
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  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			str       )       ->     str:
					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=SCREAMING_SNAKE_CASE_       ,							)
					assert hasattr(self       ,							'''env'''       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Optional[int]:
					# configuration for running training on smdistributed Model Parallel
					lowercase_       =					{
					    '''enabled''': True,
					    '''processes_per_host''': 8,
					}
					lowercase_       =					{
					    '''enabled''': True,
					    '''parameters''': {
					        '''microbatches''': 4,
					        '''placement_strategy''': '''spread''',
					        '''pipeline''': '''interleaved''',
					        '''optimize''': '''speed''',
					        '''partitions''': 4,
					        '''ddp''': True,
					    },
					}

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

					lowercase_       =					'''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=SCREAMING_SNAKE_CASE_       ,							instance_type=self.instance_type       ,							debugger_hook_config=SCREAMING_SNAKE_CASE_       ,							hyperparameters={
					        **self.env.hyperparameters,
					        '''model_name_or_path''': self.model_name_or_path,
					        '''max_steps''': 5_0_0,
					    }       ,							metric_definitions=self.env.metric_definitions       ,							distribution=SCREAMING_SNAKE_CASE_       ,							py_version='''py36'''       ,							)
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     List[str]:
					TrainingJobAnalytics(SCREAMING_SNAKE_CASE_       ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''       )




	@parameterized.expand([(1,)]       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Any:
					# create estimator
					lowercase_       =					self.create_estimator(SCREAMING_SNAKE_CASE_       )

					# run training
					estimator.fit()

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

					# extract kpis
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']       )
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']       )
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowercase_       =					(
					    Session().describe_training_job(estimator.latest_training_job.name       ).get('''TrainingTimeInSeconds'''       ,							9_9_9_9_9_9       )
					)

					# 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}       ,							SCREAMING_SNAKE_CASE_       )










import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


