

"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		KandinskyVaaControlnetPipeline
  _UpperCAmelCase          =		["image_embeds", "negative_image_embeds", "hint"]
  _UpperCAmelCase          =		["image_embeds", "negative_image_embeds", "hint"]
  _UpperCAmelCase          =		[
      "generator",
      "height",
      "width",
      "latents",
      "guidance_scale",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  _UpperCAmelCase          =		False

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return 32

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return 32

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.time_input_dim

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.time_input_dim * 4

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return 100

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )

      _lowerCAmelCase       :   Union[str, Any]  =    {
          'in_channels': 8,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'image_hint',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      _lowerCAmelCase       :   int  =    UNetaDConditionModel(**_A      )
      return model

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    VQModel(**self.dummy_movq_kwargs      )
      return model

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.dummy_unet
      _lowerCAmelCase       :   int  =    self.dummy_movq

      _lowerCAmelCase       :   str  =    DDIMScheduler(
          num_train_timesteps=1000    ,beta_schedule='linear'    ,beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,clip_sample=_A    ,set_alpha_to_one=_A    ,steps_offset=1    ,prediction_type='epsilon'    ,thresholding=_A    ,)

      _lowerCAmelCase       :   Any  =    {
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    floats_tensor((1, self.text_embedder_hidden_size)    ,rng=random.Random(_A      )      ).to(_A      )
      _lowerCAmelCase       :   Any  =    floats_tensor((1, self.text_embedder_hidden_size)    ,rng=random.Random(seed + 1      )      ).to(
          _A      )

      # create hint
      _lowerCAmelCase       :   Union[str, Any]  =    floats_tensor((1, 3, 64, 64)    ,rng=random.Random(_A      )      ).to(_A      )

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   List[Any]  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   str  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   str  =    {
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'hint': hint,
          'generator': generator,
          'height': 64,
          'width': 64,
          'guidance_scale': 4.0,
          'num_inference_steps': 2,
          'output_type': 'np',
      }
      return inputs







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    'cpu'

      _lowerCAmelCase       :   Tuple  =    self.get_dummy_components()

      _lowerCAmelCase       :   List[Any]  =    self.pipeline_class(**_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    pipe.to(_A      )

      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   int  =    pipe(**self.get_dummy_inputs(_A      )      )
      _lowerCAmelCase       :   Any  =    output.images

      _lowerCAmelCase       :   List[str]  =    pipe(
          **self.get_dummy_inputs(_A      )    ,return_dict=_A    ,)[0]

      _lowerCAmelCase       :   List[Any]  =    image[0, -3:, -3:, -1]
      _lowerCAmelCase       :   List[Any]  =    image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      _lowerCAmelCase       :   int  =    np.array(
          [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]      )

      assert (
          np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
      ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
      ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'      )

      _lowerCAmelCase       :   List[str]  =    load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/hint_image_cat.png'      )
      _lowerCAmelCase       :   Optional[int]  =    torch.from_numpy(np.array(_A      )      ).float() / 2_5_5.0
      _lowerCAmelCase       :   Tuple  =    hint.permute(2    ,0    ,1      ).unsqueeze(0      )

      _lowerCAmelCase       :   List[str]  =    KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior'    ,torch_dtype=torch.floataa      )
      pipe_prior.to(_A      )

      _lowerCAmelCase       :   Any  =    KandinskyVaaControlnetPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,torch_dtype=torch.floataa      )
      _lowerCAmelCase       :   int  =    pipeline.to(_A      )
      pipeline.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   List[str]  =    'A robot, 4k photo'

      _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device='cuda'      ).manual_seed(0      )
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    pipe_prior(
          _A    ,generator=_A    ,num_inference_steps=5    ,negative_prompt=''    ,).to_tuple()

      _lowerCAmelCase       :   Any  =    torch.Generator(device='cuda'      ).manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    pipeline(
          image_embeds=_A    ,negative_image_embeds=_A    ,hint=_A    ,generator=_A    ,num_inference_steps=100    ,output_type='np'    ,)

      _lowerCAmelCase       :   Dict  =    output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(_A    ,_A      )








"""simple docstring"""






import argparse
import struct
import unittest







class 	__UpperCamelCase    :

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    data

      # Initialize hash values
      _lowerCAmelCase       :   Any  =    [
          0x6A09_E667,
          0xBB67_AE85,
          0x3C6E_F372,
          0xA54F_F53A,
          0x510E_527F,
          0x9B05_688C,
          0x1F83_D9AB,
          0x5BE0_CD19,
      ]

      # Initialize round constants
      _lowerCAmelCase       :   str  =    [
          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,
      ]

      _lowerCAmelCase       :   Any  =    self.preprocessing(self.data      )
      self.final_hash()

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    b'\x80' + (b'\x00' * (63 - (len(_A      ) + 8) % 64))
      _lowerCAmelCase       :   Any  =    struct.pack('>Q'    ,(len(_A      ) * 8)      )
      return data + padding + big_endian_integer

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.hashes

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

                  _lowerCAmelCase       :   str  =    (
                      words[index - 16] + sa + words[index - 7] + sa
                  ) % 0x1_0000_0000

              # Compression
              _lowerCAmelCase       :   Optional[Any]  =    self.ror(_A    ,6      ) ^ self.ror(_A    ,11      ) ^ self.ror(_A    ,25      )
              _lowerCAmelCase       :   int  =    (e & f) ^ ((~e & 0xFFFF_FFFF) & g)
              _lowerCAmelCase       :   int  =    (
                  h + sa + ch + self.round_constants[index] + words[index]
              ) % 0x1_0000_0000
              _lowerCAmelCase       :   Union[str, Any]  =    self.ror(_A    ,2      ) ^ self.ror(_A    ,13      ) ^ self.ror(_A    ,22      )
              _lowerCAmelCase       :   Any  =    (a & b) ^ (a & c) ^ (b & c)
              _lowerCAmelCase       :   Any  =    (sa + maj) % 0x1_0000_0000

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

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

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

      _lowerCAmelCase       :   List[str]  =    ''.join([hex(_A      )[2:].zfill(8      ) for value in self.hashes]      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

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







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      import hashlib

      _lowerCAmelCase       :   Any  =    bytes('Test String'    ,'utf-8'      )
      self.assertEqual(SHAaaa(_A      ).hash    ,hashlib.shaaaa(_A      ).hexdigest()      )





def 		lowerCamelCase__		(					):

    '''simple docstring'''



    import doctest

    doctest.testmod()

    _lowerCAmelCase       :   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'   )

    _lowerCAmelCase       :   Tuple  =    parser.parse_args()

    _lowerCAmelCase       :   List[str]  =    args.input_string

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

    print(SHAaaa(_lowerCamelCase   ).hash   )


if __name__ == "__main__":
 main()





