





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""": 16_00, """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""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]      )
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   List[str]		):
         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 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   Any		):
         # configuration for running training on smdistributed Model Parallel
         __snake_case:						Union[str, Any]			  =						{
             """enabled""": True,
             """processes_per_host""": 8,
         }
         __snake_case:						List[Any]			  =						{
             """enabled""": True,
             """parameters""": {
                 """microbatches""": 4,
                 """placement_strategy""": """spread""",
                 """pipeline""": """interleaved""",
                 """optimize""": """speed""",
                 """partitions""": 4,
                 """ddp""": True,
             },
         }

         __snake_case:						List[Any]			  =						{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

         __snake_case:						Optional[int]			  =						"""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 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Union[str, Any]		):
         TrainingJobAnalytics(A		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)






       @parameterized.expand([(1,)]		)
       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   List[str]		):
         # create estimator
         __snake_case:						List[str]			  =						self.create_estimator(A		)

         # run training
         estimator.fit()

         # result dataframe
         __snake_case:						List[Any]			  =						TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

         # extract kpis
         __snake_case:						Dict			  =						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]		)
         __snake_case:						Optional[int]			  =						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:						List[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}							,    A		)









from __future__ import annotations

import numpy as np







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   List[str]:
  return np.maximum(0 , SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]






