
'''simple docstring'''



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowercase          =						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def A (__lowerCamelCase :List[str]						):
						if isinstance(__lowerCamelCase					,      torch.Tensor						):
												return image
						elif isinstance(__lowerCamelCase					,      PIL.Image.Image						):
												_lowerCAmelCase    		=					[image]

						_lowerCAmelCase    		=					[trans(img.convert("""RGB"""						)						) for img in image]
						_lowerCAmelCase    		=					torch.stack(__lowerCamelCase						)
						return image





class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase    		=					DDIMScheduler.from_config(scheduler.config )

												self.register_modules(unet=_lowercase       ,  scheduler=_lowercase )




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												if strength < 0 or strength > 1:
																		raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}' )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					min(int(num_inference_steps * strength )       ,  _lowercase )

												_lowerCAmelCase    		=					max(num_inference_steps - init_timestep       ,  0 )
												_lowerCAmelCase    		=					self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None ):

												"""simple docstring"""



												if not isinstance(_lowercase       ,  (torch.Tensor, PIL.Image.Image, list) ):
																		raise ValueError(
																		    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase )}' )

												_lowerCAmelCase    		=					image.to(device=_lowercase       ,  dtype=_lowercase )

												if isinstance(_lowercase       ,  _lowercase ) and len(_lowercase ) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase )}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )

												_lowerCAmelCase    		=					init_latents.shape
												_lowerCAmelCase    		=					randn_tensor(_lowercase       ,  generator=_lowercase       ,  device=_lowercase       ,  dtype=_lowercase )

												# get latents
												print("""add noise to latents at timestep"""       ,  _lowercase )
												_lowerCAmelCase    		=					self.scheduler.add_noise(_lowercase       ,  _lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					init_latents

												return latents




						@torch.no_grad()
						def __call__(   self       ,  _lowercase = None       ,  _lowercase = 0.8       ,  _lowercase = 1       ,  _lowercase = None       ,  _lowercase = 0.0       ,  _lowercase = 50       ,  _lowercase = None       ,  _lowercase = "pil"       ,  _lowercase = True       ,  ):

												"""simple docstring"""



												self.check_inputs(_lowercase )

												# 2. Preprocess image
												_lowerCAmelCase    		=					preprocess(_lowercase )

												# 3. set timesteps
												self.scheduler.set_timesteps(_lowercase       ,  device=self.device )
												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_timesteps(_lowercase       ,  _lowercase       ,  self.device )
												_lowerCAmelCase    		=					timesteps[:1].repeat(_lowercase )

												# 4. Prepare latent variables
												_lowerCAmelCase    		=					self.prepare_latents(_lowercase       ,  _lowercase       ,  _lowercase       ,  self.unet.dtype       ,  self.device       ,  _lowercase )
												_lowerCAmelCase    		=					latents

												# 5. Denoising loop
												for t in self.progress_bar(_lowercase ):
																		# 1. predict noise model_output
																		_lowerCAmelCase    		=					self.unet(_lowercase       ,  _lowercase ).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase    		=					self.scheduler.step(
																		    _lowercase       ,  _lowercase       ,  _lowercase       ,  eta=_lowercase       ,  use_clipped_model_output=_lowercase       ,  generator=_lowercase       ,  ).prev_sample

												_lowerCAmelCase    		=					(image / 2 + 0.5).clamp(0       ,  1 )
												_lowerCAmelCase    		=					image.cpu().permute(0       ,  2       ,  3       ,  1 ).numpy()
												if output_type == "pil":
																		_lowerCAmelCase    		=					self.numpy_to_pil(_lowercase )

												if not return_dict:
																		return (image, latent_timestep.item())

												return ImagePipelineOutput(images=_lowercase )


'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
