


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput



class 							A					( nn.Module							):
					'''simple docstring'''






					def __init__(self					:		Dict   ,      _UpperCAmelCase					:		Dict   ,      _UpperCAmelCase					:		Tuple=1.0	)		->     List[str]:




							"""simple docstring"""


							super().__init__()

							lowercase__      =							cut_size
							lowercase__      =							cut_power


					def 						lowerCamelCase__  (self					:		Tuple   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		Tuple	)		->     Union[str, Any]:




							"""simple docstring"""


							lowercase__    ,			lowercase__      =							pixel_values.shape[2:4]
							lowercase__      =							min(a_   ,      a_	)
							lowercase__      =							min(a_   ,      a_   ,      self.cut_size	)
							lowercase__      =							[]
							for _ in range(a_	):
									lowercase__      =							int(torch.rand([]	) ** self.cut_power * (max_size - min_size) + min_size	)
									lowercase__      =							torch.randint(0   ,      sideX - size + 1   ,      ()	)
									lowercase__      =							torch.randint(0   ,      sideY - size + 1   ,      ()	)
									lowercase__      =							pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
									cutouts.append(F.adaptive_avg_poolad(a_   ,      self.cut_size	)	)
							return torch.cat(a_	)






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							int  )     ->						Optional[int]:
		"""simple docstring"""




		lowercase__      =							F.normalize(UpperCamelCase__					,					dim=-1  )
		lowercase__      =							F.normalize(UpperCamelCase__					,					dim=-1  )
		return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Optional[Any]  )     ->						Tuple:
		"""simple docstring"""




		for param in model.parameters():
				lowercase__      =							value







class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Union[str, Any]   ,      _UpperCAmelCase					:		Any   ,      _UpperCAmelCase					:		List[Any]   ,      _UpperCAmelCase					:		Dict   ,      _UpperCAmelCase					:		List[str]   ,      _UpperCAmelCase					:		Tuple   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		Optional[Any]   ,      )		->     Optional[Any]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(
							    vae=a_   ,      text_encoder=a_   ,      clip_model=a_   ,      tokenizer=a_   ,      unet=a_   ,      scheduler=a_   ,      feature_extractor=a_   ,      )

							lowercase__      =							transforms.Normalize(mean=feature_extractor.image_mean   ,      std=feature_extractor.image_std	)
							lowercase__      =							(
							    feature_extractor.size
							    if isinstance(feature_extractor.size   ,      a_	)
							    else feature_extractor.size["""shortest_edge"""]
							)
							lowercase__      =							MakeCutouts(self.cut_out_size	)

							set_requires_grad(self.text_encoder   ,      a_	)
							set_requires_grad(self.clip_model   ,      a_	)

					def 						lowerCamelCase__  (self					:		str   ,      _UpperCAmelCase					:		List[Any] = "auto"	)		->     str:




							"""simple docstring"""


							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowercase__      =							self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(a_	)

					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     str:




							"""simple docstring"""


							self.enable_attention_slicing(a_	)

					def 						lowerCamelCase__  (self					:		List[str]	)		->     Optional[int]:




							"""simple docstring"""


							set_requires_grad(self.vae   ,      a_	)

					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Tuple:




							"""simple docstring"""


							set_requires_grad(self.vae   ,      a_	)

					def 						lowerCamelCase__  (self					:		Optional[Any]	)		->     int:




							"""simple docstring"""


							set_requires_grad(self.unet   ,      a_	)

					def 						lowerCamelCase__  (self					:		List[str]	)		->     List[Any]:




							"""simple docstring"""


							set_requires_grad(self.unet   ,      a_	)

					@torch.enable_grad()
					def 						lowerCamelCase__  (self					:		List[Any]   ,      _UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		str   ,      _UpperCAmelCase					:		List[str]   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		List[Any]   ,      _UpperCAmelCase					:		List[Any]   ,      _UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		Tuple=True   ,      )		->     Dict:




							"""simple docstring"""


							lowercase__      =							latents.detach().requires_grad_()

							lowercase__      =							self.scheduler.scale_model_input(a_   ,      a_	)

							# predict the noise residual
							lowercase__      =							self.unet(a_   ,      a_   ,      encoder_hidden_states=a_	).sample

							if isinstance(self.scheduler   ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)	):
									lowercase__      =							self.scheduler.alphas_cumprod[timestep]
									lowercase__      =							1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									lowercase__      =							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									lowercase__      =							torch.sqrt(a_	)
									lowercase__      =							pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler   ,      a_	):
									lowercase__      =							self.scheduler.sigmas[index]
									lowercase__      =							latents - sigma * noise_pred
							else:
									raise ValueError(f'''scheduler type {type(self.scheduler	)} not supported'''	)

							lowercase__      =							1 / self.vae.config.scaling_factor * sample
							lowercase__      =							self.vae.decode(a_	).sample
							lowercase__      =							(image / 2 + 0.5).clamp(0   ,      1	)

							if use_cutouts:
									lowercase__      =							self.make_cutouts(a_   ,      a_	)
							else:
									lowercase__      =							transforms.Resize(self.cut_out_size	)(a_	)
							lowercase__      =							self.normalize(a_	).to(latents.dtype	)

							lowercase__      =							self.clip_model.get_image_features(a_	)
							lowercase__      =							image_embeddings_clip / image_embeddings_clip.norm(p=2   ,      dim=-1   ,      keepdim=a_	)

							if use_cutouts:
									lowercase__      =							spherical_dist_loss(a_   ,      a_	)
									lowercase__      =							dists.view([num_cutouts, sample.shape[0], -1]	)
									lowercase__      =							dists.sum(2	).mean(0	).sum() * clip_guidance_scale
							else:
									lowercase__      =							spherical_dist_loss(a_   ,      a_	).mean() * clip_guidance_scale

							lowercase__      =							-torch.autograd.grad(a_   ,      a_	)[0]

							if isinstance(self.scheduler   ,      a_	):
									lowercase__      =							latents.detach() + grads * (sigma**2)
									lowercase__      =							noise_pred_original
							else:
									lowercase__      =							noise_pred_original - torch.sqrt(a_	) * grads
							return noise_pred, latents


					@torch.no_grad()
					def __call__(self					:		int   ,      _UpperCAmelCase					:		List[Any]   ,      _UpperCAmelCase					:		Optional[int] = 512   ,      _UpperCAmelCase					:		Optional[Any] = 512   ,      _UpperCAmelCase					:		Optional[int] = 50   ,      _UpperCAmelCase					:		str = 7.5   ,      _UpperCAmelCase					:		Optional[Any] = 1   ,      _UpperCAmelCase					:		Optional[Any] = 0.0   ,      _UpperCAmelCase					:		Union[str, Any] = 100   ,      _UpperCAmelCase					:		Optional[int] = None   ,      _UpperCAmelCase					:		int = 4   ,      _UpperCAmelCase					:		str = True   ,      _UpperCAmelCase					:		Any = None   ,      _UpperCAmelCase					:		Union[str, Any] = None   ,      _UpperCAmelCase					:		Optional[Any] = "pil"   ,      _UpperCAmelCase					:		List[str] = True   ,      )		->     str:




							"""simple docstring"""


							if isinstance(a_   ,      a_	):
									lowercase__      =							1
							elif isinstance(a_   ,      a_	):
									lowercase__      =							len(a_	)
							else:
									raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a_	)}'''	)

							if height % 8 != 0 or width % 8 != 0:
									raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''	)

							# get prompt text embeddings
							lowercase__      =							self.tokenizer(
							    a_   ,      padding="""max_length"""   ,      max_length=self.tokenizer.model_max_length   ,      truncation=a_   ,      return_tensors="""pt"""   ,      )
							lowercase__      =							self.text_encoder(text_input.input_ids.to(self.device	)	)[0]
							# duplicate text embeddings for each generation per prompt
							lowercase__      =							text_embeddings.repeat_interleave(a_   ,      dim=0	)

							if clip_guidance_scale > 0:
									if clip_prompt is not None:
											lowercase__      =							self.tokenizer(
											    a_   ,      padding="""max_length"""   ,      max_length=self.tokenizer.model_max_length   ,      truncation=a_   ,      return_tensors="""pt"""   ,      ).input_ids.to(self.device	)
									else:
											lowercase__      =							text_input.input_ids.to(self.device	)
									lowercase__      =							self.clip_model.get_text_features(a_	)
									lowercase__      =							text_embeddings_clip / text_embeddings_clip.norm(p=2   ,      dim=-1   ,      keepdim=a_	)
									# duplicate text embeddings clip for each generation per prompt
									lowercase__      =							text_embeddings_clip.repeat_interleave(a_   ,      dim=0	)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							lowercase__      =							guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
									lowercase__      =							text_input.input_ids.shape[-1]
									lowercase__      =							self.tokenizer([""""""]   ,      padding="""max_length"""   ,      max_length=a_   ,      return_tensors="""pt"""	)
									lowercase__      =							self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
									# duplicate unconditional embeddings for each generation per prompt
									lowercase__      =							uncond_embeddings.repeat_interleave(a_   ,      dim=0	)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowercase__      =							torch.cat([uncond_embeddings, text_embeddings]	)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							lowercase__      =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							lowercase__      =							text_embeddings.dtype
							if latents is None:
									if self.device.type == "mps":
											# randn does not work reproducibly on mps
											lowercase__      =							torch.randn(a_   ,      generator=a_   ,      device="""cpu"""   ,      dtype=a_	).to(
											    self.device	)
									else:
											lowercase__      =							torch.randn(a_   ,      generator=a_   ,      device=self.device   ,      dtype=a_	)
							else:
									if latents.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
									lowercase__      =							latents.to(self.device	)

							# set timesteps
							lowercase__      =							"""offset""" in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
							lowercase__      =							{}
							if accepts_offset:
									lowercase__      =							1

							self.scheduler.set_timesteps(a_   ,      **a_	)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							lowercase__      =							self.scheduler.timesteps.to(self.device	)

							# scale the initial noise by the standard deviation required by the scheduler
							lowercase__      =							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]
							lowercase__      =							"""eta""" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
							lowercase__      =							{}
							if accepts_eta:
									lowercase__      =							eta

							# check if the scheduler accepts generator
							lowercase__      =							"""generator""" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
							if accepts_generator:
									lowercase__      =							generator

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

									# predict the noise residual
									lowercase__      =							self.unet(a_   ,      a_   ,      encoder_hidden_states=a_	).sample

									# perform classifier free guidance
									if do_classifier_free_guidance:
											lowercase__    ,			lowercase__      =							noise_pred.chunk(2	)
											lowercase__      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# perform clip guidance
									if clip_guidance_scale > 0:
											lowercase__      =							(
											    text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
											)
											lowercase__    ,			lowercase__      =							self.cond_fn(
											    a_   ,      a_   ,      a_   ,      a_   ,      a_   ,      a_   ,      a_   ,      a_   ,      a_   ,      )

									# compute the previous noisy sample x_t -> x_t-1
									lowercase__      =							self.scheduler.step(a_   ,      a_   ,      a_   ,      **a_	).prev_sample

							# scale and decode the image latents with vae
							lowercase__      =							1 / self.vae.config.scaling_factor * latents
							lowercase__      =							self.vae.decode(a_	).sample

							lowercase__      =							(image / 2 + 0.5).clamp(0   ,      1	)
							lowercase__      =							image.cpu().permute(0   ,      2   ,      3   ,      1	).numpy()

							if output_type == "pil":
									lowercase__      =							self.numpy_to_pil(a_	)

							if not return_dict:
									return (image, None)

							return StableDiffusionPipelineOutput(images=a_   ,      nsfw_content_detected=a_	)





import os
import sys

from . import rsa_key_generator as rkg

A :	List[str]												=     1_2_8
A :	Optional[Any]												=     2_5_6






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						list[int]:
		"""simple docstring"""




		lowercase__      =	message.encode("""ascii"""  )

		lowercase__      =	[]
		for block_start in range(0					,					len(__magic_name__  )					,					__magic_name__  ):
				lowercase__      =	0
				for i in range(__magic_name__					,					min(block_start + block_size					,					len(__magic_name__  )  )  ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(__magic_name__  )
		return block_ints






def 				UpperCamelCase      (			__magic_name__  :							list[int]					,					__magic_name__  :							int					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						str:
		"""simple docstring"""




		lowercase__      =	[]
		for block_int in block_ints:
				lowercase__      =	[]
				for i in range(block_size - 1					,					-1					,					-1  ):
						if len(__magic_name__  ) + i < message_length:
								lowercase__      =	block_int // (BYTE_SIZE**i)
								lowercase__      =	block_int % (BYTE_SIZE**i)
								block_message.insert(0					,					chr(__magic_name__  )  )
				message.extend(__magic_name__  )
		return "".join(__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							tuple[int, int]					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						list[int]:
		"""simple docstring"""




		lowercase__      =	[]
		lowercase__    ,			lowercase__      =	key

		for block in get_blocks_from_text(__magic_name__					,					__magic_name__  ):
				encrypted_blocks.append(pow(__magic_name__					,					__magic_name__					,					__magic_name__  )  )
		return encrypted_blocks






def 				UpperCamelCase      (			__magic_name__  :							list[int]					,					__magic_name__  :							int					,					__magic_name__  :							tuple[int, int]					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE					,					)     ->						str:
		"""simple docstring"""




		lowercase__      =	[]
		lowercase__    ,			lowercase__      =	key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(__magic_name__					,					__magic_name__					,					__magic_name__  )  )
		return get_text_from_blocks(__magic_name__					,					__magic_name__					,					__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						tuple[int, int, int]:
		"""simple docstring"""




		with open(__magic_name__  ) as fo:
				lowercase__      =	fo.read()
		lowercase__    ,			lowercase__    ,			lowercase__      =	content.split(""","""  )
		return (int(__magic_name__  ), int(__magic_name__  ), int(__magic_name__  ))






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							str					,					__magic_name__  :							str					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE					,					)     ->						str:
		"""simple docstring"""




		lowercase__    ,			lowercase__    ,			lowercase__      =	read_key_file(__magic_name__  )
		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Either decrease the block size or use different keys.""".format(
				        block_size * 8					,					__magic_name__  )  )

		lowercase__      =	[str(__magic_name__  ) for i in encrypt_message(__magic_name__					,					(n, e)					,					__magic_name__  )]

		lowercase__      =	""",""".join(__magic_name__  )
		lowercase__      =	f'''{len(__magic_name__  )}_{block_size}_{encrypted_content}'''
		with open(__magic_name__					,					"""w"""  ) as fo:
				fo.write(__magic_name__  )
		return encrypted_content






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							str  )     ->						str:
		"""simple docstring"""




		lowercase__    ,			lowercase__    ,			lowercase__      =	read_key_file(__magic_name__  )
		with open(__magic_name__  ) as fo:
				lowercase__      =	fo.read()
		lowercase__    ,			lowercase__    ,			lowercase__      =	content.split("""_"""  )
		lowercase__      =	int(__magic_name__  )
		lowercase__      =	int(__magic_name__  )

		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Did you specify the correct key file and encrypted file?""".format(
				        block_size * 8					,					__magic_name__  )  )

		lowercase__      =	[]
		for block in encrypted_message.split(""","""  ):
				encrypted_blocks.append(int(__magic_name__  )  )

		return decrypt_message(__magic_name__					,					__magic_name__					,					(n, d)					,					__magic_name__  )






def 				UpperCamelCase      (			)     ->						None:
		"""simple docstring"""




		lowercase__      =	"""encrypted_file.txt"""
		lowercase__      =	input(R"""Encrypt\Decrypt [e\d]: """  )

		if response.lower().startswith("""e"""  ):
				lowercase__      =	"""encrypt"""
		elif response.lower().startswith("""d"""  ):
				lowercase__      =	"""decrypt"""

		if mode == "encrypt":
				if not os.path.exists("""rsa_pubkey.txt"""  ):
						rkg.make_key_files("""rsa"""					,					1024  )

				lowercase__      =	input("""\nEnter message: """  )
				lowercase__      =	"""rsa_pubkey.txt"""
				print(f'''Encrypting and writing to {filename}...'''  )
				lowercase__      =	encrypt_and_write_to_file(__magic_name__					,					__magic_name__					,					__magic_name__  )

				print("""\nEncrypted text:"""  )
				print(__magic_name__  )

		elif mode == "decrypt":
				lowercase__      =	"""rsa_privkey.txt"""
				print(f'''Reading from {filename} and decrypting...'''  )
				lowercase__      =	read_from_file_and_decrypt(__magic_name__					,					__magic_name__  )
				print("""writing decryption to rsa_decryption.txt..."""  )
				with open("""rsa_decryption.txt"""					,					"""w"""  ) as dec:
						dec.write(__magic_name__  )

				print("""\nDecryption:"""  )
				print(__magic_name__  )


if __name__ == "__main__":
	main()

