




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      ['''pixel_values''']






  def __init__(  self		:     List[Any]				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     PILImageResampling = PIL.Image.BICUBIC				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     Union[int, float] = 1 / 255				,						_A		:     bool = True				,						_A		:     bool = True				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						**_A		:     Optional[int]				,						)		->   None:






       """simple docstring"""







       super().__init__(**_A				)
       lowercase :       Union[str, Any]       =			size if size is not None else {'''height''': 256, '''width''': 256}
       lowercase :       List[str]       =			get_size_dict(_A				)
       lowercase :       Optional[Any]       =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase :       List[Any]       =			get_size_dict(_A				,						param_name='''crop_size'''				)

       lowercase :       Tuple       =			do_resize
       lowercase :       Any       =			size
       lowercase :       str       =			resample
       lowercase :       Optional[Any]       =			do_center_crop
       lowercase :       Tuple       =			crop_size
       lowercase :       Dict       =			do_rescale
       lowercase :       Dict       =			rescale_factor
       lowercase :       Dict       =			do_normalize
       lowercase :       List[str]       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase :       Tuple       =			image_std if image_std is not None else IMAGENET_STANDARD_STD






  def  __a      (  self		:     int				,						_A		:     np.ndarray				,						_A		:     Dict[str, int]				,						_A		:     PILImageResampling = PIL.Image.BICUBIC				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     str				,						)		->   np.ndarray:






       """simple docstring"""







       lowercase :       Tuple       =			get_size_dict(_A				)
       if "height" not in size or "width" not in size:
            raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""				)
       return resize(
           _A				,						size=(size['''height'''], size['''width'''])				,						resample=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     str				,						_A		:     np.ndarray				,						_A		:     Dict[str, int]				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     Tuple				,						)		->   np.ndarray:






       """simple docstring"""







       lowercase :       List[Any]       =			get_size_dict(_A				)
       if "height" not in size or "width" not in size:
            raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""				)
       return center_crop(_A				,						size=(size['''height'''], size['''width'''])				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     Tuple				,						_A		:     np.ndarray				,						_A		:     Union[int, float]				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     Dict				,						)		->   Dict:






       """simple docstring"""







       return rescale(_A				,						scale=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     List[Any]				,						_A		:     np.ndarray				,						_A		:     Union[float, List[float]]				,						_A		:     Union[float, List[float]]				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     Optional[Any]				,						)		->   np.ndarray:






       """simple docstring"""







       return normalize(_A				,						mean=_A				,						std=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     Tuple				,						_A		:     ImageInput				,						_A		:     bool = None				,						_A		:     Dict[str, int] = None				,						_A		:     Any=None				,						_A		:     bool = None				,						_A		:     Dict[str, int] = None				,						_A		:     bool = None				,						_A		:     float = None				,						_A		:     bool = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[str, TensorType]] = None				,						_A		:     ChannelDimension = ChannelDimension.FIRST				,						**_A		:     Any				,						)		->   PIL.Image.Image:






       """simple docstring"""







       lowercase :       List[Any]       =			do_resize if do_resize is not None else self.do_resize
       lowercase :       str       =			resample if resample is not None else self.resample
       lowercase :       Optional[Any]       =			do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase :       Tuple       =			do_rescale if do_rescale is not None else self.do_rescale
       lowercase :       Union[str, Any]       =			rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase :       Union[str, Any]       =			do_normalize if do_normalize is not None else self.do_normalize
       lowercase :       Any       =			image_mean if image_mean is not None else self.image_mean
       lowercase :       Any       =			image_std if image_std is not None else self.image_std

       lowercase :       Tuple       =			size if size is not None else self.size
       lowercase :       str       =			get_size_dict(_A				)
       lowercase :       Optional[int]       =			crop_size if crop_size is not None else self.crop_size
       lowercase :       List[Any]       =			get_size_dict(_A				,						param_name='''crop_size'''				)

       lowercase :       List[str]       =			make_list_of_images(_A				)

       if not valid_images(_A				):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

       if do_resize and size is None or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

       if do_center_crop and crop_size is None:
            raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

       # All transformations expect numpy arrays.
       lowercase :       Tuple       =			[to_numpy_array(_A				) for image in images]

       if do_resize:
            lowercase :       Optional[Any]       =			[self.resize(image=_A				,						size=_A				,						resample=_A				) for image in images]

       if do_center_crop:
            lowercase :       List[Any]       =			[self.center_crop(image=_A				,						size=_A				) for image in images]

       if do_rescale:
            lowercase :       Tuple       =			[self.rescale(image=_A				,						scale=_A				) for image in images]

       if do_normalize:
            lowercase :       Union[str, Any]       =			[self.normalize(image=_A				,						mean=_A				,						std=_A				) for image in images]

       lowercase :       Tuple       =			[to_channel_dimension_format(_A				,						_A				) for image in images]

       lowercase :       List[Any]       =			{'''pixel_values''': images}
       return BatchFeature(data=_A				,						tensor_type=_A				)





def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     if len(__magic_name__   ) != len(__magic_name__   ):
          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.
     lowercase :       str       =			[p / w for p, w in zip(__magic_name__			,		__magic_name__   )]

     # Creating a copy of the list and sorting profit/weight in ascending order
     lowercase :       str       =			sorted(__magic_name__   )

     # declaring useful variables
     lowercase :       Union[str, Any]       =			len(__magic_name__   )
     lowercase :       Optional[int]       =			0
     lowercase :       Optional[int]       =			0
     lowercase :       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
          lowercase :       Optional[int]       =			sorted_profit_by_weight[length - i - 1]
          lowercase :       Union[str, Any]       =			profit_by_weight.index(__magic_name__   )
          lowercase :       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(x) for x in input('Input profits separated by spaces: ').split()]
 lowerCAmelCase_						=			[int(x) for x in input('Input weights separated by spaces: ').split()]
 lowerCAmelCase_						=			int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)