




'''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		):




				'''simple docstring'''





				def        a_       (self			)	->       Optional[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=_A ,  )
							assert hasattr(self ,  "env"			)
				def        a_       (self ,  _UpperCAmelCase=1			)	->       Dict:
							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=_A ,  instance_type=self.instance_type ,  debugger_hook_config=_A ,  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 ,  _UpperCAmelCase			)	->       List[Any]:
							TrainingJobAnalytics(_A			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)





				def        a_       (self			)	->       List[str]:
							__UpperCamelCase      :			Union[str, Any]									=   self.create_estimator()

							# run training
							estimator.fit()

							# result dataframe
							__UpperCamelCase      :			Optional[int]									=   TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

							# extract kpis
							__UpperCamelCase      :			Union[str, Any]									=   list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
							__UpperCamelCase      :			Optional[int]									=   list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							__UpperCamelCase      :			Tuple									=   (
							    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} ,  _A			)










from datetime import datetime

import requests





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				bytes:







			'''simple docstring'''

			UpperCAmelCase   		=				'''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
			UpperCAmelCase   		=				requests.get(base_url + url ).json()[0]['''urls'''][0]['''src''']
			return requests.get(UpperCamelCase__ ).content


if __name__ == "__main__":
				__A				:    Union[str, Any]       =   input("Enter Video/IGTV url: ").strip()
				__A				:    Tuple       =   F'{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'
				with open(file_name, "wb") as fp:
								fp.write(download_video(url))
				print(F'Done. Video saved to disk as {file_name}.')



