'''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				)    ->     List[Any]:
        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=_a       ,				)
        assert hasattr(self       ,				'env'				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        # configuration for running training on smdistributed Model Parallel
        lowercase_					:       Union[str, Any]     				=					{
            'enabled': True,
            'processes_per_host': 8,
        }
        lowercase_					:       List[Any]     				=					{
            'enabled': True,
            'parameters': {
                'microbatches': 4,
                'placement_strategy': 'spread',
                'pipeline': 'interleaved',
                'optimize': 'speed',
                'partitions': 4,
                'ddp': True,
            },
        }

        lowercase_					:       str     				=					{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

        lowercase_					:       str     				=					'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=_a       ,				instance_type=self.instance_type       ,				debugger_hook_config=_a       ,				hyperparameters={
                **self.env.hyperparameters,
                'model_name_or_path': self.model_name_or_path,
                'max_steps': 500,
            }       ,				metric_definitions=self.env.metric_definitions       ,				distribution=_a       ,				py_version='py36'       ,				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        TrainingJobAnalytics(_a				).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''				)





   @parameterized.expand([(1,)]				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        # create estimator
        lowercase_					:       Any     				=					self.create_estimator(_a				)

        # run training
        estimator.fit()

        # result dataframe
        lowercase_					:       Optional[Any]     				=					TrainingJobAnalytics(estimator.latest_training_job.name				).dataframe()

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





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




