


'''simple docstring'''

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

import pytest
from parameterized import parameterized, 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.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]							)
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						int      )  ->			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        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						Union[str, Any]      )  ->			List[Any]:
			__magic_name__	:   Tuple					     =					F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
			# distributed data settings
			__magic_name__	:   Optional[Any]					     =					{'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None

			# 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=__lowerCAmelCase  ,					instance_count=__lowerCAmelCase  ,					instance_type=self.instance_type  ,					debugger_hook_config=__lowerCAmelCase  ,					hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}  ,					metric_definitions=self.env.metric_definitions  ,					distribution=__lowerCAmelCase  ,					py_version='py36'  ,					)







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						List[str]      )  ->			Any:
			TrainingJobAnalytics(__lowerCAmelCase      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )







		@parameterized.expand([(2,)]      )
		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[Any]      )  ->			Union[str, Any]:
			# create estimator
			__magic_name__	:   Dict					     =					self.create_estimator(__lowerCAmelCase      )

			# run training
			estimator.fit()

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

			# extract kpis
			__magic_name__	:   List[str]					     =					list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
			__magic_name__	:   int					     =					list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
			# get train time from SageMaker job, this includes starting, preprocessing, stopping
			__magic_name__	:   str					     =					(
			    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      )



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	if not all(x.isalpha() for x in string		):
		raise ValueError('String must only contain alphabetic characters.'		)

	__magic_name__	:   int					     =					sorted(string.lower()		)
	return len(lowerCAmelCase		) == len(set(lowerCAmelCase		)		)


if __name__ == "__main__":
							lowerCAmelCase				:Any        =      input('''Enter a string ''').strip()

							lowerCAmelCase				:List[Any]        =      is_isogram(input_str)
							print(F'{input_str} is {"an" if isogram else "not an"} isogram.')