




'''simple docstring'''
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 			SCREAMING_SNAKE_CASE(							nn.Module	):

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__=1.0	)       ->   List[str]:

           """simple docstring"""



           super().__init__()

           __lowercase     							=		cut_size
           __lowercase     							=		cut_power




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

           """simple docstring"""



           __lowercase							,__lowercase     							=		pixel_values.shape[2:4]
           __lowercase     							=		min(lowerCamelCase__   ,						lowerCamelCase__	)
           __lowercase     							=		min(lowerCamelCase__   ,						lowerCamelCase__   ,						self.cut_size	)
           __lowercase     							=		[]
           for _ in range(lowerCamelCase__	):
               __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(lowerCamelCase__   ,						self.cut_size	)	)
           return torch.cat(lowerCamelCase__	)

def 							snake_case_    (  a__						:      Tuple       ,a__						:      str     ):



    """simple docstring"""




    __lowercase     							=		F.normalize(a__       ,dim=-1     )
    __lowercase     							=		F.normalize(a__       ,dim=-1     )
    return (x - y).norm(dim=-1     ).div(2     ).arcsin().pow(2     ).mul(2     )
def 							snake_case_    (  a__						:      Union[str, Any]       ,a__						:      Optional[int]     ):



    """simple docstring"""




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






class 			SCREAMING_SNAKE_CASE(							__A	):

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

           """simple docstring"""



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

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

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

       def        snake_case__     (       self   ,						lowerCamelCase__ = "auto"	)       ->   List[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(lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   List[str]:

           """simple docstring"""



           self.enable_attention_slicing(lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           set_requires_grad(self.vae   ,						lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           set_requires_grad(self.vae   ,						lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           set_requires_grad(self.unet   ,						lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           set_requires_grad(self.unet   ,						lowerCamelCase__	)

       @torch.enable_grad()
       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__=True   ,						)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		latents.detach().requires_grad_()

           __lowercase     							=		self.scheduler.scale_model_input(lowerCamelCase__   ,						lowerCamelCase__	)

           # predict the noise residual
           __lowercase     							=		self.unet(lowerCamelCase__   ,						lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__	).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(lowerCamelCase__	)
               __lowercase     							=		pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler   ,						lowerCamelCase__	):
               __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(lowerCamelCase__	).sample
           __lowercase     							=		(image / 2 + 0.5).clamp(0   ,						1	)

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

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

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

           __lowercase     							=		-torch.autograd.grad(lowerCamelCase__   ,						lowerCamelCase__	)[0]

           if isinstance(self.scheduler   ,						lowerCamelCase__	):
               __lowercase     							=		latents.detach() + grads * (sigma**2)
               __lowercase     							=		noise_pred_original
           else:
               __lowercase     							=		noise_pred_original - torch.sqrt(lowerCamelCase__	) * 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   ,						)       ->   List[Any]:

           """simple docstring"""



           if isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
               __lowercase     							=		1
           elif isinstance(lowerCamelCase__   ,						lowerCamelCase__	):
               __lowercase     							=		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
           __lowercase     							=		self.tokenizer(
               lowerCamelCase__   ,						padding="""max_length"""   ,						max_length=self.tokenizer.model_max_length   ,						truncation=lowerCamelCase__   ,						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(lowerCamelCase__   ,						dim=0	)

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

           # 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(lowerCamelCase__	)	):
               # 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(lowerCamelCase__   ,						lowerCamelCase__	)

               # predict the noise residual
               __lowercase     							=		self.unet(lowerCamelCase__   ,						lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__	).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(
                       lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						)

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

           # scale and decode the image latents with vae
           __lowercase     							=		1 / self.vae.config.scaling_factor * latents
           __lowercase     							=		self.vae.decode(lowerCamelCase__	).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(lowerCamelCase__	)

           if not return_dict:
               return (image, None)

           return StableDiffusionPipelineOutput(images=lowerCamelCase__   ,						nsfw_content_detected=lowerCamelCase__	)










'''simple docstring'''
import numpy as np
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase							,__lowercase     							=		a.shape
    __lowercase     							=		min(a__       ,a__     )
    __lowercase     							=		np.eye(a__     )
    __lowercase     							=		a.copy()

    for k in range(t - 1     ):
        # select a column of modified matrix A':
        __lowercase     							=		r[k:, [k]]
        # construct first basis vector
        __lowercase     							=		np.zeros_like(a__     )
        __lowercase     							=		1.0
        # determine scaling factor
        __lowercase     							=		np.linalg.norm(a__     )
        # construct vector v for Householder reflection
        __lowercase     							=		x + np.sign(x[0]     ) * alpha * ea
        v /= np.linalg.norm(a__     )

        # construct the Householder matrix
        __lowercase     							=		np.eye(m - k     ) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        __lowercase     							=		np.block([[np.eye(a__     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

        __lowercase     							=		q @ q_k.T
        __lowercase     							=		q_k @ r

    return q, r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




