
"""simple docstring"""






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__SCREAMING_SNAKE_CASE							=     datasets.utils.logging.get_logger(__name__)





@dataclass
class 				a__					(						datasets.BuilderConfig       ):
     UpperCAmelCase__         =      1_00_00
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      None





class 				a__					(						datasets.ArrowBasedBuilder       ):
     UpperCAmelCase__         =      ParquetConfig



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      return datasets.DatasetInfo(features=self.config.features   )



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      if not self.config.data_files:
       raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''   )
      UpperCamelCase_     :					Optional[Any]   	=dl_manager.download_and_extract(self.config.data_files   )
      if isinstance(_lowerCamelCase      ,  (str, list, tuple)   ):
       UpperCamelCase_     :					Any   	=data_files
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Tuple   	=[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       UpperCamelCase_     :					int   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={'files': files}   )]
      UpperCamelCase_     :					str   	=[]
      for split_name, files in data_files.items():
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Optional[Any]   	=[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       UpperCamelCase_     :					List[str]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
        for file in itertools.chain.from_iterable(_lowerCamelCase   ):
         with open(_lowerCamelCase      ,  'rb'   ) as f:
          UpperCamelCase_     :					Any   	=datasets.Features.from_arrow_schema(pq.read_schema(_lowerCamelCase   )   )
         break
       splits.append(datasets.SplitGenerator(name=_lowerCamelCase      ,  gen_kwargs={'files': files}   )   )
      return splits



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :pa.Table   ):







      '''simple docstring'''



      if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       UpperCamelCase_     :					List[Any]   	=table_cast(_lowerCamelCase      ,  self.info.features.arrow_schema   )
      return pa_table




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=self.info.features.arrow_schema if self.info.features is not None else None
      if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
        raise ValueError(
            f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''   )
      for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase   )   ):
       with open(_lowerCamelCase      ,  'rb'   ) as f:
        UpperCamelCase_     :					List[str]   	=pq.ParquetFile(_lowerCamelCase   )
        try:
         for batch_idx, record_batch in enumerate(
             parquet_file.iter_batches(batch_size=self.config.batch_size      ,  columns=self.config.columns   )   ):
          UpperCamelCase_     :					List[str]   	=pa.Table.from_batches([record_batch]   )
          # Uncomment for debugging (will print the Arrow table size and elements)
          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
          yield f'''{file_idx}_{batch_idx}''', self._cast_table(_lowerCamelCase   )
        except ValueError as e:
         logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
         raise


"""simple docstring"""






def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 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.
 UpperCamelCase_     :					int   	=[p / w for p, w in zip(__lowercase						,	__lowercase	)]

 # Creating a copy of the list and sorting profit/weight in ascending order
 UpperCamelCase_     :					Optional[int]   	=sorted(__lowercase	)

 # declaring useful variables
 UpperCamelCase_     :					Optional[int]   	=len(__lowercase	)
 UpperCamelCase_     :					Optional[int]   	=0
 UpperCamelCase_     :					List[Any]   	=0
 UpperCamelCase_     :					Optional[int]   	=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
  UpperCamelCase_     :					List[str]   	=sorted_profit_by_weight[length - i - 1]
  UpperCamelCase_     :					Optional[Any]   	=profit_by_weight.index(__lowercase	)
  UpperCamelCase_     :					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.'
 )

 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input profits separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input weights separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)
