


"""simple docstring"""







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def _A						(						lowercase		):







    """simple docstring"""
    a 		=np.inf

    def set_batch_size(lowercase		) -> None:
        nonlocal batch_size
        if isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		) and feature.dtype == "binary":
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

    _visit(lowercase       ,    lowercase		)

    return None if batch_size is np.inf else batch_size






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = False ,    __A = False ,    __A = None ,    **__A ,    )					->     Optional[Any]:
     super().__init__(
         __A ,    split=__A ,    features=__A ,    cache_dir=__A ,    keep_in_memory=__A ,    streaming=__A ,    num_proc=__A ,    **__A ,    )
     a 		=path_or_paths if isinstance(__A ,    __A						) else {self.split: path_or_paths}
     a 		=_PACKAGED_DATASETS_MODULES['''parquet'''][1]
     a 		=Parquet(
         cache_dir=__A ,    data_files=__A ,    features=__A ,    hash=__A ,    **__A ,    )





 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     # Build iterable dataset
     if self.streaming:
         a 		=self.builder.as_streaming_dataset(split=self.split						)
     # Build regular (map-style) dataset
     else:
         a 		=None
         a 		=None
         a 		=None
         a 		=None

         self.builder.download_and_prepare(
             download_config=__A ,    download_mode=__A ,    verification_mode=__A ,    base_path=__A ,    num_proc=self.num_proc ,    )
         a 		=self.builder.as_dataset(
             split=self.split ,    verification_mode=__A ,    in_memory=self.keep_in_memory						)
     return dataset






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     Optional[int]:
     a 		=dataset
     a 		=path_or_buf
     a 		=batch_size or get_writer_batch_size(dataset.features						)
     a 		=parquet_writer_kwargs


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

     if isinstance(self.path_or_buf ,    (str, bytes, os.PathLike)						):
         with open(self.path_or_buf ,    '''wb+'''						) as buffer:
             a 		=self._write(file_obj=__A ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     else:
         a 		=self._write(file_obj=self.path_or_buf ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     return written





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    **__A						)					->     int:
     a 		=0
     a 		=parquet_writer_kwargs.pop('''path_or_buf''' ,    __A						)
     a 		=self.dataset.features.arrow_schema

     a 		=pq.ParquetWriter(__A ,    schema=__A ,    **__A						)

     for offset in logging.tqdm(
         range(0 ,    len(self.dataset						) ,    __A						) ,    unit='''ba''' ,    disable=not logging.is_progress_bar_enabled() ,    desc='''Creating parquet from Arrow format''' ,    ):
         a 		=query_table(
             table=self.dataset._data ,    key=slice(__A ,    offset + batch_size						) ,    indices=self.dataset._indices if self.dataset._indices is not None else None ,    )
         writer.write_table(__A						)
         written += batch.nbytes
     writer.close()
     return written



def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if len(UpperCAmelCase_						) != len(UpperCAmelCase_						):
            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						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

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

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[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						:List[Any] 							=       sorted_profit_by_weight[length - i - 1]
            a						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[int] 							=       -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.'''
     )

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

     # Function Call
     calc_profit(profit, weight, max_weight)





