

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


A_       : str         =			logging.get_logger(__name__)



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       ['''pixel_values''']






					def __init__( self    ,       A__ = True    ,       A__ = None    ,       A__ = PILImageResampling.BILINEAR    ,       A__ = True    ,       A__ = None    ,       A__ = True    ,       A__ = 1 / 255    ,       A__ = True    ,       A__ = None    ,       A__ = None    ,       **A__    ,       ):
						super().__init__(**A__			)
						A__							:    Tuple        =		size if size is not None else {"""shortest_edge""": 256}
						A__							:    Dict        =		get_size_dict(A__    ,       default_to_square=A__			)
						A__							:    int        =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						A__							:    Optional[Any]        =		get_size_dict(A__    ,       param_name="""crop_size"""			)
						A__							:    Any        =		do_resize
						A__							:    Optional[Any]        =		size
						A__							:    Dict        =		resample
						A__							:    Optional[Any]        =		do_center_crop
						A__							:    Dict        =		crop_size
						A__							:    Optional[Any]        =		do_rescale
						A__							:    List[str]        =		rescale_factor
						A__							:    Optional[int]        =		do_normalize
						A__							:    List[Any]        =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A__							:    Any        =		image_std if image_std is not None else IMAGENET_STANDARD_STD






					def   __A			( self    ,       A__    ,       A__    ,       A__ = PILImageResampling.BICUBIC    ,       A__ = None    ,       **A__    ,       ):
						A__							:    Union[str, Any]        =		get_size_dict(A__    ,       default_to_square=A__			)
						if "shortest_edge" not in size:
							raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""			)
						A__							:    Optional[int]        =		get_resize_output_image_size(A__    ,       size=size["""shortest_edge"""]    ,       default_to_square=A__			)
						return resize(A__    ,       size=A__    ,       resample=A__    ,       data_format=A__    ,       **A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__ = None    ,       **A__    ,       ):
						A__							:    List[Any]        =		get_size_dict(A__			)
						if "height" not in size or "width" not in size:
							raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""			)
						return center_crop(A__    ,       size=(size["""height"""], size["""width"""])    ,       data_format=A__    ,       **A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__ = None    ,       **A__			):
						return rescale(A__    ,       scale=A__    ,       data_format=A__    ,       **A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__ = None    ,       **A__    ,       ):
						return normalize(A__    ,       mean=A__    ,       std=A__    ,       data_format=A__    ,       **A__			)






					def   __A			( self    ,       A__    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = None    ,       A__ = ChannelDimension.FIRST    ,       **A__    ,       ):
						A__							:    str        =		do_resize if do_resize is not None else self.do_resize
						A__							:    List[Any]        =		size if size is not None else self.size
						A__							:    Dict        =		get_size_dict(A__    ,       default_to_square=A__			)
						A__							:    int        =		resample if resample is not None else self.resample
						A__							:    Union[str, Any]        =		do_center_crop if do_center_crop is not None else self.do_center_crop
						A__							:    Optional[int]        =		crop_size if crop_size is not None else self.crop_size
						A__							:    List[Any]        =		get_size_dict(A__    ,       param_name="""crop_size"""			)
						A__							:    str        =		do_rescale if do_rescale is not None else self.do_rescale
						A__							:    int        =		rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:    Any        =		do_normalize if do_normalize is not None else self.do_normalize
						A__							:    Any        =		image_mean if image_mean is not None else self.image_mean
						A__							:    Union[str, Any]        =		image_std if image_std is not None else self.image_std

						A__							:    Tuple        =		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:
							raise ValueError("""Size 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.
						A__							:    Any        =		[to_numpy_array(A__			) for image in images]

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

						if do_center_crop:
							A__							:    Union[str, Any]        =		[self.center_crop(image=A__    ,       size=A__			) for image in images]

						if do_rescale:
							A__							:    Optional[int]        =		[self.rescale(image=A__    ,       scale=A__			) for image in images]

						if do_normalize:
							A__							:    Optional[int]        =		[self.normalize(image=A__    ,       mean=A__    ,       std=A__			) for image in images]

						A__							:    List[str]        =		[to_channel_dimension_format(A__    ,       A__			) for image in images]

						A__							:    Any        =		{"""pixel_values""": images}
						return BatchFeature(data=A__    ,       tensor_type=A__			)






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Optional[Any]        =		outputs.logits

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

							if is_torch_tensor(A__			):
								A__							:    Union[str, Any]        =		target_sizes.numpy()

							A__							:    List[str]        =		[]

							for idx in range(len(A__			)			):
								A__							:    List[Any]        =		torch.nn.functional.interpolate(
								    logits[idx].unsqueeze(dim=0			)    ,       size=target_sizes[idx]    ,       mode="""bilinear"""    ,       align_corners=A__			)
								A__							:    Any        =		resized_logits[0].argmax(dim=0			)
								semantic_segmentation.append(A__			)
						else:
							A__							:    Tuple        =		logits.argmax(dim=1			)
							A__							:    Optional[Any]        =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

						return semantic_segmentation






import string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



