



import argparse
import hashlib  # hashlib is only used inside the Test class
import struct



class     UpperCAmelCase_       :







							def __init__(  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       data
														_lowerCAmelCase	:				str					  =       [0X67_45_23_01, 0XEF_CD_AB_89, 0X98_BA_DC_FE, 0X10_32_54_76, 0XC3_D2_E1_F0]







							@staticmethod
							def   snake_case__ (  __a,     __a):

														'''simple docstring'''

														return ((n << b) | (n >> (32 - b))) & 0XFF_FF_FF_FF







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       B"\x80" + B"\x00" * (63 - (len(self.data) + 8) % 64)
														_lowerCAmelCase	:				Tuple					  =       self.data + padding + struct.pack(">Q",     8 * len(self.data))
														return padded_data







							def   snake_case__ (  self):

														'''simple docstring'''

														return [
														    self.padded_data[i : i + 64] for i in range(0,     len(self.padded_data),     64)
														]







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       list(struct.unpack(">16L",     lowerCAmelCase_)) + [0] * 64
														for i in range(16,     80):
																					_lowerCAmelCase	:				Any					  =       self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),     1)
														return w







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[int]					  =       self.padding()
														_lowerCAmelCase	:				List[str]					  =       self.split_blocks()
														for block in self.blocks:
																					_lowerCAmelCase	:				str					  =       self.expand_block(lowerCAmelCase_)
																					_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase	:				List[Any]					  =       self.h
																					for i in range(0,     80):
																												if 0 <= i < 20:
																																			_lowerCAmelCase	:				Tuple					  =       (b & c) | ((~b) & d)
																																			_lowerCAmelCase	:				Optional[int]					  =       0X5A_82_79_99
																												elif 20 <= i < 40:
																																			_lowerCAmelCase	:				int					  =       b ^ c ^ d
																																			_lowerCAmelCase	:				List[Any]					  =       0X6E_D9_EB_A1
																												elif 40 <= i < 60:
																																			_lowerCAmelCase	:				int					  =       (b & c) | (b & d) | (c & d)
																																			_lowerCAmelCase	:				Optional[int]					  =       0X8F_1B_BC_DC
																												elif 60 <= i < 80:
																																			_lowerCAmelCase	:				Union[str, Any]					  =       b ^ c ^ d
																																			_lowerCAmelCase	:				int					  =       0XCA_62_C1_D6
																												_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase	:				List[str]					  =       (
																												    self.rotate(lowerCAmelCase_,     5) + f + e + k + expanded_block[i] & 0XFF_FF_FF_FF,
																												    a,
																												    self.rotate(lowerCAmelCase_,     30),
																												    c,
																												    d,
																												)
																					_lowerCAmelCase	:				str					  =       (
																					    self.h[0] + a & 0XFF_FF_FF_FF,
																					    self.h[1] + b & 0XFF_FF_FF_FF,
																					    self.h[2] + c & 0XFF_FF_FF_FF,
																					    self.h[3] + d & 0XFF_FF_FF_FF,
																					    self.h[4] + e & 0XFF_FF_FF_FF,
																					)
														return ("{:08x}" * 5).format(*self.h)






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				str					  =       b"Test String"
							assert SHAaHash(_SCREAMING_SNAKE_CASE    ).final_hash() == hashlib.shaa(_SCREAMING_SNAKE_CASE    ).hexdigest()  # noqa: S324






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[Any]					  =       argparse.ArgumentParser(description="Process some strings or files"    )
							parser.add_argument(
							    "--string"							,					dest="input_string"							,					default="Hello World!! Welcome to Cryptography"							,					help="Hash the string"							,					)
							parser.add_argument("--file"							,					dest="input_file"							,					help="Hash contents of a file"    )
							_lowerCAmelCase	:				Dict					  =       parser.parse_args()
							_lowerCAmelCase	:				Tuple					  =       args.input_string
							# In any case hash input should be a bytestring
							if args.input_file:
														with open(args.input_file							,					"rb"    ) as f:
																					_lowerCAmelCase	:				Optional[int]					  =       f.read()
							else:
														_lowerCAmelCase	:				Any					  =       bytes(_SCREAMING_SNAKE_CASE							,					"utf-8"    )
							print(SHAaHash(_SCREAMING_SNAKE_CASE    ).final_hash()    )


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod()




'''simple docstring'''





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

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BridgeTowerImageProcessor
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			int = 32			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			str=7			,    lowerCAmelCase_							:			Union[str, Any]=30			,    lowerCAmelCase_							:			str=400			,    lowerCAmelCase_							:			int=3			,    )      ->		str:
						__A=	parent
						__A=	do_resize
						__A=	size if size is not None else {'shortest_edge': 288}
						__A=	size_divisor
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	do_center_crop
						__A=	image_mean
						__A=	image_std
						__A=	do_pad
						__A=	batch_size
						__A=	num_channels
						__A=	min_resolution
						__A=	max_resolution
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str=False	)      ->		str:
						if not batched:
									__A=	self.size['shortest_edge']
									__A=	image_inputs[0]
									if isinstance(lowerCAmelCase_			,    Image.Image	):
												__A,	__A=	image.size
									else:
												__A,	__A=	image.shape[1], image.shape[2]
									__A=	size / min(lowerCAmelCase_			,    lowerCAmelCase_	)
									if h < w:
												__A,	__A=	size, scale * w
									else:
												__A,	__A=	scale * h, size

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

									__A,	__A=	int(newh + 0.5	), int(neww + 0.5	)
									__A,	__A=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


@require_torch
@require_vision
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )



