




'''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 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Optional[Any]:







          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 			snake_case_      (				self							,__a=1							)      ->				Union[str, Any]:
          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 			snake_case_      (				self							,__a							)      ->				List[Any]:
          TrainingJobAnalytics(__SCREAMING_SNAKE_CASE							).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''							)







     def 			snake_case_      (				self							)      ->				str:
          snake_case			:      List[str]        =			self.create_estimator()

          # run training
          estimator.fit()

          # result dataframe
          snake_case			:      str        =			TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

          # extract kpis
          snake_case			:      Optional[Any]        =			list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]							)
          snake_case			:      Union[str, 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			:      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}							,__SCREAMING_SNAKE_CASE							)





from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowerCamelCase  =(3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCamelCase  =(4, 6, 2, 0, 8, 1_0, 3, -2)




@dataclass
class 					_lowerCamelCase			:





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	42
     SCREAMING_SNAKE_CASE_		 =	42




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           UpperCamelCase__     : Node | None											=					None
           for i in sorted(__SCREAMING_SNAKE_CASE    ,    reverse=__SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : List[str]											=					Node(__SCREAMING_SNAKE_CASE    ,    self.head	)






     def __iter__(				self	)	-> Iterator[int]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.head
           while node:
                 yield node.data
                 UpperCamelCase__     : List[Any]											=					node.next_node






     def __len__(				self	)	-> int:




           """simple docstring"""







           return sum(1 for _ in self	)






     def __str__(				self	)	-> str:




           """simple docstring"""







           return " -> ".join([str(__SCREAMING_SNAKE_CASE	) for node in self]	)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      return SortedLinkedList(list(UpperCamelCase__		) + list(UpperCamelCase__		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    lowerCamelCase  =SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


