



'''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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]		)
class _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  Tuple):







											'''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=_lowerCAmelCase  ,       )
											assert hasattr(self  ,       'env')

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	={
											    'enabled': True,
											    'processes_per_host': 8,
											}
											__lowercase       	={
											    'enabled': True,
											    'parameters': {
											        'microbatches': 4,
											        'placement_strategy': 'spread',
											        'pipeline': 'interleaved',
											        'optimize': 'speed',
											        'partitions': 4,
											        'ddp': True,
											    },
											}

											__lowercase       	={'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

											__lowercase       	='trainer' if self.script == 'run_glue.py' else 'smtrainer'
											# 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}-{instance_count}-smp-{name_extension}"""  ,       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,
											        'max_steps': 5_0_0,
											    }  ,       metric_definitions=self.env.metric_definitions  ,       distribution=_lowerCAmelCase  ,       py_version='py36'  ,       )

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  int):







											'''simple docstring'''

											TrainingJobAnalytics(_lowerCAmelCase).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")

				@parameterized.expand([(1,)])
				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.create_estimator(_lowerCAmelCase)

											# run training
											estimator.fit()

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

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








'''simple docstring'''



from functools import lru_cache



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=2
							__lowercase       	=set()
							while i * i <= n:
														if n % i:
																					i += 1
														else:
																					n //= i
																					factors.add(_lowerCAmelCase					)
							if n > 1:
														factors.add(_lowerCAmelCase					)
							return factors



@lru_cache
def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							return len(unique_prime_factors(_lowerCAmelCase					)					)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							return len(set(_lowerCAmelCase					)					) in (0, 1)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=2

							while True:
														# Increment each value of a generated range
														__lowercase       	=[base + i for i in range(_lowerCAmelCase					)]

														# Run elements through out unique_prime_factors function
														# Append our target number to the end.
														__lowercase       	=[upf_len(_lowerCAmelCase					) for x in group]
														checker.append(_lowerCAmelCase					)

														# If all numbers in the list are equal, return the group variable.
														if equality(_lowerCAmelCase					):
																					return group

														# Increment our base variable by 1
														base += 1



def   _A       (   _lowerCAmelCase = 4					):



							"""simple docstring"""



							__lowercase       	=run(_lowerCAmelCase					)
							return results[0] if len(_lowerCAmelCase					) else None


if __name__ == "__main__":
		print(solution())



