





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  A__       (	unittest.TestCase      ):





      def 	__UpperCamelCase(			self       ):
       '''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=_a	,							)
       assert hasattr(self	,							"env"       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       F"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
       # distributed data settings
       UpperCamelCase     : Union[str, 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=_a	,							instance_count=_a	,							instance_type=self.instance_type	,							debugger_hook_config=_a	,							hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}	,							metric_definitions=self.env.metric_definitions	,							distribution=_a	,							py_version="py36"	,							)





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       TrainingJobAnalytics(_a       ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""       )





      @parameterized.expand([(2,)]       )
      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.create_estimator(_a       )

       # run training
       estimator.fit()

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

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







class  A__       :  # Public class to implement a graph





      def __init__(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       row
       UpperCamelCase     : Any	     =       col
       UpperCamelCase     : Optional[Any]	     =       graph





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       UpperCamelCase     : Dict	     =       [-1, 0, 1, -1, 1, -1, 0, 1]
       UpperCamelCase     : Any	     =       True  # Make those cells visited
       for k in range(8       ):
        if self.is_safe(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       ):
         self.diffs(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       )





      def 	__UpperCamelCase(			self       ):  # And finally, count all islands.
       '''simple docstring'''




       UpperCamelCase     : str	     =       [[False for j in range(self.COL       )] for i in range(self.ROW       )]
       UpperCamelCase     : int	     =       0
       for i in range(self.ROW       ):
        for j in range(self.COL       ):
         if visited[i][j] is False and self.graph[i][j] == 1:
          self.diffs(A_	,							A_	,							A_       )
          count += 1
       return count
