



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  _snake_case	(    a_):




				def __init__(  self    :  List[str],    __lowercase    :  AutoencoderKL,    __lowercase    :  CLIPTextModel,    __lowercase    :  CLIPTokenizer,    __lowercase    :  UNetaDConditionModel,    __lowercase    :  DDIMScheduler,    __lowercase    :  float = 0.93,    __lowercase    :  bool = True,    ):
							lowercase__         = mixing_coeff
							lowercase__         = leapfrog_steps

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

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




				def 							A__					(  self    :  Any,    __lowercase    :  str,    __lowercase    :  Optional[str] = None,    __lowercase    :  bool = False  ):
							lowercase__         = self.tokenizer(
							    __lowercase,    padding="max_length",    max_length=self.tokenizer.model_max_length,    truncation=__lowercase,    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(
										    __lowercase,    padding="max_length",    max_length=self.tokenizer.model_max_length,    truncation=__lowercase,    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 							A__					(  self    :  Tuple,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor  ):
							lowercase__         = self.mixing_coeff * x + (1 - self.mixing_coeff) * y
							lowercase__         = self.mixing_coeff * y + (1 - self.mixing_coeff) * x

							return [x, y]




				def 							A__					(  self    :  Dict,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor  ):
							lowercase__         = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
							lowercase__         = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

							return [x, y]




				def 							A__					(  self    :  Tuple,    __lowercase    :  torch.Tensor  ):
							# as self.alphas_cumprod is always in cpu
							lowercase__         = int(__lowercase  )

							lowercase__         = 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],    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    ):
							lowercase__         = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							lowercase__         = self._get_alpha_and_beta(__lowercase  )
							lowercase__         = self._get_alpha_and_beta(__lowercase  )

							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 							A__					(  self    :  int,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    ):
							lowercase__         = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							lowercase__         = self._get_alpha_and_beta(__lowercase  )
							lowercase__         = self._get_alpha_and_beta(__lowercase  )

							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 							A__					(  self    :  Union[str, Any],    __lowercase    :  torch.Tensor  ):
							lowercase__         = 1 / self.vae.config.scaling_factor * latents
							lowercase__         = self.vae.decode(__lowercase  ).sample
							lowercase__         = (image / 2 + 0.5).clamp(0,    1  )
							return image




				@torch.no_grad()
				def 							A__					(  self    :  Tuple,    __lowercase    :  Image.Image,    __lowercase    :  torch.Tensor,    __lowercase    :  torch.Tensor,    __lowercase    :  float,    __lowercase    :  Optional[torch.Generator] = None,    ):
							lowercase__         = guidance_scale > 1.0

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

							lowercase__         = self.vae.config.scaling_factor * latent

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

							for i, t in tqdm(enumerate(__lowercase  ),    total=len(__lowercase  )  ):
										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__         = 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(__lowercase,    __lowercase,    encoder_hidden_states=__lowercase  ).sample

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

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

													lowercase__         = model_input

							return coupled_latents




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

							lowercase__         = self.image_processor.preprocess(__lowercase  )

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

							self.scheduler.set_timesteps(__lowercase,    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(__lowercase,    __lowercase,    __lowercase,    __lowercase,    __lowercase  )

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

													if self.leapfrog_steps:
																if i % 2 == 1:
																			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(__lowercase,    __lowercase,    encoder_hidden_states=__lowercase  ).sample

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

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

													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",    __lowercase,    standard_warn=__lowercase  )
										lowercase__         = 'np'

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

							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   )






