
"""simple docstring"""




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



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   data
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]



				@staticmethod
				def 	__magic_name__    (SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.padding()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.split_blocks()
											for block in self.blocks:
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.expand_block(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.h
																		for i in range(0   ,       80    ):
																									if 0 <= i < 20:
																																SCREAMING_SNAKE_CASE__						:       List[str]           =   (b & c) | ((~b) & d)
																																SCREAMING_SNAKE_CASE__						:       Dict           =   0X5_a_8_2_7_9_9_9
																									elif 20 <= i < 40:
																																SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   b ^ c ^ d
																																SCREAMING_SNAKE_CASE__						:       str           =   0X6_e_d_9_e_b_a_1
																									elif 40 <= i < 60:
																																SCREAMING_SNAKE_CASE__						:       Any           =   (b & c) | (b & d) | (c & d)
																																SCREAMING_SNAKE_CASE__						:       List[Any]           =   0X8_f_1_b_b_c_d_c
																									elif 60 <= i < 80:
																																SCREAMING_SNAKE_CASE__						:       Optional[int]           =   b ^ c ^ d
																																SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   0Xc_a_6_2_c_1_d_6
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (
																									    self.rotate(SCREAMING_SNAKE_CASE__   ,       5    ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
																									    a,
																									    self.rotate(SCREAMING_SNAKE_CASE__   ,       30    ),
																									    c,
																									    d,
																									)
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   (
																		    self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
																		    self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
																		    self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
																		    self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
																		    self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
																		)
											return ("{:08x}" * 5).format(*self.h    )






def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Any           =   b"""Test String"""
							assert SHAaHash(_snake_case						).final_hash() == hashlib.shaa(_snake_case						).hexdigest()  # noqa: S324

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Union[str, 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"""						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   parser.parse_args()
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   args.input_string
							# In any case hash input should be a bytestring
							if args.input_file:
														with open(args.input_file   ,"""rb"""						) as f:
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   f.read()
							else:
														SCREAMING_SNAKE_CASE__						:       Dict           =   bytes(_snake_case   ,"""utf-8"""						)
							print(SHAaHash(_snake_case						).final_hash()						)


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

					doctest.testmod()










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  A		(  unittest.TestCase     ):



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  UNetaDModel(
								    block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=3,	out_channels=3,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),	)
								return model







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_uncond_unet
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy''',	return_dict=UpperCamelCase__   )[0]

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								lowerCAmelCase_      			=  image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase_      			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''google/ncsnpp-celebahq-256'''
								lowerCAmelCase_      			=  UNetaDModel.from_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=20,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase_      			=  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




