from collections.abc import Generator
from math import sin







def 			__UpperCamelCase     (    A				):
     if len(lowerCAmelCase__				) != 32:
          raise ValueError('''Input must be of length 32'''				)

     UpperCamelCase__					    =   B''''''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian







def 			__UpperCamelCase     (    A				):
     if i < 0:
          raise ValueError('''Input must be non-negative'''				)

     UpperCamelCase__					    =   format(lowerCAmelCase__			,				'''08x'''				)[-8:]
     UpperCamelCase__					    =   B''''''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''				)
     return little_endian_hex







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   B''''''
     for char in message:
          bit_string += format(lowerCAmelCase__			,				'''08b'''				).encode('''utf-8'''				)
     UpperCamelCase__					    =   format(len(lowerCAmelCase__				)			,				'''064b'''				).encode('''utf-8'''				)

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(lowerCAmelCase__				) % 512 != 448:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]				) + to_little_endian(start_len[:32]				)

     return bit_string







def 			__UpperCamelCase     (    A				):
     if len(lowerCAmelCase__				) % 512 != 0:
          raise ValueError('''Input must have length that\'s a multiple of 512'''				)

     for pos in range(0			,				len(lowerCAmelCase__				)			,				512				):
          UpperCamelCase__					    =   bit_string[pos : pos + 512]
          UpperCamelCase__					    =   []
          for i in range(0			,				512			,				32				):
               block_words.append(int(to_little_endian(block[i : i + 32]				)			,				2				)				)
          yield block_words







def 			__UpperCamelCase     (    A				):
     if i < 0:
          raise ValueError('''Input must be non-negative'''				)

     UpperCamelCase__					    =   format(lowerCAmelCase__			,				'''032b'''				)
     UpperCamelCase__					    =   ''''''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(lowerCAmelCase__			,				2				)







def 			__UpperCamelCase     (    A			,				A				):
     return (a + b) % 2**32







def 			__UpperCamelCase     (    A			,				A				):
     if i < 0:
          raise ValueError('''Input must be non-negative'''				)
     if shift < 0:
          raise ValueError('''Shift must be non-negative'''				)
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32







def 			__UpperCamelCase     (    A				):
     UpperCamelCase__					    =   preprocess(lowerCAmelCase__				)

     UpperCamelCase__					    =   [int(2**32 * abs(sin(i + 1				)				)				) for i in range(64				)]

     # Starting states
     UpperCamelCase__					    =   0x6745_2301
     UpperCamelCase__					    =   0xEFCD_AB89
     UpperCamelCase__					    =   0x98BA_DCFE
     UpperCamelCase__					    =   0x1032_5476

     UpperCamelCase__					    =   [
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(lowerCAmelCase__				):
          UpperCamelCase__					    =   aa
          UpperCamelCase__					    =   ba
          UpperCamelCase__					    =   ca
          UpperCamelCase__					    =   da

          # Hash current chunk
          for i in range(64				):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    UpperCamelCase__					    =   d ^ (b & (c ^ d))
                    UpperCamelCase__					    =   i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    UpperCamelCase__					    =   c ^ (d & (b ^ c))
                    UpperCamelCase__					    =   (5 * i + 1) % 16
               elif i <= 47:
                    UpperCamelCase__					    =   b ^ c ^ d
                    UpperCamelCase__					    =   (3 * i + 5) % 16
               else:
                    UpperCamelCase__					    =   c ^ (b | not_aa(lowerCAmelCase__				))
                    UpperCamelCase__					    =   (7 * i) % 16
               UpperCamelCase__					    =   (f + a + added_consts[i] + block_words[g]) % 2**32
               UpperCamelCase__					    =   d
               UpperCamelCase__					    =   c
               UpperCamelCase__					    =   b
               UpperCamelCase__					    =   sum_aa(lowerCAmelCase__			,				left_rotate_aa(lowerCAmelCase__			,				shift_amounts[i]				)				)

          # Add hashed chunk to running total
          UpperCamelCase__					    =   sum_aa(lowerCAmelCase__			,				lowerCAmelCase__				)
          UpperCamelCase__					    =   sum_aa(lowerCAmelCase__			,				lowerCAmelCase__				)
          UpperCamelCase__					    =   sum_aa(lowerCAmelCase__			,				lowerCAmelCase__				)
          UpperCamelCase__					    =   sum_aa(lowerCAmelCase__			,				lowerCAmelCase__				)

     UpperCamelCase__					    =   reformat_hex(lowerCAmelCase__				) + reformat_hex(lowerCAmelCase__				) + reformat_hex(lowerCAmelCase__				) + reformat_hex(lowerCAmelCase__				)
     return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()






'''simple docstring'''

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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

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

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





