
"""simple docstring"""

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 __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''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=__UpperCAmelCase     ,					)
      assert hasattr(self     ,					'env'				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	{
          'enabled': True,
          'processes_per_host': 8,
      }
      __UpperCamelCase      =	{
          'enabled': True,
          'parameters': {
              'microbatches': 4,
              'placement_strategy': 'spread',
              'pipeline': 'interleaved',
              'optimize': 'speed',
              'partitions': 4,
              'ddp': True,
          },
      }

      __UpperCamelCase      =	{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

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





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




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







     @parameterized.expand([(1,)]				)
     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.create_estimator(__UpperCAmelCase				)

      # run training
      estimator.fit()

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

      # extract kpis
      __UpperCamelCase      =	list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']				)
      __UpperCamelCase      =	list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']				)
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      __UpperCamelCase      =	(
          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}     ,					__UpperCAmelCase				)





"""simple docstring"""

from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters






def 							A						(    )				->     Any:
 __UpperCamelCase      =	{
     'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
     'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
     'content': ['a ' * 2_0, 'a ' * 3_0, 'b ' * 7],
 }
 __UpperCamelCase      =	Dataset.from_dict(snake_case		)
 return dataset





class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase      =	make_duplicate_clusters(__UpperCAmelCase     ,					0.8_5				)
      self.assertEqual(len(duplicate_clusters[0]				)     ,					2				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase  ,     __UpperCamelCase      =	deduplicate_dataset(__UpperCAmelCase				)
      self.assertEqual(len(__UpperCAmelCase				)     ,					2				)
      print(__UpperCAmelCase				)
      self.assertEqual(duplicate_clusters[0][0]['copies']     ,					2				)
      self.assertEqual(duplicate_clusters[0][0]['is_extreme']     ,					__UpperCAmelCase				)



