





from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class      A_	(SCREAMING_SNAKE_CASE__      ):
	def __init__(   self							, _A							, _A							, _A							, _A							, _A							, ):






				'''simple docstring'''







				super().__init__()

				self.register_modules(vae=_UpperCAmelCase							, text_encoder=_UpperCAmelCase							, tokenizer=_UpperCAmelCase							, unet=_UpperCAmelCase							, scheduler=_UpperCAmelCase		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				with torch.no_grad():
							UpperCAmelCase       		=    self.vae.encode(tfms.ToTensor()(_UpperCAmelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
							UpperCAmelCase       		=    0.1_82_15 * latent.latent_dist.sample()
				return latent
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    (1 / 0.1_82_15) * latent
				with torch.no_grad():
							UpperCAmelCase       		=    self.vae.decode(_UpperCAmelCase		).sample
				UpperCAmelCase       		=    (img / 2 + 0.5).clamp(0							, 1		)
				UpperCAmelCase       		=    img.detach().cpu().permute(0							, 2							, 3							, 1		).numpy()
				UpperCAmelCase       		=    (img * 2_5_5).round().astype('''uint8'''		)
				return Image.fromarray(img[0]		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.tokenizer(
				    _UpperCAmelCase							, padding='''max_length'''							, max_length=self.tokenizer.model_max_length							, truncation=_UpperCAmelCase							, return_tensors='''pt'''							, )

				UpperCAmelCase       		=    self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

				UpperCAmelCase       		=    self.tokenizer(
				    ''''''							, padding='''max_length'''							, max_length=self.tokenizer.model_max_length							, truncation=_UpperCAmelCase							, return_tensors='''pt'''							, )

				UpperCAmelCase       		=    self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

				return torch.cat([uncond_embedding, text_embedding]		)







	def __call__(   self							, _A							, _A							, _A = 0.3							, _A = 0.6							, _A = 0.5							, _A = 4_2							, _A = 5_0							, _A = 7.5							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    steps - int(kmin * steps		)
				UpperCAmelCase       		=    steps - int(kmax * steps		)

				UpperCAmelCase       		=    self.prep_text(_UpperCAmelCase		)

				self.scheduler.set_timesteps(_UpperCAmelCase		)

				UpperCAmelCase       		=    img.size
				UpperCAmelCase       		=    self.encode(_UpperCAmelCase		)

				torch.manual_seed(_UpperCAmelCase		)
				UpperCAmelCase       		=    torch.randn(
				    (1, self.unet.config.in_channels, height // 8, width // 8)							, ).to(self.device		)

				UpperCAmelCase       		=    self.scheduler.add_noise(
				    _UpperCAmelCase							, _UpperCAmelCase							, timesteps=self.scheduler.timesteps[tmax]							, )

				UpperCAmelCase       		=    torch.cat([latents] * 2		)

				UpperCAmelCase       		=    self.scheduler.scale_model_input(_UpperCAmelCase							, self.scheduler.timesteps[tmax]		)

				with torch.no_grad():
							UpperCAmelCase       		=    self.unet(
							    _UpperCAmelCase							, self.scheduler.timesteps[tmax]							, encoder_hidden_states=_UpperCAmelCase							, ).sample

				UpperCAmelCase       		=    pred.chunk(2		)
				UpperCAmelCase       		=    pred_uncond + guidance_scale * (pred_text - pred_uncond)

				UpperCAmelCase       		=    self.scheduler.step(_UpperCAmelCase							, self.scheduler.timesteps[tmax]							, _UpperCAmelCase		).prev_sample

				for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
							if i > tmax:
										if i < tmin:  # layout generation phase
													UpperCAmelCase       		=    self.scheduler.add_noise(
													    _UpperCAmelCase							, _UpperCAmelCase							, timesteps=_UpperCAmelCase							, )

													UpperCAmelCase       		=    (mix_factor * latents) + (
													    1 - mix_factor
													) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
													UpperCAmelCase       		=    torch.cat([input] * 2		)

										else:  # content generation phase
													UpperCAmelCase       		=    torch.cat([latents] * 2		)

										UpperCAmelCase       		=    self.scheduler.scale_model_input(_UpperCAmelCase							, _UpperCAmelCase		)

										with torch.no_grad():
													UpperCAmelCase       		=    self.unet(
													    _UpperCAmelCase							, _UpperCAmelCase							, encoder_hidden_states=_UpperCAmelCase							, ).sample

										UpperCAmelCase       		=    pred.chunk(2		)
										UpperCAmelCase       		=    pred_uncond + guidance_scale * (pred_text - pred_uncond)

										UpperCAmelCase       		=    self.scheduler.step(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase		).prev_sample

				return self.decode(_UpperCAmelCase		)










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.ndarray:







			'''simple docstring'''

			UpperCAmelCase       		=    np.stack((horizontal_flow, vertical_flow)      ,      2			)

			# Create a grid of all pixel coordinates and subtract the flow to get the
			# target pixels coordinates
			UpperCAmelCase       		=    np.stack(
			    np.meshgrid(np.arange(0      ,      image.shape[1]			)      ,      np.arange(0      ,      image.shape[0]			)			)      ,      2			)
			UpperCAmelCase       		=    np.round(grid - flow			).astype(np.intaa			)

			# Find the locations outside of the original image
			UpperCAmelCase       		=    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]			))
			UpperCAmelCase       		=    0

			UpperCAmelCase       		=    image[grid[:, :, 1], grid[:, :, 0]]

			# Set pixels at invalid locations to 0
			UpperCAmelCase       		=    0

			return warped





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = None      ,      )  ->    tuple[np.ndarray, np.ndarray]:







			'''simple docstring'''

			if alpha is None:
						UpperCAmelCase       		=    0.1

			# Initialize flow
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)

			# Prepare kernels for the calculation of the derivatives and the average velocity
			UpperCAmelCase       		=    np.array([[-1, 1], [-1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[-1, -1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[1, 1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array(
			    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]			)

			# Iteratively refine the flow
			for _ in range(UpperCamelCase__			):
						UpperCAmelCase       		=    warp(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      -kernel_t			)

						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)

						# This updates the flow as proposed in the paper (Step 12)
						UpperCAmelCase       		=    (
						    derivative_x * avg_horizontal_velocity
						    + derivative_y * avg_vertical_velocity
						    + derivative_t
						)
						UpperCAmelCase       		=    update / (alpha**2 + derivative_x**2 + derivative_y**2)

						UpperCAmelCase       		=    avg_horizontal_velocity - derivative_x * update
						UpperCAmelCase       		=    avg_vertical_velocity - derivative_y * update

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()



