'''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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]			)
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			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=UpperCamelCase__, )
        assert hasattr(self, 'env'     )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Union[str, Any]     )  ->			Any:
        _A    		=  {
            "enabled": True,
            "processes_per_host": 8,
        }
        _A    		=  {
            "enabled": True,
            "parameters": {
                "microbatches": 4,
                "placement_strategy": "spread",
                "pipeline": "interleaved",
                "optimize": "speed",
                "partitions": 4,
                "ddp": True,
            },
        }

        _A    		=  {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

        _A    		=  "trainer" if self.script == "run_glue.py" else "smtrainer"
        # 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}-{instance_count}-smp-{name_extension}', instance_count=UpperCamelCase__, instance_type=self.instance_type, debugger_hook_config=UpperCamelCase__, hyperparameters={
                **self.env.hyperparameters,
                'model_name_or_path': self.model_name_or_path,
                'max_steps': 5_00,
            }, metric_definitions=self.env.metric_definitions, distribution=UpperCamelCase__, py_version='py36', )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			str     )  ->			Dict:
        TrainingJobAnalytics(UpperCamelCase__     ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'     )

   @parameterized.expand([(1,)]     )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Union[str, Any]     )  ->			List[str]:
        _A    		=  self.create_estimator(UpperCamelCase__     )

        # run training
        estimator.fit()

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

        # extract kpis
        _A    		=  list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']     )
        _A    		=  list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']     )
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        _A    		=  (
            Session().describe_training_job(estimator.latest_training_job.name     ).get('TrainingTimeInSeconds', 99_99_99     )
        )

        # 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__     )










def   __snake_case    (       _lowerCAmelCase			:			list					,					_lowerCAmelCase			:			list					,					_lowerCAmelCase			:			int						)			->    int:
   if len(_lowerCAmelCase						) != len(_lowerCAmelCase						):
      raise ValueError("The length of profit and weight must be same."						)
   if max_weight <= 0:
      raise ValueError("max_weight must greater than zero."						)
   if any(p < 0 for p in profit						):
      raise ValueError("Profit can not be negative."						)
   if any(w < 0 for w in weight						):
      raise ValueError("Weight can not be negative."						)

   # List created to store profit gained for the 1kg in case of each weight
   # respectively.  Calculate and append profit/weight for each element.
   A_       :					Dict        =      [p / w for p, w in zip(_lowerCAmelCase					,					_lowerCAmelCase						)]

   # Creating a copy of the list and sorting profit/weight in ascending order
   A_       :					Optional[Any]        =      sorted(_lowerCAmelCase						)

   # declaring useful variables
   A_       :					List[Any]        =      len(_lowerCAmelCase						)
   A_       :					List[Any]        =      0
   A_       :					Any        =      0
   A_       :					List[Any]        =      0

   # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
   while limit <= max_weight and i < length:
      # flag value for encountered greatest element in sorted_profit_by_weight
      A_       :					List[Any]        =      sorted_profit_by_weight[length - i - 1]
      A_       :					List[Any]        =      profit_by_weight.index(_lowerCAmelCase						)
      A_       :					List[Any]        =      -1

      # check if the weight encountered is less than the total weight
      # encountered before.
      if max_weight - limit >= weight[index]:
         limit += weight[index]
         # Adding profit gained for the given weight 1 ===
         # weight[index]/weight[index]
         gain += 1 * profit[index]
      else:
         # Since the weight encountered is greater than limit, therefore take the
         # required number of remaining kgs and calculate profit for it.
         # weight remaining / weight[index]
         gain += (max_weight - limit) / weight[index] * profit[index]
         break
      i += 1
   return gain


if __name__ == "__main__":
 print(
     '''Input profits, weights, and then max_weight (all positive ints) separated by '''
     '''spaces.'''
 )

 _lowerCAmelCase				:   int						       =						[int(x) for x in input('''Input profits separated by spaces: ''').split()]
 _lowerCAmelCase				:   Dict						       =						[int(x) for x in input('''Input weights separated by spaces: ''').split()]
 _lowerCAmelCase				:   Union[str, Any]						       =						int(input('''Max weight allowed: '''))

 # Function Call
 calc_profit(profit, weight, max_weight)
