


'''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  __lowerCamelCase (       a_       ):
    """simple docstring"""


    def __init__(  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							AutoencoderKL      ,							SCREAMING_SNAKE_CASE			:							CLIPTextModel      ,							SCREAMING_SNAKE_CASE			:							CLIPTokenizer      ,							SCREAMING_SNAKE_CASE			:							UNetaDConditionModel      ,							SCREAMING_SNAKE_CASE			:							DDIMScheduler      ,							SCREAMING_SNAKE_CASE			:							float = 0.93      ,							SCREAMING_SNAKE_CASE			:							bool = True      ,							):
           _A     :	Tuple      =					mixing_coeff
           _A     :	Union[str, Any]      =					leapfrog_steps

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

           _A     :	List[Any]      =					2 ** (len(self.vae.config.block_out_channels) - 1)
           _A     :	Any      =					VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)


    def    A    (  self			:							Any      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Optional[str] = None      ,							SCREAMING_SNAKE_CASE			:							bool = False):
           _A     :	Optional[Any]      =					self.tokenizer(
               SCREAMING_SNAKE_CASE      ,							padding='max_length'      ,							max_length=self.tokenizer.model_max_length      ,							truncation=SCREAMING_SNAKE_CASE      ,							return_tensors='pt'      ,							)

           _A     :	int      =					self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state

           _A     :	List[Any]      =					prompt_embeds.to(dtype=self.text_encoder.dtype      ,							device=self.device)

           if do_classifier_free_guidance:
                  _A     :	Dict      =					'' if negative_prompt is None else negative_prompt

                  _A     :	List[Any]      =					self.tokenizer(
                      SCREAMING_SNAKE_CASE      ,							padding='max_length'      ,							max_length=self.tokenizer.model_max_length      ,							truncation=SCREAMING_SNAKE_CASE      ,							return_tensors='pt'      ,							)

                  _A     :	List[str]      =					self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state

                  _A     :	List[str]      =					torch.cat([negative_prompt_embeds, prompt_embeds])

           return prompt_embeds


    def    A    (  self			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor):
           _A     :	Dict      =					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           _A     :	Any      =					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

           return [x, y]


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor):
           _A     :	Optional[Any]      =					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           _A     :	List[str]      =					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

           return [x, y]


    def    A    (  self			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor):
           # as self.alphas_cumprod is always in cpu
           _A     :	Tuple      =					int(SCREAMING_SNAKE_CASE)

           _A     :	Union[str, Any]      =					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

           return alpha_prod, 1 - alpha_prod


    def    A    (  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							):
           _A     :	Any      =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           _A  , _A     :	Optional[Any]      =					self._get_alpha_and_beta(SCREAMING_SNAKE_CASE)
           _A  , _A     :	Any      =					self._get_alpha_and_beta(SCREAMING_SNAKE_CASE)

           _A     :	Tuple      =					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
           _A     :	Optional[int]      =					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

           _A     :	Optional[int]      =					(base - b_t * model_output) / a_t

           return model_input, next_model_input.to(base.dtype)


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							):
           _A     :	Optional[int]      =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           _A  , _A     :	Optional[int]      =					self._get_alpha_and_beta(SCREAMING_SNAKE_CASE)
           _A  , _A     :	Tuple      =					self._get_alpha_and_beta(SCREAMING_SNAKE_CASE)

           _A     :	str      =					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
           _A     :	Dict      =					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
           _A     :	int      =					a_t * base + b_t * model_output

           return model_input, next_model_input.to(base.dtype)


    @torch.no_grad()
    def    A    (  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor):
           _A     :	Union[str, Any]      =					1 / self.vae.config.scaling_factor * latents
           _A     :	Any      =					self.vae.decode(SCREAMING_SNAKE_CASE).sample
           _A     :	Optional[int]      =					(image / 2 + 0.5).clamp(0      ,							1)
           return image


    @torch.no_grad()
    def    A    (  self			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							Image.Image      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							torch.Tensor      ,							SCREAMING_SNAKE_CASE			:							float      ,							SCREAMING_SNAKE_CASE			:							Optional[torch.Generator] = None      ,							):
           _A     :	Union[str, Any]      =					guidance_scale > 1.0

           _A     :	List[Any]      =					image.to(device=self.device      ,							dtype=text_embeds.dtype)
           _A     :	List[str]      =					self.vae.encode(SCREAMING_SNAKE_CASE).latent_dist.sample(SCREAMING_SNAKE_CASE)

           _A     :	List[str]      =					self.vae.config.scaling_factor * latent

           _A     :	List[str]      =					[latent.clone(), latent.clone()]

           for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE)      ,							total=len(SCREAMING_SNAKE_CASE)):
                  _A     :	List[str]      =					self.noise_mixing_layer(x=coupled_latents[0]      ,							y=coupled_latents[1])

                  # j - model_input index, k - base index
                  for j in range(2):
                         _A     :	List[Any]      =					j ^ 1

                         if self.leapfrog_steps:
                                if i % 2 == 0:
                                       _A  , _A     :	List[Any]      =					j, k

                         _A     :	List[Any]      =					coupled_latents[j]
                         _A     :	List[str]      =					coupled_latents[k]

                         _A     :	List[Any]      =					torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

                         _A     :	Optional[Any]      =					self.unet(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							encoder_hidden_states=SCREAMING_SNAKE_CASE).sample

                         if do_classifier_free_guidance:
                                _A  , _A     :	Union[str, Any]      =					noise_pred.chunk(2)
                                _A     :	str      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                         _A  , _A     :	Optional[int]      =					self.noise_step(
                             base=SCREAMING_SNAKE_CASE      ,							model_input=SCREAMING_SNAKE_CASE      ,							model_output=SCREAMING_SNAKE_CASE      ,							timestep=SCREAMING_SNAKE_CASE      ,							)

                         _A     :	Dict      =					model_input

           return coupled_latents


    @torch.no_grad()
    def __call__(  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Image.Image      ,							SCREAMING_SNAKE_CASE			:							float = 3.0      ,							SCREAMING_SNAKE_CASE			:							int = 50      ,							SCREAMING_SNAKE_CASE			:							float = 0.8      ,							SCREAMING_SNAKE_CASE			:							Optional[str] = None      ,							SCREAMING_SNAKE_CASE			:							Optional[torch.Generator] = None      ,							SCREAMING_SNAKE_CASE			:							Optional[str] = "pil"      ,							):
           _A     :	Union[str, Any]      =					guidance_scale > 1.0

           _A     :	int      =					self.image_processor.preprocess(SCREAMING_SNAKE_CASE)

           _A     :	Dict      =					self._encode_prompt(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Any      =					self._encode_prompt(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

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

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

           _A     :	List[str]      =					self.prepare_latents(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

           for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE)      ,							total=len(SCREAMING_SNAKE_CASE)):
                  # j - model_input index, k - base index
                  for k in range(2):
                         _A     :	Optional[Any]      =					k ^ 1

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

                         _A     :	Tuple      =					coupled_latents[j]
                         _A     :	Dict      =					coupled_latents[k]

                         _A     :	List[str]      =					torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

                         _A     :	List[str]      =					self.unet(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							encoder_hidden_states=SCREAMING_SNAKE_CASE).sample

                         if do_classifier_free_guidance:
                                _A  , _A     :	Dict      =					noise_pred.chunk(2)
                                _A     :	Optional[Any]      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                         _A  , _A     :	Any      =					self.denoise_step(
                             base=SCREAMING_SNAKE_CASE      ,							model_input=SCREAMING_SNAKE_CASE      ,							model_output=SCREAMING_SNAKE_CASE      ,							timestep=SCREAMING_SNAKE_CASE      ,							)

                         _A     :	List[str]      =					model_input

                  _A     :	Tuple      =					self.denoise_mixing_layer(x=coupled_latents[0]      ,							y=coupled_latents[1])

           # either one is fine
           _A     :	Any      =					coupled_latents[0]

           if output_type not in ["latent", "pt", "np", "pil"]:
                  _A     :	Optional[int]      =					(
                      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'      ,							SCREAMING_SNAKE_CASE      ,							standard_warn=SCREAMING_SNAKE_CASE)
                  _A     :	Dict      =					'np'

           if output_type == "latent":
                  _A     :	Dict      =					final_latent
           else:
                  _A     :	Tuple      =					self.decode_latents(SCREAMING_SNAKE_CASE)
                  _A     :	List[Any]      =					self.image_processor.postprocess(SCREAMING_SNAKE_CASE      ,							output_type=SCREAMING_SNAKE_CASE)

           return image










'''simple docstring'''







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							str      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=sys.maxsize):
           _A     :	Tuple      =					'bilinear'
           _A     :	List[Any]      =					max_size
           _A     :	Optional[int]      =					short_edge_length


    def __call__(  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]):
           _A     :	Dict      =					[]
           for img in imgs:
                  _A  , _A     :	Dict      =					img.shape[:2]
                  # later: provide list and randomly choose index for resize
                  _A     :	int      =					np.random.randint(self.short_edge_length[0]      ,							self.short_edge_length[1] + 1)
                  if size == 0:
                         return img
                  _A     :	Dict      =					size * 1.0 / min(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                  if h < w:
                         _A  , _A     :	Dict      =					size, scale * w
                  else:
                         _A  , _A     :	Optional[Any]      =					scale * h, size
                  if max(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE) > self.max_size:
                         _A     :	List[Any]      =					self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	Dict      =					newh * scale
                         _A     :	Tuple      =					neww * scale
                  _A     :	List[Any]      =					int(neww + 0.5)
                  _A     :	int      =					int(newh + 0.5)

                  if img.dtype == np.uinta:
                         _A     :	List[Any]      =					Image.fromarray(SCREAMING_SNAKE_CASE)
                         _A     :	List[str]      =					pil_image.resize((neww, newh)      ,							PILImageResampling.BILINEAR)
                         _A     :	Dict      =					np.asarray(SCREAMING_SNAKE_CASE)
                  else:
                         _A     :	List[str]      =					img.permute(2      ,							0      ,							1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                         _A     :	int      =					nn.functional.interpolate(
                             SCREAMING_SNAKE_CASE      ,							(newh, neww)      ,							mode=self.interp_method      ,							align_corners=SCREAMING_SNAKE_CASE).squeeze(0)
                  img_augs.append(SCREAMING_SNAKE_CASE)

           return img_augs




class  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							int):
           _A     :	Dict      =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,							cfg.INPUT.MAX_SIZE_TEST)
           _A     :	Dict      =					cfg.INPUT.FORMAT
           _A     :	Union[str, Any]      =					cfg.SIZE_DIVISIBILITY
           _A     :	Optional[int]      =					cfg.PAD_VALUE
           _A     :	str      =					cfg.INPUT.MAX_SIZE_TEST
           _A     :	List[str]      =					cfg.MODEL.DEVICE
           _A     :	int      =					torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)      ,							1      ,							1)
           _A     :	int      =					torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)      ,							1      ,							1)
           _A     :	Tuple      =					lambda SCREAMING_SNAKE_CASE: (x - self.pixel_mean) / self.pixel_std


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Any      =					tuple(max(SCREAMING_SNAKE_CASE) for s in zip(*[img.shape for img in images]))
           _A     :	str      =					[im.shape[-2:] for im in images]
           _A     :	int      =					[
               nn.functional.pad(
                   SCREAMING_SNAKE_CASE      ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,							value=self.pad_value      ,							)
               for size, im in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           ]

           return torch.stack(SCREAMING_SNAKE_CASE), torch.tensor(SCREAMING_SNAKE_CASE)


    def __call__(  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any=False):
           with torch.no_grad():
                  if not isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Dict      =					[images]
                  if single_image:
                         assert len(SCREAMING_SNAKE_CASE) == 1
                  for i in range(len(SCREAMING_SNAKE_CASE)):
                         if isinstance(images[i]      ,							torch.Tensor):
                                images.insert(SCREAMING_SNAKE_CASE      ,							images.pop(SCREAMING_SNAKE_CASE).to(self.device).float())
                         elif not isinstance(images[i]      ,							torch.Tensor):
                                images.insert(
                                    SCREAMING_SNAKE_CASE      ,							torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE)      ,							input_format=self.input_format))
                                    .to(self.device)
                                    .float()      ,							)
            # resize smallest edge
                  _A     :	List[Any]      =					torch.tensor([im.shape[:2] for im in images])
                  _A     :	Optional[Any]      =					self.aug(SCREAMING_SNAKE_CASE)
                  # transpose images and convert to torch tensors
                  # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                  # now normalize before pad to avoid useless arithmetic
                  _A     :	List[str]      =					[self.normalizer(SCREAMING_SNAKE_CASE) for x in images]
                  # now pad them to do the following operations
                  _A  , _A     :	str      =					self.pad(SCREAMING_SNAKE_CASE)
                  # Normalize

                  if self.size_divisibility > 0:
                         raise NotImplementedError()
                  # pad
                  _A     :	Optional[int]      =					torch.true_divide(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                  if single_image:
                         return images[0], sizes[0], scales_yx[0]
                  else:
                         return images, sizes, scales_yx






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			Union[str, Any]   ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes






def       lowerCAmelCase__       (				lowerCamelCase     :			Any  ,lowerCamelCase     :			Tuple[int, int]   ):
       assert torch.isfinite(lowerCamelCase   ).all(), "Box tensor contains infinite or NaN!"
       _A  , _A     :	Union[str, Any]      =					box_size
       tensor[:, 0].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 1].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 2].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 3].clamp_(min=0  ,max=lowerCamelCase   )






