

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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]	)
class      lowerCamelCase_      (	unittest.TestCase	):




	def        lowerCAmelCase_       (     self						:     Optional[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=__A					,      )
		assert hasattr(self					,      """env"""							)




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     List[Any]=1							):
		# 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}-single"""					,      instance_count=__A					,      instance_type=self.instance_type					,      debugger_hook_config=__A					,      hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}					,      metric_definitions=self.env.metric_definitions					,      py_version="""py36"""					,      )




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     List[Any]							):
		TrainingJobAnalytics(__A							).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		# create estimator
		__A :			Tuple		    =			self.create_estimator()

		# run training
		estimator.fit()

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

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




	def __init__(     self						:     Dict					,      __A						:     Tuple					,      __A						:     Optional[int]					,      __A						:     int							):
		__A :			List[str]		    =			name
		__A :			Optional[int]		    =			value
		__A :			Optional[Any]		    =			weight




	def __repr__(     self						:     Any							):
		return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.value




	def        lowerCAmelCase_       (     self						:     str							):
		return self.name




	def        lowerCAmelCase_       (     self						:     str							):
		return self.weight




	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.value / self.weight






def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   Union[str, Any]       )					->				int:
	__A :			Tuple		    =			[]
	for i in range(len(a__       )       ):
		menu.append(Things(name[i]			,value[i]			,weight[i]       )       )
	return menu
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]       )					->				Tuple:
	__A :			Optional[int]		    =			sorted(a__			,key=a__			,reverse=a__       )
	__A :			Optional[Any]		    =			[]
	__A    ,				__A :			Tuple		    =			0.0, 0.0
	for i in range(len(a__       )       ):
		if (total_cost + items_copy[i].get_weight()) <= max_cost:
			result.append(items_copy[i]       )
			total_cost += items_copy[i].get_weight()
			total_value += items_copy[i].get_value()
	return (result, total_value)
def 							__SCREAMING_SNAKE_CASE     (   )					->				List[Any]:
	pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()





