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   lowercase_    (    unittest.TestCase		):



	"""simple docstring"""





	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[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      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE=1							)		->str:
								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      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Tuple:
								TrainingJobAnalytics(a__							).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"							)




	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								lowerCAmelCase      						=						self.create_estimator()

								# run training
								estimator.fit()

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

								# extract kpis
								lowerCAmelCase      						=						list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
								lowerCAmelCase      						=						list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']							)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								lowerCAmelCase      						=						(
								    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}						,     a__							)










def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       _lowerCamelCase				:  int						=		(boundary[1] - boundary[0]) / steps
       _lowerCamelCase				:  Any						=		boundary[0]
       _lowerCamelCase				:  Tuple						=		boundary[1]
       _lowerCamelCase				:  Optional[Any]						=		make_points(lowercase_			,     lowercase_			,     lowercase_   )
       _lowerCamelCase				:  Dict						=		0.0
       y += (h / 2.0) * f(lowercase_   )
       for i in x_i:
              # print(i)
              y += h * f(lowercase_   )
       y += (h / 2.0) * f(lowercase_   )
       return y




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Dict						=		a + h
       while x < (b - h):
              yield x
              _lowerCamelCase				:  Dict						=		x + h




def  __UpperCAmelCase(   lowercase_   ):  # enter your function here
       _lowerCamelCase				:  List[str]						=		(x - 0) * (x - 0)
       return y




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  List[Any]						=		0.0  # Lower bound of integration
       _lowerCamelCase				:  Union[str, Any]						=		1.0  # Upper bound of integration
       _lowerCamelCase				:  Dict						=		1_0.0  # define number of steps or resolution
       _lowerCamelCase				:  Optional[int]						=		[a, b]  # define boundary of integration
       _lowerCamelCase				:  str						=		method_a(lowercase_			,     lowercase_   )
       print(F"""y = {y}"""   )


if __name__ == "__main__":
       main()

