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_50, "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_00, "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_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]				)
class      _A (     unittest.TestCase				):






  def 		_a       (self						)		->   Dict:





       '''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=__lowerCamelCase ,					)
       assert hasattr(self ,					'''env'''						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   Any:





       '''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=__lowerCamelCase ,					instance_count=__lowerCamelCase ,					instance_type=self.instance_type ,					debugger_hook_config=__lowerCamelCase ,					hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} ,					metric_definitions=self.env.metric_definitions ,					distribution=__lowerCamelCase ,					py_version='''py36''' ,					)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   List[str]:





       '''simple docstring'''





       TrainingJobAnalytics(__lowerCamelCase						).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"						)






  @parameterized.expand([(2,)]						)
  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   self.create_estimator(__lowerCamelCase						)

       # 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''' ,					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} ,					__lowerCamelCase						)










"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







    return int((input_a, input_a).count(0		) == 0		)







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


if __name__ == "__main__":
       test_and_gate()
       print(and_gate(1, 0))
       print(and_gate(0, 0))
       print(and_gate(0, 1))
       print(and_gate(1, 1))




