




"""simple docstring"""





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_5_0, "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_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]   )
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    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=UpperCamelCase_      ,  )
						assert hasattr(self      ,  "env")


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    int=1):


						"""simple docstring"""




						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=UpperCamelCase_      ,  instance_type=self.instance_type      ,  debugger_hook_config=UpperCamelCase_      ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}      ,  metric_definitions=self.env.metric_definitions      ,  py_version="py36"      ,  )


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str):


						"""simple docstring"""




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


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self.create_estimator()

						# run training
						estimator.fit()

						# result dataframe
						__UpperCAmelCase	: Any		=					TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						__UpperCAmelCase	: Dict		=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
						__UpperCAmelCase	: Dict		=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__UpperCAmelCase	: Any		=					(
						    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"      ,  999999)
						)

						# 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"""





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class      a__				:
			lowercase_    =	None


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)
						__UpperCAmelCase	: Any		=					json.loads(feat_extract.to_json_string())
						for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]      ,  UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Dict		=					os.path.join(UpperCamelCase_      ,  "feat_extract.json")
									feat_extract_first.to_json_file(UpperCamelCase_)
									__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class.from_json_file(UpperCamelCase_)

						self.assertEqual(feat_extract_second.to_dict()      ,  feat_extract_first.to_dict())


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Tuple		=					feat_extract_first.save_pretrained(UpperCamelCase_)[0]
									check_json_file_has_correct_format(UpperCamelCase_)
									__UpperCAmelCase	: List[Any]		=					self.feature_extraction_class.from_pretrained(UpperCamelCase_)

						self.assertEqual(feat_extract_second.to_dict()      ,  feat_extract_first.to_dict())


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.feature_extraction_class()
						self.assertIsNotNone(UpperCamelCase_)


