


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       A    (   unittest.TestCase ):


							def 			_A       (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=UpperCamelCase__      ,				)
									assert hasattr(self      ,				'env'					)


							def 			_A       (self      ,				lowerCAmelCase					):
									__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=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': 5_0_0,
									    }      ,				metric_definitions=self.env.metric_definitions      ,				distribution=UpperCamelCase__      ,				py_version='py36'      ,				)


							def 			_A       (self      ,				lowerCAmelCase					):
									TrainingJobAnalytics(UpperCamelCase__					).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'					)







							@parameterized.expand([(1,)]					)
							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			self.create_estimator(UpperCamelCase__					)

									# 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}      ,				UpperCamelCase__					)











'''simple docstring'''


_lowerCamelCase	:       int      	=	"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich

