

"""simple docstring"""





# using dfs for finding eulerian path traversal




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       int,			UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Tuple=None)	->						List[str]:
   '''simple docstring'''


   __lowercase								=						(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         __lowercase		,__lowercase								=						True, True
         __lowercase								=						dfs(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
   return path




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       int)	->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						0
   __lowercase								=						-1
   for i in range(UpperCamelCase_):
      if i not in graph.keys():
         continue
      if len(graph[i]) % 2 == 1:
         odd_degree_nodes += 1
         __lowercase								=						i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Dict)	->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						[[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
   __lowercase		,__lowercase								=						check_circuit_or_path(UpperCamelCase_,			UpperCamelCase_)
   if check == 3:
      print("graph is not Eulerian")
      print("no path")
      return
   __lowercase								=						1
   if check == 2:
      __lowercase								=						odd_node
      print("graph has a Euler path")
   if check == 1:
      print("graph has a Euler cycle")
   __lowercase								=						dfs(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
   print(UpperCamelCase_)




def   _A  (  )	->						int:
   '''simple docstring'''


   __lowercase								=						{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   __lowercase								=						{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   __lowercase								=						{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   __lowercase								=						{1: [2, 3], 2: [1, 3], 3: [1, 2]}
   __lowercase								=						{
       1: [],
       2: []
       # all degree is zero
   }
   __lowercase								=						10
   check_euler(UpperCamelCase_,			UpperCamelCase_)
   check_euler(UpperCamelCase_,			UpperCamelCase_)
   check_euler(UpperCamelCase_,			UpperCamelCase_)
   check_euler(UpperCamelCase_,			UpperCamelCase_)
   check_euler(UpperCamelCase_,			UpperCamelCase_)


if __name__ == "__main__":
   main()







"""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": 1_6_0_0, "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_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]   )
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    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=UpperCAmelCase__,					)
    assert hasattr(self,					"env"	)


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

    __lowercase								=						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

    __lowercase								=						"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=UpperCAmelCase__,					instance_type=self.instance_type,					debugger_hook_config=UpperCAmelCase__,					hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 5_0_0,
        },					metric_definitions=self.env.metric_definitions,					distribution=UpperCAmelCase__,					py_version="py36",					)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int	):
    TrainingJobAnalytics(UpperCAmelCase__	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)



 @parameterized.expand([(1,)]	)
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any]	):
    # create estimator
    __lowercase								=						self.create_estimator(UpperCAmelCase__	)

    # run training
    estimator.fit()

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

    # extract kpis
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __lowercase								=						(
        Session().describe_training_job(estimator.latest_training_job.name	).get("TrainingTimeInSeconds",					9_9_9_9_9_9	)
    )

    # 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},					UpperCAmelCase__	)




