






"""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': 650, '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': 600, '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': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]							)
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     Dict    ):

												'''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  lowercase		(   self:     List[Any]				,		__A:     Dict    ):

												'''simple docstring'''

												a__          =  F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
												# distributed data settings
												a__          =  {'''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  lowercase		(   self:     str				,		__A:     List[str]    ):

												'''simple docstring'''

												TrainingJobAnalytics(_lowerCAmelCase    ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'    )





							@parameterized.expand([(2,)]    )
							def  lowercase		(   self:     Dict				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.create_estimator(_lowerCAmelCase    )

												# run training
												estimator.fit()

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

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







__a       :	Union[str, Any]             = range(2, 20 + 1)
__a       :	Any             = [10**k for k in range(ks[-1] + 1)]
__a       :	dict[int, dict[int, list[list[int]]]]             = {}





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  sum(a_i[j] for j in range(lowerCamelCase_ ,     len(lowerCamelCase_)))
					a__          =  sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase_) ,     lowerCamelCase_)))

					a__       ,a__          =  0, 0
					a__          =  n - i

					a__          =  memo.get(lowerCamelCase_)

					if sub_memo is not None:
										a__          =  sub_memo.get(lowerCamelCase_)

										if jumps is not None and len(lowerCamelCase_) > 0:
															# find and make the largest jump without going over
															a__          =  -1
															for _k in range(len(lowerCamelCase_) - 1 ,     -1 ,     -1):
																				if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																									a__          =  _k
																									break

															if max_jump >= 0:
																				a__       ,a__       ,a__          =  jumps[max_jump]
																				# since the difference between jumps is cached, add c
																				a__          =  diff + c
																				for j in range(min(lowerCamelCase_ ,     len(lowerCamelCase_))):
																									a__       ,a__          =  divmod(lowerCamelCase_ ,     10)
																				if new_c > 0:
																									add(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

										else:
															a__          =  []
					else:
										a__          =  {c: []}
										a__          =  sub_memo

					if dn >= max_dn or c + diff >= base[k]:
										return diff, dn

					if k > ks[0]:
										while True:
															# keep doing smaller jumps
															a__       ,a__          =  next_term(lowerCamelCase_ ,     k - 1 ,     i + dn ,     lowerCamelCase_)
															diff += _diff
															dn += terms_jumped

															if dn >= max_dn or c + diff >= base[k]:
																				break
					else:
										# would be too small a jump, just compute sequential terms instead
										a__       ,a__          =  compute(lowerCamelCase_ ,     lowerCamelCase_ ,     i + dn ,     lowerCamelCase_)
										diff += _diff
										dn += terms_jumped

					a__          =  sub_memo[c]

					# keep jumps sorted by # of terms skipped
					a__          =  0
					while j < len(lowerCamelCase_):
										if jumps[j][1] > dn:
															break
										j += 1

					# cache the jump for this value digitsum(b) and c
					sub_memo[c].insert(lowerCamelCase_ ,     (diff, dn, k))
					return (diff, dn)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					if i >= n:
										return 0, i
					if k > len(lowerCamelCase_):
										a_i.extend([0 for _ in range(k - len(lowerCamelCase_))])

					# note: a_i -> b * 10^k + c
					# ds_b -> digitsum(b)
					# ds_c -> digitsum(c)
					a__          =  i
					a__       ,a__       ,a__          =  0, 0, 0
					for j in range(len(lowerCamelCase_)):
										if j >= k:
															ds_b += a_i[j]
										else:
															ds_c += a_i[j]

					while i < n:
										i += 1
										a__          =  ds_c + ds_b
										diff += addend
										a__          =  0
										for j in range(lowerCamelCase_):
															a__          =  a_i[j] + addend
															a__       ,a__          =  divmod(lowerCamelCase_ ,     10)

															ds_c += a_i[j]

										if addend > 0:
															break

					if addend > 0:
										add(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					return diff, i - start_i





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					for j in range(lowerCamelCase_ ,     len(lowerCamelCase_)):
										a__          =  digits[j] + addend
										if s >= 10:
															a__       ,a__          =  divmod(lowerCamelCase_ ,     10)
															a__          =  addend // 10 + quotient
										else:
															a__          =  s
															a__          =  addend // 10

										if addend == 0:
															break

					while addend > 0:
										a__       ,a__          =  divmod(lowerCamelCase_ ,     10)
										digits.append(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ = 10**15):
					a__          =  [1]
					a__          =  1
					a__          =  0
					while True:
										a__       ,a__          =  next_term(lowerCamelCase_ ,     20 ,     i + dn ,     lowerCamelCase_)
										dn += terms_jumped
										if dn == n - i:
															break

					a__          =  0
					for j in range(len(lowerCamelCase_)):
										a_n += digits[j] * 10**j
					return a_n


if __name__ == "__main__":
	print(F'''{solution() = }''')


