


'''simple docstring'''





def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Optional[Any]						,					__A				:    Tuple       )			->	Union[str, Any]:

       """simple docstring"""

       if len(lowercase__       ) != len(lowercase__       ):
              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_      :	Tuple							=						[p / w for p, w in zip(lowercase__						,					lowercase__       )]

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

       # declaring useful variables
       a_      :	List[Any]							=						len(lowercase__       )
       a_      :	Union[str, Any]							=						0
       a_      :	Union[str, Any]							=						0
       a_      :	str							=						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_      :	Tuple							=						sorted_profit_by_weight[length - i - 1]
              a_      :	List[str]							=						profit_by_weight.index(lowercase__       )
              a_      :	str							=						-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.'
   )

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

   # Function Call
   calc_profit(profit, weight, max_weight)






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Tuple       )			->	Any:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    Any						,					__A				:    Tuple       )			->	Tuple:

       """simple docstring"""

       a_      :	Dict							=						tmp_path / 'cache'
       a_      :	int							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Union[str, Any]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Dict						,					__A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

       a_      :	Tuple							=						tmp_path / 'cache'
       a_      :	str							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	List[str]							=						features.copy() if features else default_expected_features
       a_      :	int							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						ParquetDatasetReader(__A						,					features=__A						,					cache_dir=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    List[str]						,					__A				:    int       )			->	List[str]:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	str							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	List[str]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					split=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    str						,					__A				:    Optional[int]       )			->	Any:

       """simple docstring"""

       if issubclass(__A						,					__A       ):
              a_      :	Tuple							=						parquet_path
       elif issubclass(__A						,					__A       ):
              a_      :	str							=						[parquet_path]
       a_      :	int							=						tmp_path / 'cache'
       a_      :	int							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	str							=						ParquetDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Dict						,					__A				:    Optional[Any]=("train",)       )			->	Optional[int]:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       for split in splits:
              a_      :	str							=						dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Union[str, Any]							=						tmp_path / 'cache'
       a_      :	Any							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Tuple							=						ParquetDatasetReader(
                  {'train': parquet_path}						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]						,					__A				:    Tuple       )			->	List[Any]:

       """simple docstring"""

       a_      :	Optional[Any]							=						tmp_path / 'cache'
       a_      :	List[str]							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	Optional[int]							=						features.copy() if features else default_expected_features
       a_      :	Tuple							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	Optional[Any]							=						ParquetDatasetReader({'train': parquet_path}						,					features=__A						,					cache_dir=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[Any]						,					__A				:    Optional[Any]       )			->	Any:

       """simple docstring"""

       if split:
              a_      :	Any							=						{split: parquet_path}
       else:
              a_      :	Dict							=						'train'
              a_      :	int							=						{'train': parquet_path, 'test': parquet_path}
       a_      :	int							=						tmp_path / 'cache'
       a_      :	List[Any]							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	Tuple							=						ParquetDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A						,					splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

       a_      :	List[str]							=						ParquetDatasetWriter(__A						,					tmp_path / 'foo.parquet'       )
       assert writer.write() > 0
       a_      :	List[str]							=						pq.ParquetFile(tmp_path / 'foo.parquet'       )
       a_      :	Dict							=						pf.read()
       assert dataset.data.table == output_table







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any       )			->	Optional[int]:

       """simple docstring"""

       a_      :	str							=						str(shared_datadir / 'test_image_rgb.jpg'       )
       a_      :	List[Any]							=						{'image': [image_path]}
       a_      :	int							=						Features({'image': Image()}       )
       a_      :	List[Any]							=						Dataset.from_dict(__A						,					features=__A       )
       a_      :	str							=						ParquetDatasetWriter(__A						,					tmp_path / 'foo.parquet'       )
       assert writer.write() > 0

       a_      :	str							=						Dataset.from_parquet(str(tmp_path / 'foo.parquet'       )       )
       assert dataset.features == reloaded_dataset.features

       a_      :	str							=						ParquetDatasetReader(str(tmp_path / 'foo.parquet'       )						,					streaming=__A       ).read()
       assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    'feature, expected'						,					[
        (Features({'foo': Value('int32'       )}       ), None),
        (Features({'image': Image(), 'foo': Value('int32'       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()       )}       ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[str]       )			->	List[str]:

       """simple docstring"""

       assert get_writer_batch_size(__A       ) == expected


