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  __lowerCAmelCase	(							nn.Module    ):



 '''simple docstring'''

 def __init__(      self							:		int							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		str=1.0	)			->				Tuple:
        super().__init__()

        _UpperCamelCase  :							Union[str, Any]	    =	cut_size
        _UpperCamelCase  :							Optional[Any]	    =	cut_power

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		Tuple	)			->				str:
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	pixel_values.shape[2:4]
        _UpperCamelCase  :							Dict	    =	min(lowerCamelCase							,      lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	min(lowerCamelCase							,      lowerCamelCase							,      self.cut_size	)
        _UpperCamelCase  :							Any	    =	[]
        for _ in range(lowerCamelCase	):
               _UpperCamelCase  :							List[Any]	    =	int(torch.rand([]	) ** self.cut_power * (max_size - min_size) + min_size	)
               _UpperCamelCase  :							Optional[Any]	    =	torch.randint(0							,      sideX - size + 1							,      ()	)
               _UpperCamelCase  :							List[str]	    =	torch.randint(0							,      sideY - size + 1							,      ()	)
               _UpperCamelCase  :							List[str]	    =	pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
               cutouts.append(F.adaptive_avg_poolad(lowerCamelCase							,      self.cut_size	)	)
        return torch.cat(lowerCamelCase	)







def SCREAMING_SNAKE_CASE					(  lowercase_:						Dict		,    lowercase_:						List[Any]					):
       _UpperCamelCase  :							Any	    =	F.normalize(lowercase_		,    dim=-1					)
       _UpperCamelCase  :							Tuple	    =	F.normalize(lowercase_		,    dim=-1					)
       return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Dict					):
       for param in model.parameters():
              _UpperCamelCase  :							Tuple	    =	value





class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		AutoencoderKL							,      lowerCamelCase							:		CLIPTextModel							,      lowerCamelCase							:		CLIPModel							,      lowerCamelCase							:		CLIPTokenizer							,      lowerCamelCase							:		UNetaDConditionModel							,      lowerCamelCase							:		Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,      lowerCamelCase							:		CLIPImageProcessor							,      )			->				List[str]:
        super().__init__()
        self.register_modules(
            vae=lowerCamelCase							,      text_encoder=lowerCamelCase							,      clip_model=lowerCamelCase							,      tokenizer=lowerCamelCase							,      unet=lowerCamelCase							,      scheduler=lowerCamelCase							,      feature_extractor=lowerCamelCase							,      )

        _UpperCamelCase  :							Optional[Any]	    =	transforms.Normalize(mean=feature_extractor.image_mean							,      std=feature_extractor.image_std	)
        _UpperCamelCase  :							str	    =	(
            feature_extractor.size
            if isinstance(feature_extractor.size							,      lowerCamelCase	)
            else feature_extractor.size['shortest_edge']
        )
        _UpperCamelCase  :							Optional[int]	    =	MakeCutouts(self.cut_out_size	)

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

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Optional[Union[str, int]] = "auto"	)			->				Dict:
        if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _UpperCamelCase  :							Any	    =	self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(lowerCamelCase	)

 def        __snake_case       (      self							:		int	)			->				Union[str, Any]:
        self.enable_attention_slicing(lowerCamelCase	)

 def        __snake_case       (      self							:		List[Any]	)			->				str:
        set_requires_grad(self.vae							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple	)			->				Dict:
        set_requires_grad(self.vae							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Dict:
        set_requires_grad(self.unet							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[int]	)			->				Optional[Any]:
        set_requires_grad(self.unet							,      lowerCamelCase	)

 @torch.enable_grad()
 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		str							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		List[str]=True							,      )			->				List[Any]:
        _UpperCamelCase  :							str	    =	latents.detach().requires_grad_()

        _UpperCamelCase  :							str	    =	self.scheduler.scale_model_input(lowerCamelCase							,      lowerCamelCase	)

        # predict the noise residual
        _UpperCamelCase  :							List[Any]	    =	self.unet(lowerCamelCase							,      lowerCamelCase							,      encoder_hidden_states=lowerCamelCase	).sample

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

               _UpperCamelCase  :							Tuple	    =	torch.sqrt(lowerCamelCase	)
               _UpperCamelCase  :							List[Any]	    =	pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler							,      lowerCamelCase	):
               _UpperCamelCase  :							Dict	    =	self.scheduler.sigmas[index]
               _UpperCamelCase  :							List[str]	    =	latents - sigma * noise_pred
        else:
               raise ValueError(f"scheduler type {type(self.scheduler	)} not supported"	)

        _UpperCamelCase  :							Any	    =	1 / self.vae.config.scaling_factor * sample
        _UpperCamelCase  :							Dict	    =	self.vae.decode(lowerCamelCase	).sample
        _UpperCamelCase  :							Optional[int]	    =	(image / 2 + 0.5).clamp(0							,      1	)

        if use_cutouts:
               _UpperCamelCase  :							str	    =	self.make_cutouts(lowerCamelCase							,      lowerCamelCase	)
        else:
               _UpperCamelCase  :							str	    =	transforms.Resize(self.cut_out_size	)(lowerCamelCase	)
        _UpperCamelCase  :							str	    =	self.normalize(lowerCamelCase	).to(latents.dtype	)

        _UpperCamelCase  :							Dict	    =	self.clip_model.get_image_features(lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	image_embeddings_clip / image_embeddings_clip.norm(p=2							,      dim=-1							,      keepdim=lowerCamelCase	)

        if use_cutouts:
               _UpperCamelCase  :							Any	    =	spherical_dist_loss(lowerCamelCase							,      lowerCamelCase	)
               _UpperCamelCase  :							Tuple	    =	dists.view([num_cutouts, sample.shape[0], -1]	)
               _UpperCamelCase  :							Dict	    =	dists.sum(2	).mean(0	).sum() * clip_guidance_scale
        else:
               _UpperCamelCase  :							Optional[Any]	    =	spherical_dist_loss(lowerCamelCase							,      lowerCamelCase	).mean() * clip_guidance_scale

        _UpperCamelCase  :							Tuple	    =	-torch.autograd.grad(lowerCamelCase							,      lowerCamelCase	)[0]

        if isinstance(self.scheduler							,      lowerCamelCase	):
               _UpperCamelCase  :							Any	    =	latents.detach() + grads * (sigma**2)
               _UpperCamelCase  :							int	    =	noise_pred_original
        else:
               _UpperCamelCase  :							Union[str, Any]	    =	noise_pred_original - torch.sqrt(lowerCamelCase	) * grads
        return noise_pred, latents

 @torch.no_grad()
 def __call__(      self							:		Tuple							,      lowerCamelCase							:		Union[str, List[str]]							,      lowerCamelCase							:		Optional[int] = 5_1_2							,      lowerCamelCase							:		Optional[int] = 5_1_2							,      lowerCamelCase							:		Optional[int] = 5_0							,      lowerCamelCase							:		Optional[float] = 7.5							,      lowerCamelCase							:		Optional[int] = 1							,      lowerCamelCase							:		float = 0.0							,      lowerCamelCase							:		Optional[float] = 1_0_0							,      lowerCamelCase							:		Optional[Union[str, List[str]]] = None							,      lowerCamelCase							:		Optional[int] = 4							,      lowerCamelCase							:		Optional[bool] = True							,      lowerCamelCase							:		Optional[torch.Generator] = None							,      lowerCamelCase							:		Optional[torch.FloatTensor] = None							,      lowerCamelCase							:		Optional[str] = "pil"							,      lowerCamelCase							:		bool = True							,      )			->				Any:
        if isinstance(lowerCamelCase							,      lowerCamelCase	):
               _UpperCamelCase  :							Optional[Any]	    =	1
        elif isinstance(lowerCamelCase							,      lowerCamelCase	):
               _UpperCamelCase  :							Dict	    =	len(lowerCamelCase	)
        else:
               raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase	)}"	)

        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
        _UpperCamelCase  :							str	    =	self.tokenizer(
            lowerCamelCase							,      padding='max_length'							,      max_length=self.tokenizer.model_max_length							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )
        _UpperCamelCase  :							Optional[int]	    =	self.text_encoder(text_input.input_ids.to(self.device	)	)[0]
        # duplicate text embeddings for each generation per prompt
        _UpperCamelCase  :							int	    =	text_embeddings.repeat_interleave(lowerCamelCase							,      dim=0	)

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

        # set timesteps
        _UpperCamelCase  :							Any	    =	'offset' in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
        _UpperCamelCase  :							Optional[int]	    =	{}
        if accepts_offset:
               _UpperCamelCase  :							int	    =	1

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

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

        # scale the initial noise by the standard deviation required by the scheduler
        _UpperCamelCase  :							List[str]	    =	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]
        _UpperCamelCase  :							Optional[Any]	    =	'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
        _UpperCamelCase  :							List[Any]	    =	{}
        if accepts_eta:
               _UpperCamelCase  :							Tuple	    =	eta

        # check if the scheduler accepts generator
        _UpperCamelCase  :							List[str]	    =	'generator' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
        if accepts_generator:
               _UpperCamelCase  :							int	    =	generator

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

               # predict the noise residual
               _UpperCamelCase  :							str	    =	self.unet(lowerCamelCase							,      lowerCamelCase							,      encoder_hidden_states=lowerCamelCase	).sample

               # perform classifier free guidance
               if do_classifier_free_guidance:
                      _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	noise_pred.chunk(2	)
                      _UpperCamelCase  :							Dict	    =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               # perform clip guidance
               if clip_guidance_scale > 0:
                      _UpperCamelCase  :							Optional[Any]	    =	(
                          text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
                      )
                      _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	self.cond_fn(
                          lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      )

               # compute the previous noisy sample x_t -> x_t-1
               _UpperCamelCase  :							Dict	    =	self.scheduler.step(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      **lowerCamelCase	).prev_sample

        # scale and decode the image latents with vae
        _UpperCamelCase  :							Optional[int]	    =	1 / self.vae.config.scaling_factor * latents
        _UpperCamelCase  :							Tuple	    =	self.vae.decode(lowerCamelCase	).sample

        _UpperCamelCase  :							Dict	    =	(image / 2 + 0.5).clamp(0							,      1	)
        _UpperCamelCase  :							Optional[Any]	    =	image.cpu().permute(0							,      2							,      3							,      1	).numpy()

        if output_type == "pil":
               _UpperCamelCase  :							Union[str, Any]	    =	self.numpy_to_pil(lowerCamelCase	)

        if not return_dict:
               return (image, None)

        return StableDiffusionPipelineOutput(images=lowerCamelCase							,      nsfw_content_detected=lowerCamelCase	)







"""simple docstring"""
from __future__ import annotations

import math
import random






def 						lowercase				(	_snake_case		: int	,  _snake_case		: int	,  _snake_case		: int	)	->list[int]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							d * e - 1
	__snake_case      :		List[str]         =							0
	__snake_case      :		Dict         =							0
	while p == 0:
		__snake_case      :		str         =							random.randint(2	,  n - 1	)
		__snake_case      :		int         =							k
		while True:
			if t % 2 == 0:
				__snake_case      :		int         =							t // 2
				__snake_case      :		int         =							(g**t) % n
				__snake_case      :		Optional[Any]         =							math.gcd(x - 1	,  _snake_case	)
				if x > 1 and y > 1:
					__snake_case      :		Tuple         =							y
					__snake_case      :		int         =							n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





