
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 				SCREAMING_SNAKE_CASE__			(							unittest.TestCase			):

							"""simple docstring"""






							def 			A__						(     self	)->      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=__UpperCAmelCase   ,		)
													assert hasattr(self   ,		'''env'''	)



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Tuple:





													'''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 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Tuple:





													'''simple docstring'''






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



							@parameterized.expand([(1,)]	)
							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Dict:





													'''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'''   ,		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}   ,		__UpperCAmelCase	)











"""simple docstring"""





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
  import torch






@require_torch
@require_optimum
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

         self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							model.reverse_bettertransformer()

         self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

           self.assertFalse(
               any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__UpperCAmelCase      ):
             model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
