





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': 650, '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': 600, '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': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]	)
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         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        lowerCAmelCase__		(    self				,     snake_case_							):
         _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        lowerCAmelCase__		(    self				,     snake_case_							):
         TrainingJobAnalytics(snake_case_							).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"							)




     @parameterized.expand([(2,)]							)
     def        lowerCAmelCase__		(    self				,     snake_case_							):
         # create estimator
         _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_9999							)
         )

         # 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_							)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A							:			Optional[Any]         =			{}


try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[int]         =			["MLukeTokenizer"]

if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mluke import MLukeTokenizer


else:
 import sys

 __A							:			List[str]         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





