


'''simple docstring'''






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_5_0, "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_0_0, "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_0_0, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]    )
class  __magic_name__							(		unittest.TestCase    ):






       def       lowerCAmelCase		(      self)						->  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=_lowerCamelCase  ,							)
          assert hasattr(self  ,							'env')






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
          # distributed data settings
          _UpperCAmelCase       :       Union[str, Any]							   ={'''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       lowerCAmelCase		(      self  ,							snake_case)						->  List[Any]:




          '''simple docstring'''
          TrainingJobAnalytics(_lowerCamelCase).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")






       @parameterized.expand([(2,)])
       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          # create estimator
          _UpperCAmelCase       :       Any							   =self.create_estimator(_lowerCamelCase)

          # run training
          estimator.fit()

          # result dataframe
          _UpperCAmelCase       :       Dict							   =TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

          # extract kpis
          _UpperCAmelCase       :       int							   =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
          _UpperCAmelCase       :       Any							   =list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          _UpperCAmelCase       :       Dict							   =(
              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}  ,							_lowerCamelCase)





'''simple docstring'''






def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   def count_of_possible_combinations(__lowerCamelCase	:							int							) -> int:
      if target < 0:
         return 0
      if target == 0:
         return 1
      return sum(count_of_possible_combinations(target - item							) for item in array							)

   return count_of_possible_combinations(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   def count_of_possible_combinations_with_dp_array(
       __lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]							) -> int:
      if target < 0:
         return 0
      if target == 0:
         return 1
      if dp_array[target] != -1:
         return dp_array[target]
      _UpperCAmelCase       :       str							   =sum(
          count_of_possible_combinations_with_dp_array(target - item	,   __lowerCamelCase							)
          for item in array							)
      _UpperCAmelCase       :       Optional[Any]							   =answer
      return answer

   _UpperCAmelCase       :       int							   =[-1] * (target + 1)
   return count_of_possible_combinations_with_dp_array(__lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Tuple							   =[0] * (target + 1)
   _UpperCAmelCase       :       Dict							   =1

   for i in range(1	,   target + 1							):
      for j in range(__lowerCamelCase							):
         if i - array[j] >= 0:
            dp_array[i] += dp_array[i - array[j]]

   return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowercase							=3
 lowercase							=5
 lowercase							=[1, 2, 5]
 print(combination_sum_iv(n, array, target))

