



"""simple docstring"""







import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge",
            "results": {"train_runtime": 6_5_0, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]    )
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





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






				"""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=_snake_case			,)
				assert hasattr(self			,'''env'''						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  Any=1						)					->		Any:






				"""simple docstring"""



				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}-single"""			,instance_count=_snake_case			,instance_type=self.instance_type			,debugger_hook_config=_snake_case			,hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}			,metric_definitions=self.env.metric_definitions			,py_version='''py36'''			,)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  List[Any]						)					->		List[Any]:






				"""simple docstring"""



				TrainingJobAnalytics(_snake_case						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			self.create_estimator()

				# run training
				estimator.fit()

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

				# extract kpis
				lowercase__   :    Any				=			list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']						)
				lowercase__   :    Dict				=			list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']						)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				lowercase__   :    List[Any]				=			(
				    Session().describe_training_job(estimator.latest_training_job.name						).get('''TrainingTimeInSeconds'''			,999_999						)
				)

				# 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}			,_snake_case						)





"""simple docstring"""







def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      List[Any]:
		print('''\nThe shortest path matrix using Floyd Warshall algorithm\n'''	)
		for i in range(__lowerCamelCase	):
				for j in range(__lowerCamelCase	):
						if dist[i][j] != float('''inf'''	):
								print(int(dist[i][j]	)						,     end='''\t'''	)
						else:
								print('''INF'''						,     end='''\t'''	)
				print()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    str				=			[[float('''inf'''	) for _ in range(__lowerCamelCase	)] for _ in range(__lowerCamelCase	)]

		for i in range(__lowerCamelCase	):
				for j in range(__lowerCamelCase	):
						lowercase__   :    List[str]				=			graph[i][j]

						# check vertex k against all other vertices (i, j)
		for k in range(__lowerCamelCase	):
				# looping through rows of graph array
				for i in range(__lowerCamelCase	):
						# looping through columns of graph array
						for j in range(__lowerCamelCase	):
								if (
								    dist[i][k] != float('''inf'''	)
								    and dist[k][j] != float('''inf'''	)
								    and dist[i][k] + dist[k][j] < dist[i][j]
								):
										lowercase__   :    str				=			dist[i][k] + dist[k][j]

		_print_dist(__lowerCamelCase						,     __lowerCamelCase	)
		return dist, v


if __name__ == "__main__":
							lowerCAmelCase_   						=   int(input('Enter number of vertices: '))
							lowerCAmelCase_   						=   int(input('Enter number of edges: '))

							lowerCAmelCase_   						=   [[float('inf') for i in range(v)] for j in range(v)]

							for i in range(v):
														lowerCAmelCase_   						=   0.0

														# src and dst are indices that must be within the array size graph[e][v]
														# failure to follow this will result in an error
							for i in range(e):
														print('\nEdge ', i + 1)
														lowerCAmelCase_   						=   int(input('Enter source:'))
														lowerCAmelCase_   						=   int(input('Enter destination:'))
														lowerCAmelCase_   						=   float(input('Enter weight:'))
														lowerCAmelCase_   						=   weight

							floyd_warshall(graph, v)

							# Example Input
							# Enter number of vertices: 3
							# Enter number of edges: 2

							# # generated graph from vertex and edge inputs
							# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
							# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

							# specify source, destination and weight for edge #1
							# Edge  1
							# Enter source:1
							# Enter destination:2
							# Enter weight:2

							# specify source, destination and weight for edge #2
							# Edge  2
							# Enter source:2
							# Enter destination:1
							# Enter weight:1

							# # Expected Output from the vertice, edge and src, dst, weight inputs!!
							# 0		INF	INF
							# INF	0	2
							# INF	1	0
