






'''simple docstring'''



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




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



         '''simple docstring'''
         __a				     =    data
         __a				     =    [0x67452301, 0xefcdab89, 0x98badcfe, 0x10325476, 0xc3d2e1f0]




      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         return ((n << b) | (n >> (32 - b))) & 0xffffffff




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    b'''\x80''' + b'''\x00''' * (63 - (len(self.data      ) + 8) % 64)
         __a				     =    self.data + padding + struct.pack('>Q' ,   8 * len(self.data      )      )
         return padded_data




      def  __UpperCamelCase							(  self      )							->Any:



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




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



         '''simple docstring'''
         __a				     =    list(struct.unpack('>16L' ,   UpperCAmelCase__      )      ) + [0] * 64
         for i in range(16 ,   80      ):
            __a				     =    self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) ,   1      )
         return w






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



         '''simple docstring'''
         __a				     =    self.padding()
         __a				     =    self.split_blocks()
         for block in self.blocks:
            __a				     =    self.expand_block(UpperCAmelCase__      )
            __a				     =    self.h
            for i in range(0 ,   80      ):
               if 0 <= i < 20:
                  __a				     =    (b & c) | ((~b) & d)
                  __a				     =    0x5a827999
               elif 20 <= i < 40:
                  __a				     =    b ^ c ^ d
                  __a				     =    0x6ed9eba1
               elif 40 <= i < 60:
                  __a				     =    (b & c) | (b & d) | (c & d)
                  __a				     =    0x8f1bbcdc
               elif 60 <= i < 80:
                  __a				     =    b ^ c ^ d
                  __a				     =    0xca62c1d6
               __a				     =    (
                   self.rotate(UpperCAmelCase__ ,   5      ) + f + e + k + expanded_block[i] & 0xffffffff,
                   a,
                   self.rotate(UpperCAmelCase__ ,   30      ),
                   c,
                   d,
               )
            __a				     =    (
                self.h[0] + a & 0xffffffff,
                self.h[1] + b & 0xffffffff,
                self.h[2] + c & 0xffffffff,
                self.h[3] + d & 0xffffffff,
                self.h[4] + e & 0xffffffff,
            )
         return ("{:08x}" * 5).format(*self.h      )







def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    b'''Test String'''
   assert SHAaHash(SCREAMING_SNAKE_CASE__    ).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__    ).hexdigest()  # noqa: S324



def    __UpperCAmelCase	(				)  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    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'    )
   __a				     =    parser.parse_args()
   __a				     =    args.input_string
   # In any case hash input should be a bytestring
   if args.input_file:
      with open(args.input_file,					'rb'    ) as f:
         __a				     =    f.read()
   else:
      __a				     =    bytes(SCREAMING_SNAKE_CASE__,					'utf-8'    )
   print(SHAaHash(SCREAMING_SNAKE_CASE__    ).final_hash()    )


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

 doctest.testmod()







'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a     ,			__a				     =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   __a				     =    image.resize((w, h),					resample=PIL_INTERPOLATION['lanczos']    )
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    ).astype(np.floataa    ) / 2_5_5.0
   __a				     =    image[None].transpose(0,					3,					1,					2    )
   __a				     =    torch.from_numpy(SCREAMING_SNAKE_CASE__    )
   return 2.0 * image - 1.0



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




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



         '''simple docstring'''
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase ,   unet=lowerCamelCase ,   scheduler=lowerCamelCase      )






      @torch.no_grad()
      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 100 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = None ,   lowerCamelCase = "pil" ,   lowerCamelCase = True ,   )							->Union[Tuple, ImagePipelineOutput]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    1
         elif isinstance(lowerCamelCase ,   torch.Tensor      ):
            __a				     =    image.shape[0]
         else:
            raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase      )}"""      )

         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    preprocess(lowerCamelCase      )

         __a     ,			__a				     =    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         __a				     =    (batch_size, self.unet.config.in_channels // 2, height, width)
         __a				     =    next(self.unet.parameters()      ).dtype

         __a				     =    randn_tensor(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device ,   dtype=lowerCamelCase      )

         __a				     =    image.to(device=self.device ,   dtype=lowerCamelCase      )

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase ,   device=self.device      )
         __a				     =    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         __a				     =    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         __a				     =    'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
         __a				     =    {}
         if accepts_eta:
            __a				     =    eta

         for t in self.progress_bar(lowerCamelCase      ):
            # concat latents and low resolution image in the channel dimension.
            __a				     =    torch.cat([latents, image] ,   dim=1      )
            __a				     =    self.scheduler.scale_model_input(lowerCamelCase ,   lowerCamelCase      )
            # predict the noise residual
            __a				     =    self.unet(lowerCamelCase ,   lowerCamelCase      ).sample
            # compute the previous noisy sample x_t -> x_t-1
            __a				     =    self.scheduler.step(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      ).prev_sample

         # decode the image latents with the VQVAE
         __a				     =    self.vqvae.decode(lowerCamelCase      ).sample
         __a				     =    torch.clamp(lowerCamelCase ,   -1.0 ,   1.0      )
         __a				     =    image / 2 + 0.5
         __a				     =    image.cpu().permute(0 ,   2 ,   3 ,   1      ).numpy()

         if output_type == "pil":
            __a				     =    self.numpy_to_pil(lowerCamelCase      )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )