


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''': 1_600, '''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''': 1_600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]			)
class 							lowercase  (						unittest.TestCase			):







     def 							lowercase__       (						self :						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=_lowercase   ,   )
           assert hasattr(self   ,   '''env'''      )







     def 							lowercase__       (						self :						Any   ,   _lowercase :						List[str]      ):
           # configuration for running training on smdistributed Model Parallel
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{
               '''enabled''': True,
               '''processes_per_host''': 8,
           }
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		{
               '''enabled''': True,
               '''parameters''': {
                   '''microbatches''': 4,
                   '''placement_strategy''': '''spread''',
                   '''pipeline''': '''interleaved''',
                   '''optimize''': '''speed''',
                   '''partitions''': 4,
                   '''ddp''': True,
               },
           }

           SCREAMING_SNAKE_CASE__       :  int						     =		{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

           SCREAMING_SNAKE_CASE__       :  Optional[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''': 5_00,
               }   ,   metric_definitions=self.env.metric_definitions   ,   distribution=_lowercase   ,   py_version='''py36'''   ,   )







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						Union[str, Any]      ):
           TrainingJobAnalytics(_lowercase      ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""      )







     @parameterized.expand([(1,)]      )
     def 							lowercase__       (						self :						str   ,   _lowercase :						Optional[Any]      ):
           # create estimator
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.create_estimator(_lowercase      )

           # run training
           estimator.fit()

           # result dataframe
           SCREAMING_SNAKE_CASE__       :  Any						     =		TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

           # extract kpis
           SCREAMING_SNAKE_CASE__       :  Dict						     =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']      )
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           SCREAMING_SNAKE_CASE__       :  str						     =		(
               Session().describe_training_job(estimator.latest_training_job.name      ).get('''TrainingTimeInSeconds'''   ,   99_99_99      )
           )

           # 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      )













"""simple docstring"""






__UpperCAmelCase						=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			# Return True if there is node that has not iterated.
			a__  :	int		 =			[False] * len(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			[s]
			a__  :	Optional[int]		 =			True

			while queue:
						a__  :	Any		 =			queue.pop(0			)
						for ind in range(len(graph[u]			)			):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(lowerCAmelCase__			)
												a__  :	Any		 =			True
												a__  :	Union[str, Any]		 =			u

			return visited[t]






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			[-1] * (len(lowerCAmelCase__			))
			a__  :	Any		 =			0
			a__  :	List[str]		 =			[]
			a__  :	Tuple		 =			[i[:] for i in graph]  # Record original cut, copy.
			while bfs(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
						a__  :	Any		 =			float("Inf"			)
						a__  :	Optional[int]		 =			sink

						while s != source:
									# Find the minimum value in select path
									a__  :	Union[str, Any]		 =			min(lowerCAmelCase__							,			graph[parent[s]][s]			)
									a__  :	List[str]		 =			parent[s]

						max_flow += path_flow
						a__  :	List[str]		 =			sink

						while v != source:
									a__  :	int		 =			parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									a__  :	List[str]		 =			parent[v]

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(len(graph[0]			)			):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j)			)

			return res


if __name__ == "__main__":
		print(mincut(test_graph, source=0, sink=5))