




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]   )
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     str							)					->     Union[str, Any]:
							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=__lowerCamelCase					,      )
							assert hasattr(self					,      "env"							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     List[Any]=1							)					->     Optional[int]:
							# 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}-single"""					,      instance_count=__lowerCamelCase					,      instance_type=self.instance_type					,      debugger_hook_config=__lowerCamelCase					,      hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}					,      metric_definitions=self.env.metric_definitions					,      py_version="py36"					,      )





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[str]							)					->     int:
							TrainingJobAnalytics(__lowerCamelCase							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							# create estimator
							a    =			self.create_estimator()

							# run training
							estimator.fit()

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

							# extract kpis
							a    =			list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]							)
							a    =			list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]							)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							a    =			(
							    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}					,      __lowerCamelCase							)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






