



'''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_5_0, '''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_0_0, '''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_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]	)
class 			lowerCAmelCase_(							unittest.TestCase	):
  '''simple docstring'''




  def 				UpperCAmelCase_      (       self )       ->						Tuple:
      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=__SCREAMING_SNAKE_CASE    ,)
      assert hasattr(self    ,"""env""" )




  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase )       ->						Dict:
      lowerCAmelCase__					:    List[Any]				 =   F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__					:    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=__SCREAMING_SNAKE_CASE    ,instance_count=__SCREAMING_SNAKE_CASE    ,instance_type=self.instance_type    ,debugger_hook_config=__SCREAMING_SNAKE_CASE    ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}    ,metric_definitions=self.env.metric_definitions    ,distribution=__SCREAMING_SNAKE_CASE    ,py_version="""py36"""    ,)




  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase )       ->						Any:
      TrainingJobAnalytics(__SCREAMING_SNAKE_CASE ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )






  @parameterized.expand([(2,)] )
  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase )       ->						str:
      # create estimator
      lowerCAmelCase__					:    Dict				 =   self.create_estimator(__SCREAMING_SNAKE_CASE )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__					:    Union[str, Any]				 =   TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

      # extract kpis
      lowerCAmelCase__					:    Optional[int]				 =   list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
      lowerCAmelCase__					:    Tuple				 =   list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase__					:    List[str]				 =   (
          Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds"""    ,99_9999 )
      )

      # 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}    ,__SCREAMING_SNAKE_CASE )










'''simple docstring'''







import math





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	str									=   []
				a_		:	Tuple									=   2
				a_		:	Optional[Any]									=   int(math.sqrt(__A   )   )  # Size of every segment
				a_		:	Optional[Any]									=   [True] * (end + 1)
				a_		:	Union[str, Any]									=   []

				while start <= end:
								if temp[start] is True:
												in_prime.append(__A   )
												for i in range(start * start    ,		end + 1    ,		__A   ):
																a_		:	List[Any]									=   False
								start += 1
				prime += in_prime

				a_		:	Any									=   end + 1
				a_		:	Optional[Any]									=   min(2 * end    ,		__A   )

				while low <= n:
								a_		:	Optional[Any]									=   [True] * (high - low + 1)
								for each in in_prime:
												a_		:	Any									=   math.floor(low / each   ) * each
												if t < low:
																t += each

												for j in range(__A    ,		high + 1    ,		__A   ):
																a_		:	Union[str, Any]									=   False

								for j in range(len(__A   )   ):
												if temp[j] is True:
																prime.append(j + low   )

								a_		:	Tuple									=   high + 1
								a_		:	str									=   min(high + end    ,		__A   )

				return prime




print(sieve(10**6))




