


'''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": 6_50, "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": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]			)
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """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=UpperCamelCase			,  )
   assert hasattr(self			,  "env"     )






  def   snake_case		(    self			,  UpperCamelCase=1     ):







   """simple docstring"""
   # 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=UpperCamelCase			,  instance_type=self.instance_type			,  debugger_hook_config=UpperCamelCase			,  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			,  UpperCamelCase     ):







   """simple docstring"""
   TrainingJobAnalytics(UpperCamelCase     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )







  def   snake_case		(    self     ):







   """simple docstring"""
   # create estimator
   lowerCamelCase_      =  self.create_estimator()

   # run training
   estimator.fit()

   # result dataframe
   lowerCamelCase_      =  TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

   # extract kpis
   lowerCamelCase_      =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
   lowerCamelCase_      =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
   # get train time from SageMaker job, this includes starting, preprocessing, stopping
   lowerCamelCase_      =  (
       Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"			,  99_9999     )
   )

   # 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}			,  UpperCamelCase     )











'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				list[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       [0] * len_array

														if len_array > 0:
																					lowerCamelCase__					:	Union[str, Any]         =       array[0]

														for i in range(1			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       self.prefix_sum[i - 1] + array[i]




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if start == 0:
																					return self.prefix_sum[end]

														return self.prefix_sum[end] - self.prefix_sum[start - 1]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       {0}
														for sum_item in self.prefix_sum:
																					if sum_item - target_sum in sums:
																												return True

																					sums.add(UpperCamelCase__     )

														return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()

