



import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
__lowerCamelCase								=   """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
__lowerCamelCase								=   """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
__lowerCamelCase								=   """"""

# TODO: Add the licence for the dataset here if you can find it
__lowerCamelCase								=   """"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
__lowerCamelCase								=   {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}



class 						_snake_case (     datasets.GeneratorBasedBuilder			):





 """simple docstring"""



 lowerCamelCase_       =     datasets.Version('''1.1.0'''			)

 # This is an example of a dataset with multiple configurations.
 # If you don't want/need to define several sub-sets in your dataset,
 # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

 # If you need to make complex sub-parts in the datasets with configurable options
 # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
 # BUILDER_CONFIG_CLASS = MyBuilderConfig

 # You will be able to load one or the other configurations in the following list with
 # data = datasets.load_dataset('my_dataset', 'first_domain')
 # data = datasets.load_dataset('my_dataset', 'second_domain')
 lowerCamelCase_       =     [
     datasets.BuilderConfig(name='''first_domain'''   ,version=lowerCamelCase   ,description='''This part of my dataset covers a first domain'''			),
     datasets.BuilderConfig(name='''second_domain'''   ,version=lowerCamelCase   ,description='''This part of my dataset covers a second domain'''			),
 ]

 lowerCamelCase_       =     '''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
     _A          =      datasets.Features(
         {
             '''sentence''': datasets.Value('''string'''   ),
             '''option1''': datasets.Value('''string'''   ),
             '''answer''': datasets.Value('''string'''   )
             # These are the features of your dataset like images, labels ...
         }   )
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
     _A          =      datasets.Features(
         {
             '''sentence''': datasets.Value('''string'''   ),
             '''option2''': datasets.Value('''string'''   ),
             '''second_domain_answer''': datasets.Value('''string'''   )
             # These are the features of your dataset like images, labels ...
         }   )
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION		,      features=a		,      homepage=_HOMEPAGE		,      license=_LICENSE		,      citation=_CITATION		,      )
 def lowercase_						(  self		,      a   )		->							List[str]:




   """simple docstring"""







   _A          =      _URLS[self.config.name]
   _A          =      dl_manager.download_and_extract(a   )
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN		,      gen_kwargs={
               '''filepath''': os.path.join(a		,      '''train.jsonl'''   ),
               '''split''': '''train''',
           }		,      ),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION		,      gen_kwargs={
               '''filepath''': os.path.join(a		,      '''dev.jsonl'''   ),
               '''split''': '''dev''',
           }		,      ),
       datasets.SplitGenerator(
           name=datasets.Split.TEST		,      gen_kwargs={
               '''filepath''': os.path.join(a		,      '''test.jsonl'''   ),
               '''split''': '''test'''
           }		,      ),
   ]



 def lowercase_						(  self		,      a		,      a   )		->							Dict:




   """simple docstring"""







   with open(a		,      encoding='''utf-8'''   ) as f:
     for key, row in enumerate(a   ):
       _A          =      json.loads(a   )
       if self.config.name == "first_domain":
         # Yields examples as (key, example) tuples
         yield key, {
             "sentence": data["sentence"],
             "option1": data["option1"],
             "answer": "" if split == "test" else data["answer"],
         }
       else:
         yield key, {
             "sentence": data["sentence"],
             "option2": data["option2"],
             "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
         }




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case							)				->	list[int]:
  _A          =      [0] * len(__snake_case							)
  for i in range(1  ,			len(__snake_case							)							):
    _A          =      duration_times[i - 1] + waiting_times[i - 1]
  return waiting_times




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(__snake_case							)
  ]




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [1, 2, 3]

    # ensure that we actually have processes
    if len(processes) == 0:
        print("""Zero amount of processes""")
        raise SystemExit(0)

    # duration time of all processes
    __lowerCamelCase								=   [1_9, 8, 9]

    # ensure we can match each id to a duration time
    if len(duration_times) != len(processes):
        print("""Unable to match all id's with their duration time""")
        raise SystemExit(0)

    # get the waiting times and the turnaround times
    __lowerCamelCase								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   calculate_average_turnaround_time(turnaround_times)

    # print all the results
    print("""Process ID\tDuration Time\tWaiting Time\tTurnaround Time""")
    for i, process in enumerate(processes):
        print(
            f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
            f'''{turnaround_times[i]}'''
        )
    print(f'''Average waiting time = {average_waiting_time}''')
    print(f'''Average turn around time = {average_turnaround_time}''')