

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 					lowercase							:




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




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

																		if img.dtype == np.uinta:
																									snake_case_     							=       Image.fromarray(snake_case			)
																									snake_case_     							=       pil_image.resize((neww, newh) ,  PILImageResampling.BILINEAR			)
																									snake_case_     							=       np.asarray(snake_case			)
																		else:
																									snake_case_     							=       img.permute(2 ,  0 ,  1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
																									snake_case_     							=       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 					lowercase							:




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




				def        a				(   self ,  snake_case			):
											snake_case_     							=       tuple(max(snake_case			) for s in zip(*[img.shape for img in images]			)			)
											snake_case_     							=       [im.shape[-2:] for im in images]
											snake_case_     							=       [
											    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_     							=       [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_     							=       torch.tensor([im.shape[:2] for im in images]			)
																		snake_case_     							=       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_     							=       [self.normalizer(snake_case			) for x in images]
																		# now pad them to do the following operations
																		snake_case_							,       snake_case_     							=       self.pad(snake_case			)
																		# Normalize

																		if self.size_divisibility > 0:
																									raise NotImplementedError()
																		# pad
																		snake_case_     							=       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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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







import argparse
import struct
import unittest







class 					lowercase							:




				def __init__(   self ,  snake_case			):
											snake_case_     							=       data

											# Initialize hash values
											snake_case_     							=       [
											    0x6A09E667,
											    0xBB67AE85,
											    0x3C6EF372,
											    0xA54FF53A,
											    0x510E527F,
											    0x9B05688C,
											    0x1F83D9AB,
											    0x5BE0CD19,
											]

											# Initialize round constants
											snake_case_     							=       [
											    0x428A2F98,
											    0x71374491,
											    0xB5C0FBCF,
											    0xE9B5DBA5,
											    0x3956C25B,
											    0x59F111F1,
											    0x923F82A4,
											    0xAB1C5ED5,
											    0xD807AA98,
											    0x12835B01,
											    0x243185BE,
											    0x550C7DC3,
											    0x72BE5D74,
											    0x80DEB1FE,
											    0x9BDC06A7,
											    0xC19BF174,
											    0xE49B69C1,
											    0xEFBE4786,
											    0x0FC19DC6,
											    0x240CA1CC,
											    0x2DE92C6F,
											    0x4A7484AA,
											    0x5CB0A9DC,
											    0x76F988DA,
											    0x983E5152,
											    0xA831C66D,
											    0xB00327C8,
											    0xBF597FC7,
											    0xC6E00BF3,
											    0xD5A79147,
											    0x06CA6351,
											    0x14292967,
											    0x27B70A85,
											    0x2E1B2138,
											    0x4D2C6DFC,
											    0x53380D13,
											    0x650A7354,
											    0x766A0ABB,
											    0x81C2C92E,
											    0x92722C85,
											    0xA2BFE8A1,
											    0xA81A664B,
											    0xC24B8B70,
											    0xC76C51A3,
											    0xD192E819,
											    0xD6990624,
											    0xF40E3585,
											    0x106AA070,
											    0x19A4C116,
											    0x1E376C08,
											    0x2748774C,
											    0x34B0BCB5,
											    0x391C0CB3,
											    0x4ED8AA4A,
											    0x5B9CCA4F,
											    0x682E6FF3,
											    0x748F82EE,
											    0x78A5636F,
											    0x84C87814,
											    0x8CC70208,
											    0x90BEFFFA,
											    0xA4506CEB,
											    0xBEF9A3F7,
											    0xC67178F2,
											]

											snake_case_     							=       self.preprocessing(self.data			)
											self.final_hash()




				@staticmethod
				def        a				(   snake_case			):
											snake_case_     							=       b'\x80' + (b'\x00' * (63 - (len(snake_case			) + 8) % 64))
											snake_case_     							=       struct.pack('>Q' ,  (len(snake_case			) * 8)			)
											return data + padding + big_endian_integer




				def        a				(   self			):
											# Convert into blocks of 64 bytes
											snake_case_     							=       [
											    self.preprocessed_data[x : x + 64]
											    for x in range(0 ,  len(self.preprocessed_data			) ,  64			)
											]

											for block in self.blocks:
																		# Convert the given block into a list of 4 byte integers
																		snake_case_     							=       list(struct.unpack('>16L' ,  snake_case			)			)
																		# add 48 0-ed integers
																		words += [0] * 48

																		snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       self.hashes

																		for index in range(0 ,  64			):
																									if index > 15:
																																# modify the zero-ed indexes at the end of the array
																																snake_case_     							=       (
																																    self.ror(words[index - 15] ,  7			)
																																    ^ self.ror(words[index - 15] ,  18			)
																																    ^ (words[index - 15] >> 3)
																																)
																																snake_case_     							=       (
																																    self.ror(words[index - 2] ,  17			)
																																    ^ self.ror(words[index - 2] ,  19			)
																																    ^ (words[index - 2] >> 10)
																																)

																																snake_case_     							=       (
																																    words[index - 16] + sa + words[index - 7] + sa
																																) % 0x100000000

																									# Compression
																									snake_case_     							=       self.ror(snake_case ,  6			) ^ self.ror(snake_case ,  11			) ^ self.ror(snake_case ,  25			)
																									snake_case_     							=       (e & f) ^ ((~e & 0xFFFFFFFF) & g)
																									snake_case_     							=       (
																									    h + sa + ch + self.round_constants[index] + words[index]
																									) % 0x100000000
																									snake_case_     							=       self.ror(snake_case ,  2			) ^ self.ror(snake_case ,  13			) ^ self.ror(snake_case ,  22			)
																									snake_case_     							=       (a & b) ^ (a & c) ^ (b & c)
																									snake_case_     							=       (sa + maj) % 0x100000000

																									snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       (
																									    g,
																									    f,
																									    e,
																									    ((d + tempa) % 0x100000000),
																									    c,
																									    b,
																									    a,
																									    ((tempa + tempa) % 0x100000000),
																									)

																		snake_case_     							=       [a, b, c, d, e, f, g, h]

																		# Modify final values
																		snake_case_     							=       [
																		    ((element + mutated_hash_values[index]) % 0x100000000)
																		    for index, element in enumerate(self.hashes			)
																		]

											snake_case_     							=       ''.join([hex(snake_case			)[2:].zfill(8			) for value in self.hashes]			)




				def        a				(   self ,  snake_case ,  snake_case			):
											return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											import hashlib

											snake_case_     							=       bytes('Test String' ,  'utf-8'			)
											self.assertEqual(SHAaaa(snake_case			).hash ,  hashlib.shaaaa(snake_case			).hexdigest()			)





def    __lowerCamelCase      (					):







							'''simple docstring'''






							import doctest

							doctest.testmod()

							snake_case_     							=       argparse.ArgumentParser()
							parser.add_argument(
							    '-s'  ,				'--string'  ,				dest='input_string'  ,				default='Hello World!! Welcome to Cryptography'  ,				help='Hash the string'  ,				)
							parser.add_argument(
							    '-f'  ,				'--file'  ,				dest='input_file'  ,				help='Hash contents of a file' )

							snake_case_     							=       parser.parse_args()

							snake_case_     							=       args.input_string

							# hash input should be a bytestring
							if args.input_file:
														with open(args.input_file  ,				'rb' ) as f:
																					snake_case_     							=       f.read()
							else:
														snake_case_     							=       bytes(UpperCamelCase__  ,				'utf-8' )

							print(SHAaaa(UpperCamelCase__ ).hash )


if __name__ == "__main__":
					main()




