




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        UpperCAmelCase__				(							nn.Module				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=1.0	):
            super().__init__()

            a			   =cut_size
            a			   =cut_power






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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[Any]   ,       UpperCAmelCase_	:      List[Any]      )->			Optional[Any]:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




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






class        UpperCAmelCase__				(							lowerCamelCase_				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    ):
            super().__init__()
            self.register_modules(
                vae=__UpperCAmelCase ,    text_encoder=__UpperCAmelCase ,    clip_model=__UpperCAmelCase ,    tokenizer=__UpperCAmelCase ,    unet=__UpperCAmelCase ,    scheduler=__UpperCAmelCase ,    feature_extractor=__UpperCAmelCase ,    )

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

            set_requires_grad(self.text_encoder ,    __UpperCAmelCase	)
            set_requires_grad(self.clip_model ,    __UpperCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase = "auto"	):



            if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   a			   =self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            self.enable_attention_slicing(__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            set_requires_grad(self.vae ,    __UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            set_requires_grad(self.vae ,    __UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            set_requires_grad(self.unet ,    __UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            set_requires_grad(self.unet ,    __UpperCAmelCase	)
     @torch.enable_grad()
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=True ,    ):
            a			   =latents.detach().requires_grad_()

            a			   =self.scheduler.scale_model_input(__UpperCAmelCase ,    __UpperCAmelCase	)

            # predict the noise residual
            a			   =self.unet(__UpperCAmelCase ,    __UpperCAmelCase ,    encoder_hidden_states=__UpperCAmelCase	).sample

            if isinstance(self.scheduler ,    (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)	):
                   a			   =self.scheduler.alphas_cumprod[timestep]
                   a			   =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
                   a			   =(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

            a			   =-torch.autograd.grad(__UpperCAmelCase ,    __UpperCAmelCase	)[0]

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






     @torch.no_grad()
     def __call__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase = 512 ,    _lowerCAmelCase = 512 ,    _lowerCAmelCase = 50 ,    _lowerCAmelCase = 7.5 ,    _lowerCAmelCase = 1 ,    _lowerCAmelCase = 0.0 ,    _lowerCAmelCase = 100 ,    _lowerCAmelCase = None ,    _lowerCAmelCase = 4 ,    _lowerCAmelCase = True ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    _lowerCAmelCase = "pil" ,    _lowerCAmelCase = True ,    ):



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

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

            if clip_guidance_scale > 0:
                   if clip_prompt is not None:
                          a			   =self.tokenizer(
                              __UpperCAmelCase ,    padding="""max_length""" ,    max_length=self.tokenizer.model_max_length ,    truncation=__UpperCAmelCase ,    return_tensors="""pt""" ,    ).input_ids.to(self.device	)
                   else:
                          a			   =text_input.input_ids.to(self.device	)
                   a			   =self.clip_model.get_text_features(__UpperCAmelCase	)
                   a			   =text_embeddings_clip / text_embeddings_clip.norm(p=2 ,    dim=-1 ,    keepdim=__UpperCAmelCase	)
                   # duplicate text embeddings clip for each generation per prompt
                   a			   =text_embeddings_clip.repeat_interleave(__UpperCAmelCase ,    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.
            a			   =guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                   a			   =text_input.input_ids.shape[-1]
                   a			   =self.tokenizer([""""""] ,    padding="""max_length""" ,    max_length=__UpperCAmelCase ,    return_tensors="""pt"""	)
                   a			   =self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
                   # duplicate unconditional embeddings for each generation per prompt
                   a			   =uncond_embeddings.repeat_interleave(__UpperCAmelCase ,    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
                   a			   =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`.
            a			   =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            a			   =text_embeddings.dtype
            if latents is None:
                   if self.device.type == "mps":
                          # randn does not work reproducibly on mps
                          a			   =torch.randn(__UpperCAmelCase ,    generator=__UpperCAmelCase ,    device="""cpu""" ,    dtype=__UpperCAmelCase	).to(
                              self.device	)
                   else:
                          a			   =torch.randn(__UpperCAmelCase ,    generator=__UpperCAmelCase ,    device=self.device ,    dtype=__UpperCAmelCase	)
            else:
                   if latents.shape != latents_shape:
                          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
                   a			   =latents.to(self.device	)

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

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

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

            # 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

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

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

                   # predict the noise residual
                   a			   =self.unet(__UpperCAmelCase ,    __UpperCAmelCase ,    encoder_hidden_states=__UpperCAmelCase	).sample

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

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

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

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

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

            if output_type == "pil":
                   a			   =self.numpy_to_pil(__UpperCAmelCase	)

            if not return_dict:
                   return (image, None)

            return StableDiffusionPipelineOutput(images=__UpperCAmelCase ,    nsfw_content_detected=__UpperCAmelCase	)






__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCamelCase_     				= gear_one[0]
			gear_one.append(__snake_case					)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(__snake_case					)					) == 0:
						UpperCamelCase_     				= gear_two[0]
						gear_two.append(__snake_case					)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(__snake_case					)					) == 0:
									UpperCamelCase_     				= gear_three[0]
									gear_three.append(__snake_case					)
									del gear_three[0]
									gear_three_pos += 1




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= alphabets.index(__snake_case					)
			UpperCamelCase_     				= gear_one[target]
			UpperCamelCase_     				= gear_two[target]
			UpperCamelCase_     				= gear_three[target]
			UpperCamelCase_     				= reflector[target]
			UpperCamelCase_     				= gear_three.index(__snake_case					)
			UpperCamelCase_     				= gear_two.index(__snake_case					)
			UpperCamelCase_     				= gear_one.index(__snake_case					)
			code.append(alphabets[target]					)
			rotator()


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[Any]							     =		int(input("""Please set token:(must be only digits)\n"""))
												break
								except Exception as error:
												print(error)
				for _ in range(token):
								rotator()
				for j in decode:
								engine(j)
				print("""\n""" + """""".join(code))
				print(
				    f"""\nYour Token is {token} please write it down.\nIf you want to decode """
				    """this message again you should input same digits as token!"""
				)
