


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': 1600, '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': 1600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]     )
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Optional[int]:
             """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=lowercase_			,   )
             assert hasattr(self			,   'env' )






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[int] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		={
                 'enabled': True,
                 'processes_per_host': 8,
             }
             _lowerCamelCase       :		Dict		={
                 'enabled': True,
                 'parameters': {
                     'microbatches': 4,
                     'placement_strategy': 'spread',
                     'pipeline': 'interleaved',
                     'optimize': 'speed',
                     'partitions': 4,
                     'ddp': True,
                 },
             }

             _lowerCamelCase       :		Union[str, Any]		={'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

             _lowerCamelCase       :		int		='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=lowercase_			,   instance_type=self.instance_type			,   debugger_hook_config=lowercase_			,   hyperparameters={
                     **self.env.hyperparameters,
                     'model_name_or_path': self.model_name_or_path,
                     'max_steps': 500,
                 }			,   metric_definitions=self.env.metric_definitions			,   distribution=lowercase_			,   py_version='py36'			,   )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




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






      @parameterized.expand([(1,)] )
      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Any )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.create_estimator(lowercase_ )

             # run training
             estimator.fit()

             # result dataframe
             _lowerCamelCase       :		List[Any]		=TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

             # extract kpis
             _lowerCamelCase       :		Tuple		=list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
             _lowerCamelCase       :		Optional[Any]		=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}			,   lowercase_ )






def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       assert x is not None
       assert y is not None

       _lowerCamelCase       :		Dict		=len(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=len(SCREAMING_SNAKE_CASE__     )

       # declaring the array for storing the dp values
       _lowerCamelCase       :		Optional[int]		=[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

       for i in range(1		,			m + 1     ):
              for j in range(1		,			n + 1     ):
                     _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

                     _lowerCamelCase       :		Union[str, Any]		=max(l[i - 1][j]		,			l[i][j - 1]		,			l[i - 1][j - 1] + match     )

       _lowerCamelCase       :		Optional[int]		=''
       _lowerCamelCase	,     _lowerCamelCase       :		Dict		=m, n
       while i > 0 and j > 0:
              _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            _lowerCamelCase       :		Union[str, Any]		=x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
 lowerCamelCase											=       'AGGTAB'
 lowerCamelCase											=       'GXTXAYB'
 lowerCamelCase											=       4
 lowerCamelCase											=       'GTAB'

 lowerCamelCase					,				lowerCamelCase											=       longest_common_subsequence(a, b)
 print('len =', ln, ', sub-sequence =', subseq)
 import doctest

 doctest.testmod()


