






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCAmelCase_           = logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase_           = '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any]=8   ):
				A	 =      height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				A	 =      width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor



class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       UNetaDConditionModel						,_SCREAMING_SNAKE_CASE		:       DDPMScheduler						,_SCREAMING_SNAKE_CASE		:       VQModel						,)    ->   int:


						'''simple docstring'''
						super().__init__()

						self.register_modules(
						    unet=_SCREAMING_SNAKE_CASE						,scheduler=_SCREAMING_SNAKE_CASE						,movq=_SCREAMING_SNAKE_CASE						,)
						A	 =      2 ** (len(self.movq.config.block_out_channels		) - 1)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						if latents is None:
										A	 =      randn_tensor(_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE						,dtype=_SCREAMING_SNAKE_CASE		)
						else:
										if latents.shape != shape:
														raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
										A	 =      latents.to(_SCREAMING_SNAKE_CASE		)

						A	 =      latents * scheduler.init_noise_sigma
						return latents
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int=0		)    ->   Optional[int]:


						'''simple docstring'''
						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError('Please install accelerate via `pip install accelerate`'		)

						A	 =      torch.device(f'cuda:{gpu_id}'		)

						A	 =      [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
														cpu_offload(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[str]=0		)    ->   Optional[Any]:


						'''simple docstring'''
						if is_accelerate_available() and is_accelerate_version('>='						,'0.17.0.dev0'		):
										from accelerate import cpu_offload_with_hook
						else:
										raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'		)

						A	 =      torch.device(f'cuda:{gpu_id}'		)

						if self.device.type != "cpu":
										self.to('cpu'						,silence_dtype_warnings=_SCREAMING_SNAKE_CASE		)
										torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						A	 =      None
						for cpu_offloaded_model in [self.unet, self.movq]:
										A ,		A	 =      cpu_offload_with_hook(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,prev_module_hook=_SCREAMING_SNAKE_CASE		)

						# We'll offload the last model manually.
						A	 =      hook
		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def       A(  self		:       Dict		)    ->   Optional[int]:


						'''simple docstring'''
						if not hasattr(self.unet						,'_hf_hook'		):
										return self.device
						for module in self.unet.modules():
										if (
										    hasattr(_SCREAMING_SNAKE_CASE						,'_hf_hook'		)
										    and hasattr(module._hf_hook						,'execution_device'		)
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device		)
						return self.device







		@torch.no_grad()
		@replace_example_docstring(_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Union[torch.FloatTensor, List[torch.FloatTensor]]						,_SCREAMING_SNAKE_CASE		:       Union[torch.FloatTensor, List[torch.FloatTensor]]						,_SCREAMING_SNAKE_CASE		:       int = 5_1_2						,_SCREAMING_SNAKE_CASE		:       int = 5_1_2						,_SCREAMING_SNAKE_CASE		:       int = 1_0_0						,_SCREAMING_SNAKE_CASE		:       float = 4.0						,_SCREAMING_SNAKE_CASE		:       int = 1						,_SCREAMING_SNAKE_CASE		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[torch.FloatTensor] = None						,_SCREAMING_SNAKE_CASE		:       Optional[str] = "pil"						,_SCREAMING_SNAKE_CASE		:       bool = True						,)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self._execution_device

						A	 =      guidance_scale > 1.0

						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      torch.cat(_SCREAMING_SNAKE_CASE						,dim=0		)
						A	 =      image_embeds.shape[0] * num_images_per_prompt
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      torch.cat(_SCREAMING_SNAKE_CASE						,dim=0		)

						if do_classifier_free_guidance:
										A	 =      image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE						,dim=0		)
										A	 =      negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE						,dim=0		)

						A	 =      torch.cat([negative_image_embeds, image_embeds]						,dim=0		).to(dtype=self.unet.dtype						,device=_SCREAMING_SNAKE_CASE		)

						self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						,device=_SCREAMING_SNAKE_CASE		)
						A	 =      self.scheduler.timesteps

						A	 =      self.unet.config.in_channels

						A ,		A	 =      downscale_height_and_width(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,self.movq_scale_factor		)

						# create initial latent
						A	 =      self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)						,image_embeds.dtype						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,self.scheduler						,)

						for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE		)		):
										# expand the latents if we are doing classifier free guidance
										A	 =      torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

										A	 =      {'image_embeds': image_embeds}
										A	 =      self.unet(
										    sample=_SCREAMING_SNAKE_CASE						,timestep=_SCREAMING_SNAKE_CASE						,encoder_hidden_states=_SCREAMING_SNAKE_CASE						,added_cond_kwargs=_SCREAMING_SNAKE_CASE						,return_dict=_SCREAMING_SNAKE_CASE						,)[0]

										if do_classifier_free_guidance:
														A ,		A	 =      noise_pred.split(latents.shape[1]						,dim=1		)
														A ,		A	 =      noise_pred.chunk(2		)
														A ,		A	 =      variance_pred.chunk(2		)
														A	 =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														A	 =      torch.cat([noise_pred, variance_pred_text]						,dim=1		)

										if not (
										    hasattr(self.scheduler.config						,'variance_type'		)
										    and self.scheduler.config.variance_type in ["learned", "learned_range"]
										):
														A ,		A	 =      noise_pred.split(latents.shape[1]						,dim=1		)

										# compute the previous noisy sample x_t -> x_t-1
										A	 =      self.scheduler.step(
										    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE						,)[0]
						# post-processing
						A	 =      self.movq.decode(_SCREAMING_SNAKE_CASE						,force_not_quantize=_SCREAMING_SNAKE_CASE		)['sample']

						if output_type not in ["pt", "np", "pil"]:
										raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'		)

						if output_type in ["np", "pil"]:
										A	 =      image * 0.5 + 0.5
										A	 =      image.clamp(0						,1		)
										A	 =      image.cpu().permute(0						,2						,3						,1		).float().numpy()

						if output_type == "pil":
										A	 =      self.numpy_to_pil(_SCREAMING_SNAKE_CASE		)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)













def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      ''
				for i in table:
								res += inp[i - 1]
				return res







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				return data[1:] + data[0]







def      snake_case		(							UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Dict   ):
				A	 =      ''
				for i in range(len(UpperCAmelCase   )   ):
								if a[i] == b[i]:
												res += "0"
								else:
												res += "1"
				return res







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Optional[Any]   ):
				A	 =      int('0b' + data[0] + data[-1],						2   )
				A	 =      int('0b' + data[1:3],						2   )
				return bin(s[row][col]   )[2:]







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Any,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[int]   ):
				A	 =      message[:4]
				A	 =      message[4:]
				A	 =      apply_table(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      xor(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      apply_sbox(UpperCAmelCase,						temp[:4]   )  # noqa: E741
				A	 =      apply_sbox(UpperCAmelCase,						temp[4:]   )
				A	 =      '0' * (2 - len(UpperCAmelCase   )) + l  # noqa: E741
				A	 =      '0' * (2 - len(UpperCAmelCase   )) + r
				A	 =      apply_table(l + r,						UpperCAmelCase   )
				A	 =      xor(UpperCAmelCase,						UpperCAmelCase   )
				return temp + right


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter 10 bit key: ')
					lowerCAmelCase_           = input('Enter 8 bit message: ')

					lowerCAmelCase_           = [6, 3, 7, 4, 8, 5, 10, 9]
					lowerCAmelCase_           = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
					lowerCAmelCase_           = [2, 4, 3, 1]
					lowerCAmelCase_           = [2, 6, 3, 1, 4, 8, 5, 7]
					lowerCAmelCase_           = [4, 1, 3, 5, 7, 2, 8, 6]
					lowerCAmelCase_           = [4, 1, 2, 3, 2, 3, 4, 1]
					lowerCAmelCase_           = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
					lowerCAmelCase_           = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

					# key generation
					lowerCAmelCase_           = apply_table(key, paa_table)
					lowerCAmelCase_           = temp[:5]
					lowerCAmelCase_           = temp[5:]
					lowerCAmelCase_           = left_shift(left)
					lowerCAmelCase_           = left_shift(right)
					lowerCAmelCase_           = apply_table(left + right, pa_table)
					lowerCAmelCase_           = left_shift(left)
					lowerCAmelCase_           = left_shift(right)
					lowerCAmelCase_           = left_shift(left)
					lowerCAmelCase_           = left_shift(right)
					lowerCAmelCase_           = apply_table(left + right, pa_table)

					# encryption
					lowerCAmelCase_           = apply_table(message, IP)
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = temp[4:] + temp[:4]
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = apply_table(temp, IP_inv)
					print('Cipher text is:', CT)

					# decryption
					lowerCAmelCase_           = apply_table(CT, IP)
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = temp[4:] + temp[:4]
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = apply_table(temp, IP_inv)
					print('Plain text after decypting is:', PT)





