



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_6_0_0, '''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_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]		)
class     __lowerCAmelCase					(    unittest.TestCase		):



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



						"""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=A				,	)
						assert hasattr(self				,	'env')



			def 					_lowerCamelCase (		self     :						Any				,	A     :						List[Any])     ->					Optional[int]:



						"""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=A				,	instance_type=self.instance_type				,	debugger_hook_config=A				,	hyperparameters={
						        **self.env.hyperparameters,
						        'model_name_or_path': self.model_name_or_path,
						        'max_steps': 5_00,
						    }				,	metric_definitions=self.env.metric_definitions				,	distribution=A				,	py_version='py36'				,	)



			def 					_lowerCamelCase (		self     :						int				,	A     :						str)     ->					int:



						"""simple docstring"""






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



			@parameterized.expand([(1,)])
			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.create_estimator(A)

						# 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_99_99)
						)

						# 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}				,	A)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__     = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = ["YolosFeatureExtractor"]
							UpperCAmelCase__     = ["YolosImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "YolosForObjectDetection",
							    "YolosModel",
							    "YolosPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
