

import argparse
import struct
import unittest






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Union[str, Any]			,     __lowerCamelCase		:	bytes      )	->							None:
       SCREAMING_SNAKE_CASE__					=					data

       # Initialize hash values
       SCREAMING_SNAKE_CASE__					=					[
           0x6a09_e667,
           0xbb67_ae85,
           0x3c6e_f372,
           0xa54f_f53a,
           0x510e_527f,
           0x9b05_688c,
           0x1f83_d9ab,
           0x5be0_cd19,
       ]

       # Initialize round constants
       SCREAMING_SNAKE_CASE__					=					[
           0x428a_2f98,
           0x7137_4491,
           0xb5c0_fbcf,
           0xe9b5_dba5,
           0x3956_c25b,
           0x59f1_11f1,
           0x923f_82a4,
           0xab1c_5ed5,
           0xd807_aa98,
           0x1283_5b01,
           0x2431_85be,
           0x550c_7dc3,
           0x72be_5d74,
           0x80de_b1fe,
           0x9bdc_06a7,
           0xc19b_f174,
           0xe49b_69c1,
           0xefbe_4786,
           0x0fc1_9dc6,
           0x240c_a1cc,
           0x2de9_2c6f,
           0x4a74_84aa,
           0x5cb0_a9dc,
           0x76f9_88da,
           0x983e_5152,
           0xa831_c66d,
           0xb003_27c8,
           0xbf59_7fc7,
           0xc6e0_0bf3,
           0xd5a7_9147,
           0x06ca_6351,
           0x1429_2967,
           0x27b7_0a85,
           0x2e1b_2138,
           0x4d2c_6dfc,
           0x5338_0d13,
           0x650a_7354,
           0x766a_0abb,
           0x81c2_c92e,
           0x9272_2c85,
           0xa2bf_e8a1,
           0xa81a_664b,
           0xc24b_8b70,
           0xc76c_51a3,
           0xd192_e819,
           0xd699_0624,
           0xf40e_3585,
           0x106a_a070,
           0x19a4_c116,
           0x1e37_6c08,
           0x2748_774c,
           0x34b0_bcb5,
           0x391c_0cb3,
           0x4ed8_aa4a,
           0x5b9c_ca4f,
           0x682e_6ff3,
           0x748f_82ee,
           0x78a5_636f,
           0x84c8_7814,
           0x8cc7_0208,
           0x90be_fffa,
           0xa450_6ceb,
           0xbef9_a3f7,
           0xc671_78f2,
       ]

       SCREAMING_SNAKE_CASE__					=					self.preprocessing(self.data      )
       self.final_hash()






  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	bytes      )	->							bytes:
       SCREAMING_SNAKE_CASE__					=					B'''\x80''' + (B'''\x00''' * (63 - (len(__lowerCamelCase      ) + 8) % 64))
       SCREAMING_SNAKE_CASE__					=					struct.pack('''>Q'''			,     (len(__lowerCamelCase      ) * 8)      )
       return data + padding + big_endian_integer






  def    lowercase_	(							self		:	int      )	->							None:
       # Convert into blocks of 64 bytes
       SCREAMING_SNAKE_CASE__					=					[
           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__					=					list(struct.unpack('''>16L'''			,     __lowerCamelCase      )      )
            # 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__					=					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__					=					(
                          self.ror(words[index - 15]			,     7      )
                          ^ self.ror(words[index - 15]			,     18      )
                          ^ (words[index - 15] >> 3)
                      )
                      SCREAMING_SNAKE_CASE__					=					(
                          self.ror(words[index - 2]			,     17      )
                          ^ self.ror(words[index - 2]			,     19      )
                          ^ (words[index - 2] >> 10)
                      )

                      SCREAMING_SNAKE_CASE__					=					(
                          words[index - 16] + sa + words[index - 7] + sa
                      ) % 0x1_0000_0000

                 # Compression
                 SCREAMING_SNAKE_CASE__					=					self.ror(__lowerCamelCase			,     6      ) ^ self.ror(__lowerCamelCase			,     11      ) ^ self.ror(__lowerCamelCase			,     25      )
                 SCREAMING_SNAKE_CASE__					=					(e & f) ^ ((~e & 0xffff_ffff) & g)
                 SCREAMING_SNAKE_CASE__					=					(
                     h + sa + ch + self.round_constants[index] + words[index]
                 ) % 0x1_0000_0000
                 SCREAMING_SNAKE_CASE__					=					self.ror(__lowerCamelCase			,     2      ) ^ self.ror(__lowerCamelCase			,     13      ) ^ self.ror(__lowerCamelCase			,     22      )
                 SCREAMING_SNAKE_CASE__					=					(a & b) ^ (a & c) ^ (b & c)
                 SCREAMING_SNAKE_CASE__					=					(sa + maj) % 0x1_0000_0000

                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
                     g,
                     f,
                     e,
                     ((d + tempa) % 0x1_0000_0000),
                     c,
                     b,
                     a,
                     ((tempa + tempa) % 0x1_0000_0000),
                 )

            SCREAMING_SNAKE_CASE__					=					[a, b, c, d, e, f, g, h]

            # Modify final values
            SCREAMING_SNAKE_CASE__					=					[
                ((element + mutated_hash_values[index]) % 0x1_0000_0000)
                for index, element in enumerate(self.hashes      )
            ]

       SCREAMING_SNAKE_CASE__					=					''''''.join([hex(__lowerCamelCase      )[2:].zfill(8      ) for value in self.hashes]      )







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int      )	->							int:
       return 0xffff_ffff & (value << (32 - rotations)) | (value >> rotations)






class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def    lowercase_	(							self		:	Optional[int]      )	->							None:
       import hashlib

       SCREAMING_SNAKE_CASE__					=					bytes('''Test String'''			,     '''utf-8'''      )
       self.assertEqual(SHAaaa(__lowerCamelCase      ).hash			,     hashlib.shaaaa(__lowerCamelCase      ).hexdigest()      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE__					=					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__					=					parser.parse_args()

     SCREAMING_SNAKE_CASE__					=					args.input_string

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

     print(SHAaaa(_A       ).hash       )


if __name__ == "__main__":
       main()





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class  UpperCAmelCase__	( A__		,  A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      AltDiffusionPipeline
  a     =      TEXT_TO_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_BATCH_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS






  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(
           block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,     cross_attention_dim=32			,     )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     clip_sample=__lowerCamelCase			,     set_alpha_to_one=__lowerCamelCase			,     )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					AutoencoderKL(
           block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,     latent_channels=4			,     )

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					CLIPTextConfig(
           bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     projection_dim=32			,     intermediate_size=37			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=5002			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
       SCREAMING_SNAKE_CASE__					=					77

       SCREAMING_SNAKE_CASE__					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[int]=0      )	->							Union[str, Any]:
       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	List[Any]      )	->							str:
       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )






  def    lowercase_	(							self		:	List[Any]      )	->							int:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






  def    lowercase_	(							self		:	Dict      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesConfig(
           hidden_size=32			,     project_dim=32			,     intermediate_size=37			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=5002			,     )
       # TODO: remove after fixing the non-deterministic text encoder
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesModelWithTransformation(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder

       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					'''A photo of an astronaut'''
       SCREAMING_SNAKE_CASE__					=					alt_pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]      )

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







  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(skip_prk_steps=__lowerCamelCase      )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesConfig(
           hidden_size=32			,     project_dim=32			,     intermediate_size=37			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=5002			,     )
       # TODO: remove after fixing the non-deterministic text encoder
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesModelWithTransformation(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder
       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]      )

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






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






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							str:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	str      )	->							Any:
       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe([prompt]			,     generator=__lowerCamelCase			,     guidance_scale=6.0			,     num_inference_steps=20			,     output_type='''np'''      )

       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]      )

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







  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''			,     subfolder='''scheduler'''      )

       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					alt_pipe([prompt]			,     generator=__lowerCamelCase			,     num_inference_steps=2			,     output_type='''numpy'''      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]      )

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


