




"""simple docstring"""







import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,_lowercase    ):


    """simple docstring"""







    def constraint_to_multiple_of(_lowercase			,_lowercase			,_lowercase=0			,_lowercase=None    ):
        UpperCamelCase              =	round(val / multiple    ) * multiple

        if max_val is not None and x > max_val:
            UpperCamelCase              =	math.floor(val / multiple    ) * multiple

        if x < min_val:
            UpperCamelCase              =	math.ceil(val / multiple    ) * multiple

        return x

    UpperCamelCase              =	(output_size, output_size) if isinstance(_lowercase			,_lowercase    ) else output_size

    UpperCamelCase       ,	UpperCamelCase              =	get_image_size(_lowercase    )
    UpperCamelCase       ,	UpperCamelCase              =	output_size

    # determine new height and width
    UpperCamelCase              =	output_height / input_height
    UpperCamelCase              =	output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width    ) < abs(1 - scale_height    ):
            # fit width
            UpperCamelCase              =	scale_width
        else:
            # fit height
            UpperCamelCase              =	scale_height

    UpperCamelCase              =	constraint_to_multiple_of(scale_height * input_height			,multiple=_lowercase    )
    UpperCamelCase              =	constraint_to_multiple_of(scale_width * input_width			,multiple=_lowercase    )

    return (new_height, new_width)


class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       ['''pixel_values''']







   def __init__(		self       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = PILImageResampling.BILINEAR       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = 1       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = 1 / 2_5_5       ,					lowerCamelCase_ = True       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   None:
       super().__init__(**lowerCamelCase_)
       UpperCamelCase              =	size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
       UpperCamelCase              =	get_size_dict(lowerCamelCase_)
       UpperCamelCase              =	do_resize
       UpperCamelCase              =	size
       UpperCamelCase              =	keep_aspect_ratio
       UpperCamelCase              =	ensure_multiple_of
       UpperCamelCase              =	resample
       UpperCamelCase              =	do_rescale
       UpperCamelCase              =	rescale_factor
       UpperCamelCase              =	do_normalize
       UpperCamelCase              =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase              =	image_std if image_std is not None else IMAGENET_STANDARD_STD







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = 1       ,					lowerCamelCase_ = PILImageResampling.BICUBIC       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   np.ndarray:
       UpperCamelCase              =	get_size_dict(lowerCamelCase_)
       if "height" not in size or "width" not in size:
           raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}')
       UpperCamelCase              =	get_resize_output_image_size(
           lowerCamelCase_       ,					output_size=(size['''height'''], size['''width'''])       ,					keep_aspect_ratio=lowerCamelCase_       ,					multiple=lowerCamelCase_       ,					)
       return resize(lowerCamelCase_       ,					size=lowerCamelCase_       ,					resample=lowerCamelCase_       ,					data_format=lowerCamelCase_       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   Any:
       return rescale(lowerCamelCase_       ,					scale=lowerCamelCase_       ,					data_format=lowerCamelCase_       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   np.ndarray:
       return normalize(lowerCamelCase_       ,					mean=lowerCamelCase_       ,					std=lowerCamelCase_       ,					data_format=lowerCamelCase_       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = ChannelDimension.FIRST       ,					**lowerCamelCase_       ,					)				->   PIL.Image.Image:
       UpperCamelCase              =	do_resize if do_resize is not None else self.do_resize
       UpperCamelCase              =	size if size is not None else self.size
       UpperCamelCase              =	get_size_dict(lowerCamelCase_)
       UpperCamelCase              =	keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       UpperCamelCase              =	ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       UpperCamelCase              =	resample if resample is not None else self.resample
       UpperCamelCase              =	do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase              =	rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase              =	do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase              =	image_mean if image_mean is not None else self.image_mean
       UpperCamelCase              =	image_std if image_std is not None else self.image_std

       UpperCamelCase              =	make_list_of_images(lowerCamelCase_)

       if not valid_images(lowerCamelCase_):
           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_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.
       UpperCamelCase              =	[to_numpy_array(lowerCamelCase_) for image in images]

       if do_resize:
           UpperCamelCase              =	[self.resize(image=lowerCamelCase_       ,					size=lowerCamelCase_       ,					resample=lowerCamelCase_) for image in images]

       if do_rescale:
           UpperCamelCase              =	[self.rescale(image=lowerCamelCase_       ,					scale=lowerCamelCase_) for image in images]

       if do_normalize:
           UpperCamelCase              =	[self.normalize(image=lowerCamelCase_       ,					mean=lowerCamelCase_       ,					std=lowerCamelCase_) for image in images]

       UpperCamelCase              =	[to_channel_dimension_format(lowerCamelCase_       ,					lowerCamelCase_) for image in images]

       UpperCamelCase              =	{'''pixel_values''': images}
       return BatchFeature(data=lowerCamelCase_       ,					tensor_type=lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Optional[int]:
       UpperCamelCase              =	outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
           if len(lowerCamelCase_) != len(lowerCamelCase_):
               raise ValueError(
                   '''Make sure that you pass in as many target sizes as the batch dimension of the logits''')

           if is_torch_tensor(lowerCamelCase_):
               UpperCamelCase              =	target_sizes.numpy()

           UpperCamelCase              =	[]

           for idx in range(len(lowerCamelCase_)):
               UpperCamelCase              =	torch.nn.functional.interpolate(
                   logits[idx].unsqueeze(dim=0)       ,					size=target_sizes[idx]       ,					mode='''bilinear'''       ,					align_corners=lowerCamelCase_)
               UpperCamelCase              =	resized_logits[0].argmax(dim=0)
               semantic_segmentation.append(lowerCamelCase_)
       else:
           UpperCamelCase              =	logits.argmax(dim=1)
           UpperCamelCase              =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

       return semantic_segmentation


import random

class    _lowercase				:

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    str       ) ->			tuple[list[int], list[int]]:
         """simple docstring"""





         A_							       =		[ord(lowerCamelCase__       ) for i in text]
         A_							       =		[]
         A_							       =		[]
         for i in plain:
               A_							       =		random.randint(1			,						3_0_0       )
               A_							       =		(i + k) * k
               cipher.append(lowerCamelCase__       )
               key.append(lowerCamelCase__       )
         return cipher, key





   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    list[int]			,						lowerCamelCase__	:    list[int]       ) ->			str:
         """simple docstring"""





         A_							       =		[]
         for i in range(len(lowerCamelCase__       )       ):
               A_							       =		int((cipher[i] - (key[i]) ** 2) / key[i]       )
               plain.append(chr(lowerCamelCase__       )       )
         return "".join(lowerCamelCase__       )


if __name__ == "__main__":
      __lowercase      ,				__lowercase				      =						Onepad().encrypt("""Hello""")
      print(c, k)
      print(Onepad().decrypt(c, k))





