





from __future__ import annotations

_UpperCAmelCase        =				{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 							_UpperCamelCase					:

							def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: dict[str, list[str]]					,   _SCREAMING_SNAKE_CASE: str		)       -> None:




														"""simple docstring"""


														UpperCamelCase_          =  graph
														# mapping node to its parent in resulting breadth first tree
														UpperCamelCase_          =  {}
														UpperCamelCase_          =  source_vertex

							def lowercase (		self: List[Any]		)       -> None:




														"""simple docstring"""


														UpperCamelCase_          =  {self.source_vertex}
														UpperCamelCase_          =  None
														UpperCamelCase_          =  [self.source_vertex]  # first in first out queue

														while queue:
																					UpperCamelCase_          =  queue.pop(0		)
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(_SCREAMING_SNAKE_CASE		)
																																			UpperCamelCase_          =  vertex
																																			queue.append(_SCREAMING_SNAKE_CASE		)



							def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> str:




														"""simple docstring"""


														if target_vertex == self.source_vertex:
																					return self.source_vertex

														UpperCamelCase_          =  self.parent.get(_SCREAMING_SNAKE_CASE		)
														if target_vertex_parent is None:
																					UpperCamelCase_          =  (
																					    f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																					)
																					raise ValueError(_SCREAMING_SNAKE_CASE		)

														return self.shortest_path(_SCREAMING_SNAKE_CASE		) + f'''->{target_vertex}'''


if __name__ == "__main__":
					_UpperCAmelCase        =				Graph(graph, 'G')
					g.breath_first_search()
					print(g.shortest_path('D'))
					print(g.shortest_path('G'))
					print(g.shortest_path('Foo'))








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 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Union[str, Any]		)       -> List[str]:




              """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=_SCREAMING_SNAKE_CASE					,   )
              assert hasattr(self					,   "env"		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"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=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


              TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

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

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # 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}					,   _SCREAMING_SNAKE_CASE		)

