import argparse
import struct
import unittest





class       lowercase__		:


						def __init__( self			:       List[str]					,	UpperCamelCase__			:       bytes		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							data

												# Initialize hash values
												SCREAMING_SNAKE_CASE							:					int           =							[
												    0X6a_09_e6_67,
												    0Xbb_67_ae_85,
												    0X3c_6e_f3_72,
												    0Xa5_4f_f5_3a,
												    0X51_0e_52_7f,
												    0X9b_05_68_8c,
												    0X1f_83_d9_ab,
												    0X5b_e0_cd_19,
												]

												# Initialize round constants
												SCREAMING_SNAKE_CASE							:					List[str]           =							[
												    0X42_8a_2f_98,
												    0X71_37_44_91,
												    0Xb5_c0_fb_cf,
												    0Xe9_b5_db_a5,
												    0X39_56_c2_5b,
												    0X59_f1_11_f1,
												    0X92_3f_82_a4,
												    0Xab_1c_5e_d5,
												    0Xd8_07_aa_98,
												    0X12_83_5b_01,
												    0X24_31_85_be,
												    0X55_0c_7d_c3,
												    0X72_be_5d_74,
												    0X80_de_b1_fe,
												    0X9b_dc_06_a7,
												    0Xc1_9b_f1_74,
												    0Xe4_9b_69_c1,
												    0Xef_be_47_86,
												    0X0f_c1_9d_c6,
												    0X24_0c_a1_cc,
												    0X2d_e9_2c_6f,
												    0X4a_74_84_aa,
												    0X5c_b0_a9_dc,
												    0X76_f9_88_da,
												    0X98_3e_51_52,
												    0Xa8_31_c6_6d,
												    0Xb0_03_27_c8,
												    0Xbf_59_7f_c7,
												    0Xc6_e0_0b_f3,
												    0Xd5_a7_91_47,
												    0X06_ca_63_51,
												    0X14_29_29_67,
												    0X27_b7_0a_85,
												    0X2e_1b_21_38,
												    0X4d_2c_6d_fc,
												    0X53_38_0d_13,
												    0X65_0a_73_54,
												    0X76_6a_0a_bb,
												    0X81_c2_c9_2e,
												    0X92_72_2c_85,
												    0Xa2_bf_e8_a1,
												    0Xa8_1a_66_4b,
												    0Xc2_4b_8b_70,
												    0Xc7_6c_51_a3,
												    0Xd1_92_e8_19,
												    0Xd6_99_06_24,
												    0Xf4_0e_35_85,
												    0X10_6a_a0_70,
												    0X19_a4_c1_16,
												    0X1e_37_6c_08,
												    0X27_48_77_4c,
												    0X34_b0_bc_b5,
												    0X39_1c_0c_b3,
												    0X4e_d8_aa_4a,
												    0X5b_9c_ca_4f,
												    0X68_2e_6f_f3,
												    0X74_8f_82_ee,
												    0X78_a5_63_6f,
												    0X84_c8_78_14,
												    0X8c_c7_02_08,
												    0X90_be_ff_fa,
												    0Xa4_50_6c_eb,
												    0Xbe_f9_a3_f7,
												    0Xc6_71_78_f2,
												]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.preprocessing(self.data		)
												self.final_hash()


						@staticmethod
						def  __A ( UpperCamelCase__			:       bytes		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							B'''\x80''' + (B'''\x00''' * (63 - (len(UpperCamelCase__		) + 8) % 64))
												SCREAMING_SNAKE_CASE							:					int           =							struct.pack('''>Q'''					,	(len(UpperCamelCase__		) * 8)		)
												return data + padding + big_endian_integer


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    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
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							list(struct.unpack('''>16L'''					,	UpperCamelCase__		)		)
																		# add 48 0-ed integers
																		words += [0] * 48

																		SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							self.hashes

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

																														SCREAMING_SNAKE_CASE							:					Any           =							(
																														    words[index - 16] + sa + words[index - 7] + sa
																														) % 0X1_00_00_00_00

																								# Compression
																								SCREAMING_SNAKE_CASE							:					Any           =							self.ror(UpperCamelCase__					,	6		) ^ self.ror(UpperCamelCase__					,	11		) ^ self.ror(UpperCamelCase__					,	25		)
																								SCREAMING_SNAKE_CASE							:					List[str]           =							(e & f) ^ ((~e & 0Xff_ff_ff_ff) & g)
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							(
																								    h + sa + ch + self.round_constants[index] + words[index]
																								) % 0X1_00_00_00_00
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							self.ror(UpperCamelCase__					,	2		) ^ self.ror(UpperCamelCase__					,	13		) ^ self.ror(UpperCamelCase__					,	22		)
																								SCREAMING_SNAKE_CASE							:					str           =							(a & b) ^ (a & c) ^ (b & c)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							(sa + maj) % 0X1_00_00_00_00

																								SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							(
																								    g,
																								    f,
																								    e,
																								    ((d + tempa) % 0X1_00_00_00_00),
																								    c,
																								    b,
																								    a,
																								    ((tempa + tempa) % 0X1_00_00_00_00),
																								)

																		SCREAMING_SNAKE_CASE							:					Any           =							[a, b, c, d, e, f, g, h]

																		# Modify final values
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[
																		    ((element + mutated_hash_values[index]) % 0X1_00_00_00_00)
																		    for index, element in enumerate(self.hashes		)
																		]

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							''''''.join([hex(UpperCamelCase__		)[2:].zfill(8		) for value in self.hashes]		)



						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												return 0Xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)






class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												import hashlib

												SCREAMING_SNAKE_CASE							:					Dict           =							bytes('''Test String'''					,	'''utf-8'''		)
												self.assertEqual(SHAaaa(UpperCamelCase__		).hash					,	hashlib.shaaaa(UpperCamelCase__		).hexdigest()		)






def    A			(					):
						import doctest

						doctest.testmod()

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							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'''		)

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							parser.parse_args()

						SCREAMING_SNAKE_CASE							:					Dict           =							args.input_string

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

						print(SHAaaa(_lowercase		).hash		)


if __name__ == "__main__":
					main()






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


__UpperCamelCase	: int    		=  logging.get_logger(__name__)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						def constraint_to_multiple_of(_lowercase	,						_lowercase	,						_lowercase=0	,						_lowercase=None		):
												SCREAMING_SNAKE_CASE							:					int           =							round(val / multiple		) * multiple

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

												if x < min_val:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							math.ceil(val / multiple		) * multiple

												return x

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(output_size, output_size) if isinstance(_lowercase	,						_lowercase		) else output_size

						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_image_size(_lowercase		)
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							output_size

						# determine new height and width
						SCREAMING_SNAKE_CASE							:					Dict           =							output_height / input_height
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width		) < abs(1 - scale_height		):
																		# fit width
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_width
												else:
																		# fit height
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_height

						SCREAMING_SNAKE_CASE							:					List[str]           =							constraint_to_multiple_of(scale_height * input_height	,						multiple=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							constraint_to_multiple_of(scale_width * input_width	,						multiple=_lowercase		)

						return (new_height, new_width)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""pixel_values"""]


						def __init__( self			:       int					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Dict[str, int] = None					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BILINEAR					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Union[int, float] = 1 / 255					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							size if size is not None else {'''height''': 384, '''width''': 384}
												SCREAMING_SNAKE_CASE							:					Any           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							do_resize
												SCREAMING_SNAKE_CASE							:					Any           =							size
												SCREAMING_SNAKE_CASE							:					str           =							keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					int           =							resample
												SCREAMING_SNAKE_CASE							:					Any           =							do_rescale
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rescale_factor
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							do_normalize
												SCREAMING_SNAKE_CASE							:					str           =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BICUBIC					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							get_size_dict(UpperCamelCase__		)
												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()}"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							get_resize_output_image_size(
												    UpperCamelCase__					,	output_size=(size['''height'''], size['''width'''])					,	keep_aspect_ratio=UpperCamelCase__					,	multiple=UpperCamelCase__					,	)
												return resize(UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[int, float]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												return rescale(UpperCamelCase__					,	scale=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												return normalize(UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       ImageInput					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       PILImageResampling = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       float = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       ChannelDimension = ChannelDimension.FIRST					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					Tuple           =							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:					str           =							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:					Dict           =							make_list_of_images(UpperCamelCase__		)

												if not valid_images(UpperCamelCase__		):
																		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.
												SCREAMING_SNAKE_CASE							:					Tuple           =							[to_numpy_array(UpperCamelCase__		) for image in images]

												if do_resize:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[self.resize(image=UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__		) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.rescale(image=UpperCamelCase__					,	scale=UpperCamelCase__		) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.normalize(image=UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[to_channel_dimension_format(UpperCamelCase__					,	UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Tuple           =							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Tuple] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							outputs.logits

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

																		if is_torch_tensor(UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							target_sizes.numpy()

																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]

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

												return semantic_segmentation





