






'''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_600, "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_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]      )
class 				lowercase       (							unittest.TestCase      ):


				'''simple docstring'''

				def    a__				(					self     :     Dict     )							->							Optional[int]:

					'''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=A__				,     )
					assert hasattr(self				,     "env"     )

				def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     Any     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     {
					    """enabled""": True,
					    """processes_per_host""": 8,
					}
					lowerCamelCase__  =     {
					    """enabled""": True,
					    """parameters""": {
					        """microbatches""": 4,
					        """placement_strategy""": """spread""",
					        """pipeline""": """interleaved""",
					        """optimize""": """speed""",
					        """partitions""": 4,
					        """ddp""": True,
					    },
					}

					lowerCamelCase__  =     {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

					lowerCamelCase__  =     """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=A__				,     instance_type=self.instance_type				,     debugger_hook_config=A__				,     hyperparameters={
					        **self.env.hyperparameters,
					        "model_name_or_path": self.model_name_or_path,
					        "max_steps": 500,
					    }				,     metric_definitions=self.env.metric_definitions				,     distribution=A__				,     py_version="py36"				,     )

				def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     str     )							->							Union[str, Any]:

					'''simple docstring'''




					TrainingJobAnalytics(A__     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )




				@parameterized.expand([(1,)]     )
				def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Tuple     )							->							Optional[int]:

					'''simple docstring'''




					lowerCamelCase__  =     self.create_estimator(A__     )

					# 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"				,     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}				,     A__     )









'''simple docstring'''





from __future__ import annotations






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     list(range(len(lowercase__)))
 lowerCamelCase__  =     [v / w for v, w in zip(lowercase__				,     lowercase__)]
 index.sort(key=lambda lowercase__: ratio[i]				,     reverse=lowercase__)

 lowerCamelCase__  =     0
 lowerCamelCase__  =     [0] * len(lowercase__)
 for i in index:
  if weight[i] <= capacity:
   lowerCamelCase__  =     1
   max_value += value[i]
   capacity -= weight[i]
  else:
   lowerCamelCase__  =     capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()

