

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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Optional[int]			):
       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      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Optional[Any]=1			):
       # 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      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       TrainingJobAnalytics(__lowerCamelCase			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)





   def      _snake_case		(       self					:			List[str]			):
       # create estimator
       SCREAMING_SNAKE_CASE										=   self.create_estimator()

       # run training
       estimator.fit()

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

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






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline





@pytest.mark.integration
def  __magic_name__  (				)				-> Union[str, Any]:

							"""simple docstring"""



							with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT				):
														with pytest.raises(lowercase				):
																					requests.request("""GET"""				,       """https://huggingface.co"""				)
														with pytest.raises(requests.exceptions.ConnectTimeout				):
																					requests.request("""GET"""				,       """https://huggingface.co"""				,       timeout=1.0				)





@pytest.mark.integration
def  __magic_name__  (				)				-> Union[str, Any]:

							"""simple docstring"""



							with offline(OfflineSimulationMode.CONNECTION_FAILS				):
														with pytest.raises(requests.exceptions.ConnectionError				):
																					requests.request("""GET"""				,       """https://huggingface.co"""				)





def  __magic_name__  (				)				-> Dict:

							"""simple docstring"""



							with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1				):
														with pytest.raises(lowercase				):
																					http_head("""https://huggingface.co"""				)