


"""simple docstring"""



import argparse
import struct
import unittest







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		)			->	Any:

       '''simple docstring'''




       snake_case					:   Dict         =					data

       # Initialize hash values
       snake_case					:   int         =					[
           0x6_a_0_9_e_6_6_7,
           0xb_b_6_7_a_e_8_5,
           0x3_c_6_e_f_3_7_2,
           0xa_5_4_f_f_5_3_a,
           0x5_1_0_e_5_2_7_f,
           0x9_b_0_5_6_8_8_c,
           0x1_f_8_3_d_9_a_b,
           0x5_b_e_0_c_d_1_9,
       ]

       # Initialize round constants
       snake_case					:   List[str]         =					[
           0x4_2_8_a_2_f_9_8,
           0x7_1_3_7_4_4_9_1,
           0xb_5_c_0_f_b_c_f,
           0xe_9_b_5_d_b_a_5,
           0x3_9_5_6_c_2_5_b,
           0x5_9_f_1_1_1_f_1,
           0x9_2_3_f_8_2_a_4,
           0xa_b_1_c_5_e_d_5,
           0xd_8_0_7_a_a_9_8,
           0x1_2_8_3_5_b_0_1,
           0x2_4_3_1_8_5_b_e,
           0x5_5_0_c_7_d_c_3,
           0x7_2_b_e_5_d_7_4,
           0x8_0_d_e_b_1_f_e,
           0x9_b_d_c_0_6_a_7,
           0xc_1_9_b_f_1_7_4,
           0xe_4_9_b_6_9_c_1,
           0xe_f_b_e_4_7_8_6,
           0x0_f_c_1_9_d_c_6,
           0x2_4_0_c_a_1_c_c,
           0x2_d_e_9_2_c_6_f,
           0x4_a_7_4_8_4_a_a,
           0x5_c_b_0_a_9_d_c,
           0x7_6_f_9_8_8_d_a,
           0x9_8_3_e_5_1_5_2,
           0xa_8_3_1_c_6_6_d,
           0xb_0_0_3_2_7_c_8,
           0xb_f_5_9_7_f_c_7,
           0xc_6_e_0_0_b_f_3,
           0xd_5_a_7_9_1_4_7,
           0x0_6_c_a_6_3_5_1,
           0x1_4_2_9_2_9_6_7,
           0x2_7_b_7_0_a_8_5,
           0x2_e_1_b_2_1_3_8,
           0x4_d_2_c_6_d_f_c,
           0x5_3_3_8_0_d_1_3,
           0x6_5_0_a_7_3_5_4,
           0x7_6_6_a_0_a_b_b,
           0x8_1_c_2_c_9_2_e,
           0x9_2_7_2_2_c_8_5,
           0xa_2_b_f_e_8_a_1,
           0xa_8_1_a_6_6_4_b,
           0xc_2_4_b_8_b_7_0,
           0xc_7_6_c_5_1_a_3,
           0xd_1_9_2_e_8_1_9,
           0xd_6_9_9_0_6_2_4,
           0xf_4_0_e_3_5_8_5,
           0x1_0_6_a_a_0_7_0,
           0x1_9_a_4_c_1_1_6,
           0x1_e_3_7_6_c_0_8,
           0x2_7_4_8_7_7_4_c,
           0x3_4_b_0_b_c_b_5,
           0x3_9_1_c_0_c_b_3,
           0x4_e_d_8_a_a_4_a,
           0x5_b_9_c_c_a_4_f,
           0x6_8_2_e_6_f_f_3,
           0x7_4_8_f_8_2_e_e,
           0x7_8_a_5_6_3_6_f,
           0x8_4_c_8_7_8_1_4,
           0x8_c_c_7_0_2_0_8,
           0x9_0_b_e_f_f_f_a,
           0xa_4_5_0_6_c_e_b,
           0xb_e_f_9_a_3_f_7,
           0xc_6_7_1_7_8_f_2,
       ]

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







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		)			->	Union[str, Any]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




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

           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
                   snake_case					:   Optional[int]         =					(
                       self.ror(words[index - 15]		,			7		)
                       ^ self.ror(words[index - 15]		,			18		)
                       ^ (words[index - 15] >> 3)
                   )
                   snake_case					:   int         =					(
                       self.ror(words[index - 2]		,			17		)
                       ^ self.ror(words[index - 2]		,			19		)
                       ^ (words[index - 2] >> 10)
                   )

                   snake_case					:   Any         =					(
                       words[index - 16] + sa + words[index - 7] + sa
                   ) % 0x1_0_0_0_0_0_0_0_0

               # Compression
               snake_case					:   Any         =					self.ror(UpperCamelCase__		,			6		) ^ self.ror(UpperCamelCase__		,			11		) ^ self.ror(UpperCamelCase__		,			25		)
               snake_case					:   List[str]         =					(e & f) ^ ((~e & 0xf_f_f_f_f_f_f_f) & g)
               snake_case					:   Optional[int]         =					(
                   h + sa + ch + self.round_constants[index] + words[index]
               ) % 0x1_0_0_0_0_0_0_0_0
               snake_case					:   List[Any]         =					self.ror(UpperCamelCase__		,			2		) ^ self.ror(UpperCamelCase__		,			13		) ^ self.ror(UpperCamelCase__		,			22		)
               snake_case					:   str         =					(a & b) ^ (a & c) ^ (b & c)
               snake_case					:   Tuple         =					(sa + maj) % 0x1_0_0_0_0_0_0_0_0

               snake_case					:   Tuple         =					(
                   g,
                   f,
                   e,
                   ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
                   c,
                   b,
                   a,
                   ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
               )

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

           # Modify final values
           snake_case					:   Tuple         =					[
               ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
               for index, element in enumerate(self.hashes		)
           ]

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




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







class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       import hashlib

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







def 						__lowerCAmelCase		(		)			->		List[str]:


    """simple docstring"""





    import doctest

    doctest.testmod()

    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"				)

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

    snake_case					:   Dict         =					args.input_string

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

    print(SHAaaa(_lowercase				).hash				)


if __name__ == "__main__":
   main()









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					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 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

       snake_case					:   Optional[Any]         =					image[0, -3:, -3:, -1]
       snake_case					:   List[str]         =					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 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 _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

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





