'''simple docstring'''

A_	   = {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str)    ->		list[tuple[str, str]]:



						"""simple docstring"""

						return [alphabet[char] for char in key.upper()]


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			str)    ->		str:



						"""simple docstring"""

						snake_case :       Any				=    ""
						snake_case :       str				=    0
						snake_case :       Union[str, Any]				=    generate_table(_lowerCAmelCase)
						for char in words.upper():
												cipher += get_opponent(table[count],      _lowerCAmelCase)
												snake_case :       Optional[int]				=    (count + 1) % len(_lowerCAmelCase)
						return cipher


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			str)    ->		str:



						"""simple docstring"""

						return encrypt(_lowerCAmelCase,      _lowerCAmelCase)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			tuple[str, str],      _lowerCAmelCase    :			str)    ->		tuple[int, int]:



						"""simple docstring"""

						snake_case :       Union[str, Any]				=    0 if char in table[0] else 1
						snake_case :       List[Any]				=    table[row].index(_lowerCAmelCase)
						return row, col


def        UpperCAmelCase__   (				_lowerCAmelCase    :			tuple[str, str],      _lowerCAmelCase    :			str)    ->		str:



						"""simple docstring"""

						snake_case				,					snake_case :       str				=    get_position(_lowerCAmelCase,      char.upper())
						if row == 1:
												return table[0][col]
						else:
												return table[1][col] if row == 0 else char


if __name__ == "__main__":
						import doctest

						doctest.testmod()  # Fist ensure that all our tests are passing...
						A_	   = input("Enter key: ").strip()
						A_	   = input("Enter text to encrypt: ").strip()
						A_	   = encrypt(key, text)

						print(f"Encrypted: {cipher_text}")
						print(f"Decrypted with key: {decrypt(key, cipher_text)}")






'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





