





'''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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]   )
class  UpperCAmelCase     (			unittest.TestCase   ):




      def 				lowercase__    (			self			: int  )				->      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=__snake_case			,			)
            assert hasattr(self			,			"""env"""  )




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




      def 				lowercase__    (			self			: Any			,			__snake_case			: Optional[int]  )				->      Optional[Any]:
            TrainingJobAnalytics(__snake_case  ).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"  )




      def 				lowercase__    (			self			: int  )				->      Any:
            # create estimator
            _lowerCAmelCase							     =							self.create_estimator()

            # run training
            estimator.fit()

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

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









import requests

a__				=		'''YOUR API KEY'''



def    __UpperCAmelCase (	__a      :			str		,__a      :			str = giphy_api_key	)			->     list:

					"""simple docstring"""

					_a	:    Optional[Any]         =		'''+'''.join(query.split()	)
					_a	:    Union[str, Any]         =		F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
					_a	:    List[Any]         =		requests.get(__a	).json()['''data''']
					return [gif["url"] for gif in gifs]


if __name__ == "__main__":
			print('''\n'''.join(get_gifs('''space ship''')))



