




'''simple docstring'''
from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)






class 			SCREAMING_SNAKE_CASE(							__A	):

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__ = 0.93   ,						lowerCamelCase__ = True   ,						)       ->   Optional[int]:

           """simple docstring"""



           __lowercase     							=		mixing_coeff
           __lowercase     							=		leapfrog_steps

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

           __lowercase     							=		2 ** (len(self.vae.config.block_out_channels	) - 1)
           __lowercase     							=		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = False	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		self.tokenizer(
               lowerCamelCase__   ,						padding="""max_length"""   ,						max_length=self.tokenizer.model_max_length   ,						truncation=lowerCamelCase__   ,						return_tensors="""pt"""   ,						)

           __lowercase     							=		self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

           __lowercase     							=		prompt_embeds.to(dtype=self.text_encoder.dtype   ,						device=self.device	)

           if do_classifier_free_guidance:
               __lowercase     							=		"""""" if negative_prompt is None else negative_prompt

               __lowercase     							=		self.tokenizer(
                   lowerCamelCase__   ,						padding="""max_length"""   ,						max_length=self.tokenizer.model_max_length   ,						truncation=lowerCamelCase__   ,						return_tensors="""pt"""   ,						)

               __lowercase     							=		self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

               __lowercase     							=		torch.cat([negative_prompt_embeds, prompt_embeds]	)

           return prompt_embeds

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

           """simple docstring"""



           __lowercase     							=		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           __lowercase     							=		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

           return [x, y]

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

           """simple docstring"""



           __lowercase     							=		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           __lowercase     							=		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

           return [x, y]

       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		int(lowerCamelCase__	)

           __lowercase     							=		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

           return alpha_prod, 1 - alpha_prod

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

           """simple docstring"""



           __lowercase     							=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           __lowercase							,__lowercase     							=		self._get_alpha_and_beta(lowerCamelCase__	)
           __lowercase							,__lowercase     							=		self._get_alpha_and_beta(lowerCamelCase__	)

           __lowercase     							=		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
           __lowercase     							=		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

           __lowercase     							=		(base - b_t * model_output) / a_t

           return model_input, next_model_input.to(base.dtype	)

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

           """simple docstring"""



           __lowercase     							=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           __lowercase							,__lowercase     							=		self._get_alpha_and_beta(lowerCamelCase__	)
           __lowercase							,__lowercase     							=		self._get_alpha_and_beta(lowerCamelCase__	)

           __lowercase     							=		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
           __lowercase     							=		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
           __lowercase     							=		a_t * base + b_t * model_output

           return model_input, next_model_input.to(base.dtype	)

       @torch.no_grad()
       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Optional[int]:

           """simple docstring"""



           __lowercase     							=		1 / self.vae.config.scaling_factor * latents
           __lowercase     							=		self.vae.decode(lowerCamelCase__	).sample
           __lowercase     							=		(image / 2 + 0.5).clamp(0   ,						1	)
           return image

       @torch.no_grad()
       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__ = None   ,						)       ->   str:

           """simple docstring"""



           __lowercase     							=		guidance_scale > 1.0

           __lowercase     							=		image.to(device=self.device   ,						dtype=text_embeds.dtype	)
           __lowercase     							=		self.vae.encode(lowerCamelCase__	).latent_dist.sample(lowerCamelCase__	)

           __lowercase     							=		self.vae.config.scaling_factor * latent

           __lowercase     							=		[latent.clone(), latent.clone()]

           for i, t in tqdm(enumerate(lowerCamelCase__	)   ,						total=len(lowerCamelCase__	)	):
               __lowercase     							=		self.noise_mixing_layer(x=coupled_latents[0]   ,						y=coupled_latents[1]	)

               # j - model_input index, k - base index
               for j in range(2	):
                   __lowercase     							=		j ^ 1

                   if self.leapfrog_steps:
                       if i % 2 == 0:
                           __lowercase							,__lowercase     							=		j, k

                   __lowercase     							=		coupled_latents[j]
                   __lowercase     							=		coupled_latents[k]

                   __lowercase     							=		torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

                   __lowercase     							=		self.unet(lowerCamelCase__   ,						lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__	).sample

                   if do_classifier_free_guidance:
                       __lowercase							,__lowercase     							=		noise_pred.chunk(2	)
                       __lowercase     							=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   __lowercase							,__lowercase     							=		self.noise_step(
                       base=lowerCamelCase__   ,						model_input=lowerCamelCase__   ,						model_output=lowerCamelCase__   ,						timestep=lowerCamelCase__   ,						)

                   __lowercase     							=		model_input

           return coupled_latents




       @torch.no_grad()
       def __call__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__ = 3.0   ,						lowerCamelCase__ = 50   ,						lowerCamelCase__ = 0.8   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = "pil"   ,						)       ->   Optional[Any]:

           """simple docstring"""



           __lowercase     							=		guidance_scale > 1.0

           __lowercase     							=		self.image_processor.preprocess(lowerCamelCase__	)

           __lowercase     							=		self._encode_prompt(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)
           __lowercase     							=		self._encode_prompt(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)

           self.scheduler.set_timesteps(lowerCamelCase__   ,						self.device	)

           __lowercase     							=		num_inference_steps - int(num_inference_steps * strength	)
           __lowercase     							=		self.scheduler.timesteps[t_limit:]
           __lowercase     							=		fwd_timesteps.flip(0	)

           __lowercase     							=		self.prepare_latents(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)

           for i, t in tqdm(enumerate(lowerCamelCase__	)   ,						total=len(lowerCamelCase__	)	):
               # j - model_input index, k - base index
               for k in range(2	):
                   __lowercase     							=		k ^ 1

                   if self.leapfrog_steps:
                       if i % 2 == 1:
                           __lowercase							,__lowercase     							=		j, k

                   __lowercase     							=		coupled_latents[j]
                   __lowercase     							=		coupled_latents[k]

                   __lowercase     							=		torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

                   __lowercase     							=		self.unet(lowerCamelCase__   ,						lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__	).sample

                   if do_classifier_free_guidance:
                       __lowercase							,__lowercase     							=		noise_pred.chunk(2	)
                       __lowercase     							=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   __lowercase							,__lowercase     							=		self.denoise_step(
                       base=lowerCamelCase__   ,						model_input=lowerCamelCase__   ,						model_output=lowerCamelCase__   ,						timestep=lowerCamelCase__   ,						)

                   __lowercase     							=		model_input

               __lowercase     							=		self.denoise_mixing_layer(x=coupled_latents[0]   ,						y=coupled_latents[1]	)

           # either one is fine
           __lowercase     							=		coupled_latents[0]

           if output_type not in ["latent", "pt", "np", "pil"]:
               __lowercase     							=		(
                   F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""   ,						"""1.0.0"""   ,						lowerCamelCase__   ,						standard_warn=lowerCamelCase__	)
               __lowercase     							=		"""np"""

           if output_type == "latent":
               __lowercase     							=		final_latent
           else:
               __lowercase     							=		self.decode_latents(lowerCamelCase__	)
               __lowercase     							=		self.image_processor.postprocess(lowerCamelCase__   ,						output_type=lowerCamelCase__	)

           return image










'''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()




