
'''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""": 1_600, """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""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]			)
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								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=UpperCamelCase					,						)
								assert hasattr(self					,						"env"      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								# configuration for running training on smdistributed Model Parallel
								_SCREAMING_SNAKE_CASE				= {
								    "enabled": True,
								    "processes_per_host": 8,
								}
								_SCREAMING_SNAKE_CASE				= {
								    "enabled": True,
								    "parameters": {
								        "microbatches": 4,
								        "placement_strategy": "spread",
								        "pipeline": "interleaved",
								        "optimize": "speed",
								        "partitions": 4,
								        "ddp": True,
								    },
								}

								_SCREAMING_SNAKE_CASE				= {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

								_SCREAMING_SNAKE_CASE				= "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=UpperCamelCase					,						instance_type=self.instance_type					,						debugger_hook_config=UpperCamelCase					,						hyperparameters={
								        **self.env.hyperparameters,
								        "model_name_or_path": self.model_name_or_path,
								        "max_steps": 500,
								    }					,						metric_definitions=self.env.metric_definitions					,						distribution=UpperCamelCase					,						py_version="py36"					,						)




		def     lowercase       (				self					,						UpperCamelCase      ):
								TrainingJobAnalytics(UpperCamelCase      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )




		@parameterized.expand([(1,)]      )
		def     lowercase       (				self					,						UpperCamelCase      ):
								# create estimator
								_SCREAMING_SNAKE_CASE				= self.create_estimator(UpperCamelCase      )

								# 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"					,						999_999      )
								)

								# 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}					,						UpperCamelCase      )

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= int(_SCREAMING_SNAKE_CASE  )
						if n_element < 1:
												_SCREAMING_SNAKE_CASE				= ValueError("a should be a positive number"  )
												raise my_error

						_SCREAMING_SNAKE_CASE				= [1]
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= (0, 0, 0)
						_SCREAMING_SNAKE_CASE				= 1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2	,			hamming_list[j] * 3	,			hamming_list[k] * 5  )  )
												index += 1
						return hamming_list


if __name__ == "__main__":
						_snake_case				:   int     =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
						print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
						_snake_case				:   str     =				hamming(int(n))
						print("""-----------------------------------------------------""")
						print(F"The list with nth numbers is: {hamming_numbers}")
						print("""-----------------------------------------------------""")