






'''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': 16_00, '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': 16_00, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]			)
class 	UpperCamelCase__					(     unittest.TestCase			):

		"""simple docstring"""




		def       snake_case     ( self						:						int    ):







							"""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       snake_case     ( self						:						str							,    __A						:						Any    ):







							"""simple docstring"""
							_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       snake_case     ( self						:						Optional[Any]							,    __A						:						List[str]    ):







							"""simple docstring"""
							TrainingJobAnalytics(UpperCamelCase__    ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""    )




		@parameterized.expand([(1,)]    )
		def       snake_case     ( self						:						Any							,    __A						:						Tuple    ):







							"""simple docstring"""
							_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'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


