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 			lowercase_      (self     :						List[str]		)						->				Optional[int]:
         """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=__UpperCAmelCase       ,  )
         assert hasattr(self       ,  "env"		)


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Any		)						->				Union[str, 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=__UpperCAmelCase       ,  instance_count=__UpperCAmelCase       ,  instance_type=self.instance_type       ,  debugger_hook_config=__UpperCAmelCase       ,  hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  distribution=__UpperCAmelCase       ,  py_version="py36"       ,  )


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						Tuple		)						->				Any:
         """simple docstring"""



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





   @parameterized.expand([(2,)]		)
   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[Any]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator(__UpperCAmelCase		)

         # 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"       ,  9_9_9_9_9_9		)
         )

         # 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}       ,  __UpperCAmelCase		)




import math
import random




def    lowerCAmelCase_ (     __A,  __A = False							)     ->				float:







      '''simple docstring'''


      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
UpperCamelCase__											=					0.0_2




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      UpperCAmelCase__								=  float(2 * (random.randint(1,  100							)) - 1							)

      for _ in range(__A							):
            # Forward propagation
            UpperCAmelCase__								=  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            UpperCAmelCase__								=  (expected / 100) - layer_a
            # Error delta
            UpperCAmelCase__								=  layer_1_error * sigmoid_function(__A,  __A							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 100


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					int(input('Expected value: '))
      UpperCamelCase__											=					int(input('Number of propagations: '))
      print(forward_propagation(expected, number_propagations))



