






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        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     List[str]   )				-> Dict:
        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=__lowercase						, )
        assert hasattr(self						, """env"""   )
      def       UpperCAmelCase ( self   :     List[str]						, __lowercase   :     Optional[Any]   )				-> Any:
        # configuration for running training on smdistributed Model Parallel
        __UpperCAmelCase				:      List[Any]	 =    {
            """enabled""": True,
            """processes_per_host""": 8,
        }
        __UpperCAmelCase				:      Optional[int]	 =    {
            """enabled""": True,
            """parameters""": {
                """microbatches""": 4,
                """placement_strategy""": """spread""",
                """pipeline""": """interleaved""",
                """optimize""": """speed""",
                """partitions""": 4,
                """ddp""": True,
            },
        }

        __UpperCAmelCase				:      Tuple	 =    {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

        __UpperCAmelCase				:      Dict	 =    """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=__lowercase						, instance_type=self.instance_type						, debugger_hook_config=__lowercase						, hyperparameters={
                **self.env.hyperparameters,
                """model_name_or_path""": self.model_name_or_path,
                """max_steps""": 500,
            }						, metric_definitions=self.env.metric_definitions						, distribution=__lowercase						, py_version="""py36"""						, )
      def       UpperCAmelCase ( self   :     List[str]						, __lowercase   :     Optional[int]   )				-> str:
        TrainingJobAnalytics(__lowercase   ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""   )




      @parameterized.expand([(1,)]   )
      def       UpperCAmelCase ( self   :     Dict						, __lowercase   :     Union[str, Any]   )				-> Tuple:
        # create estimator
        __UpperCAmelCase				:      List[str]	 =    self.create_estimator(__lowercase   )

        # run training
        estimator.fit()

        # result dataframe
        __UpperCAmelCase				:      Any	 =    TrainingJobAnalytics(estimator.latest_training_job.name   ).dataframe()

        # extract kpis
        __UpperCAmelCase				:      int	 =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]   )
        __UpperCAmelCase				:      str	 =    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]   )
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        __UpperCAmelCase				:      Dict	 =    (
            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}						, __lowercase   )










"""simple docstring"""
lowerCAmelCase__    						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowerCAmelCase__    						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__    						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

