


import argparse
import struct
import unittest






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->None:




         '''simple docstring'''





         A_      :				Optional[int]		     =					data

         # Initialize hash values
         A_      :				Union[str, Any]		     =					[
             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
         A_      :				Optional[Any]		     =					[
             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,
         ]

         A_      :				str		     =					self.preprocessing(self.data		)
         self.final_hash()



       @staticmethod
       def        _snake_case       (      _SCREAMING_SNAKE_CASE		)->bytes:




         '''simple docstring'''





         A_      :				List[Any]		     =					B'''\x80''' + (B'''\x00''' * (63 - (len(_SCREAMING_SNAKE_CASE		) + 8) % 64))
         A_      :				List[str]		     =					struct.pack('''>Q'''				,       (len(_SCREAMING_SNAKE_CASE		) * 8)		)
         return data + padding + big_endian_integer



       def        _snake_case       (      self		)->None:




         '''simple docstring'''





         A_      :				List[str]		     =					[
             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
           A_      :				int		     =					list(struct.unpack('''>16L'''				,       _SCREAMING_SNAKE_CASE		)		)
           # add 48 0-ed integers
           words += [0] * 48

           A_     ,       A_     ,       A_     ,       A_     ,       A_     ,       A_     ,       A_     ,       A_      :				Dict		     =					self.hashes

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

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

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

             A_     ,       A_     ,       A_     ,       A_     ,       A_     ,       A_     ,       A_     ,       A_      :				List[Any]		     =					(
                 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),
             )

           A_      :				str		     =					[a, b, c, d, e, f, g, h]

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

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







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





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






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       def        _snake_case       (      self		)->None:




         '''simple docstring'''





         import hashlib

         A_      :				List[str]		     =					bytes('''Test String'''				,       '''utf-8'''		)
         self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE		).hash				,       hashlib.shaaaa(_SCREAMING_SNAKE_CASE		).hexdigest()		)




def   _SCREAMING_SNAKE_CASE	( ):
  import doctest

  doctest.testmod()

  A_      :				Optional[Any]		     =					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'''       )

  A_      :				List[Any]		     =					parser.parse_args()

  A_      :				List[Any]		     =					args.input_string

  # hash input should be a bytestring
  if args.input_file:
    with open(args.input_file ,			'''rb'''       ) as f:
      A_      :				Tuple		     =					f.read()
  else:
    A_      :				List[str]		     =					bytes(SCREAMING_SNAKE_CASE ,			'''utf-8'''       )

  print(SHAaaa(SCREAMING_SNAKE_CASE       ).hash       )


if __name__ == "__main__":
     main()








import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[str]		     =					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''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



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




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



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




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



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




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

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







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

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




