



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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_50, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]   )
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							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=_snake_case					,       )
								assert hasattr(self					,       'env'       )



					def      _a				(  self       :							Tuple					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
								# distributed data settings
								A__	  =     {'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None

								# 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=_snake_case					,       instance_count=_snake_case					,       instance_type=self.instance_type					,       debugger_hook_config=_snake_case					,       hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}					,       metric_definitions=self.env.metric_definitions					,       distribution=_snake_case					,       py_version='py36'					,       )



					def      _a				(  self       :							Dict					,       _snake_case       :							str       ):






								"""simple docstring"""


								TrainingJobAnalytics(_snake_case       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )



					@parameterized.expand([(2,)]       )
					def      _a				(  self       :							List[str]					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     self.create_estimator(_snake_case       )

								# run training
								estimator.fit()

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

								# extract kpis
								A__	  =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']       )
								A__	  =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']       )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								A__	  =     (
								    Session().describe_training_job(estimator.latest_training_job.name       ).get('TrainingTimeInSeconds'					,       99_99_99       )
								)

								# 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}					,       _snake_case       )












"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


