
'''simple docstring'''







from collections.abc import Generator
from math import sin







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bytes:
	if len(__A			) != 32:
		raise ValueError('Input must be of length 32'			)

	_snake_case     		=	b''
	for i in [3, 2, 1, 0]:
		little_endian += string_aa[8 * i : 8 * i + 8]
	return little_endian







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bytes:
	if i < 0:
		raise ValueError('Input must be non-negative'			)

	_snake_case     		=	format(__A    ,       '08x'			)[-8:]
	_snake_case     		=	b''
	for i in [3, 2, 1, 0]:
		little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'			)
	return little_endian_hex







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bytes:
	_snake_case     		=	b''
	for char in message:
		bit_string += format(__A    ,       '08b'			).encode('utf-8'			)
	_snake_case     		=	format(len(__A			)    ,       '064b'			).encode('utf-8'			)

	# Pad bit_string to a multiple of 512 chars
	bit_string += b"1"
	while len(__A			) % 512 != 448:
		bit_string += b"0"
	bit_string += to_little_endian(start_len[32:]			) + to_little_endian(start_len[:32]			)

	return bit_string







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Generator[list[int], None, None]:
	if len(__A			) % 512 != 0:
		raise ValueError('Input must have length that\'s a multiple of 512'			)

	for pos in range(0    ,       len(__A			)    ,       512			):
		_snake_case     		=	bit_string[pos : pos + 512]
		_snake_case     		=	[]
		for i in range(0    ,       512    ,       32			):
			block_words.append(int(to_little_endian(block[i : i + 32]			)    ,       2			)			)
		yield block_words







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if i < 0:
		raise ValueError('Input must be non-negative'			)

	_snake_case     		=	format(__A    ,       '032b'			)
	_snake_case     		=	''
	for c in i_str:
		new_str += "1" if c == "0" else "0"
	return int(__A    ,       2			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	return (a + b) % 2**32







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	if i < 0:
		raise ValueError('Input must be non-negative'			)
	if shift < 0:
		raise ValueError('Shift must be non-negative'			)
	return ((i << shift) ^ (i >> (32 - shift))) % 2**32







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bytes:
	_snake_case     		=	preprocess(__A			)

	_snake_case     		=	[int(2**32 * abs(sin(i + 1			)			)			) for i in range(64			)]

	# Starting states
	_snake_case     		=	0x67452301
	_snake_case     		=	0xEFCDAB89
	_snake_case     		=	0x98BADCFE
	_snake_case     		=	0x10325476

	_snake_case     		=	[
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	]

	# Process bit string in chunks, each with 16 32-char words
	for block_words in get_block_words(__A			):
		_snake_case     		=	aa
		_snake_case     		=	ba
		_snake_case     		=	ca
		_snake_case     		=	da

		# Hash current chunk
		for i in range(64			):
			if i <= 15:
				# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
				_snake_case     		=	d ^ (b & (c ^ d))
				_snake_case     		=	i
			elif i <= 31:
				# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
				_snake_case     		=	c ^ (d & (b ^ c))
				_snake_case     		=	(5 * i + 1) % 16
			elif i <= 47:
				_snake_case     		=	b ^ c ^ d
				_snake_case     		=	(3 * i + 5) % 16
			else:
				_snake_case     		=	c ^ (b | not_aa(__A			))
				_snake_case     		=	(7 * i) % 16
			_snake_case     		=	(f + a + added_consts[i] + block_words[g]) % 2**32
			_snake_case     		=	d
			_snake_case     		=	c
			_snake_case     		=	b
			_snake_case     		=	sum_aa(__A    ,       left_rotate_aa(__A    ,       shift_amounts[i]			)			)

		# Add hashed chunk to running total
		_snake_case     		=	sum_aa(__A    ,       __A			)
		_snake_case     		=	sum_aa(__A    ,       __A			)
		_snake_case     		=	sum_aa(__A    ,       __A			)
		_snake_case     		=	sum_aa(__A    ,       __A			)

	_snake_case     		=	reformat_hex(__A			) + reformat_hex(__A			) + reformat_hex(__A			) + reformat_hex(__A			)
	return digest


if __name__ == "__main__":
					import doctest

					doctest.testmod()



'''simple docstring'''







import unittest

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 GLPNImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size_divisor
								_snake_case     		=	do_rescale




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= GLPNImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	GLPNImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_resize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'size_divisor'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'resample'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_rescale'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     Image.Image						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								_snake_case     		=	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 (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	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 (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

