





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": 1600, "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": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]				)
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      List[str]:






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




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      str:






       """simple docstring"""
       # configuration for running training on smdistributed Model Parallel
       UpperCamelCase__							:				Any          =      {
           '''enabled''': True,
           '''processes_per_host''': 8,
       }
       UpperCamelCase__							:				str          =      {
           '''enabled''': True,
           '''parameters''': {
               '''microbatches''': 4,
               '''placement_strategy''': '''spread''',
               '''pipeline''': '''interleaved''',
               '''optimize''': '''speed''',
               '''partitions''': 4,
               '''ddp''': True,
           },
       }

       UpperCamelCase__							:				str          =      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

       UpperCamelCase__							:				str          =      '''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=__magic_name__,		instance_type=self.instance_type,		debugger_hook_config=__magic_name__,		hyperparameters={
               **self.env.hyperparameters,
               '''model_name_or_path''': self.model_name_or_path,
               '''max_steps''': 500,
           },		metric_definitions=self.env.metric_definitions,		distribution=__magic_name__,		py_version='''py36''',		)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tuple:






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




   @parameterized.expand([(1,)]							)
   def       UpperCamelCase__  (    self,		__magic_name__							) ->      int:






       """simple docstring"""
       # create estimator
       UpperCamelCase__							:				Dict          =      self.create_estimator(__magic_name__							)

       # run training
       estimator.fit()

       # result dataframe
       UpperCamelCase__							:				Optional[Any]          =      TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

       # extract kpis
       UpperCamelCase__							:				Any          =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
       UpperCamelCase__							:				int          =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']							)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCamelCase__							:				Optional[int]          =      (
           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},		__magic_name__							)








UpperCAmelCase_     							= 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_     							= 'TRWAGMYFPDXBNJZSQVHLCKE'




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str      )      ->	bool:

    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				Any          =      f"Expected string as input, found {type(__UpperCAmelCase      ).__name__}"
        raise TypeError(__UpperCAmelCase      )

    UpperCamelCase__							:				int          =      spanish_id.replace('''-'''		,   ''''''      ).upper()
    if len(__UpperCAmelCase      ) != 9:
        raise ValueError(__UpperCAmelCase      )

    try:
        UpperCamelCase__							:				List[str]          =      int(spanish_id_clean[0:8]      )
        UpperCamelCase__							:				Optional[int]          =      spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(__UpperCAmelCase      ) from ex

    if letter.isdigit():
        raise ValueError(__UpperCAmelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

