UpperCAmelCase		:							Optional[Any]							=     dfs(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__     )
        _UpperCAmelCase		:							Optional[Any]							=     min(low[at]						,   low[to]     )

        # AP found via bridge
        if at < low[to]:
          _UpperCAmelCase		:							List[str]							=     True
        # AP found via cycle
        if at == low[to]:
          _UpperCAmelCase		:							Optional[Any]							=     True
      else:
        _UpperCAmelCase		:							str							=     min(low[at]						,   snake_case__     )
    return out_edge_count

  for i in range(snake_case__     ):
    if not visited[i]:
      _UpperCAmelCase		:							List[Any]							=     0
      _UpperCAmelCase		:							List[str]							=     dfs(snake_case__						,   snake_case__						,   -1						,   snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     out_edge_count > 1

  for x in range(len(snake_case__     )     ):
    if is_art[x] is True:
      print(snake_case__     )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)





