



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


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





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			Any:







			'''simple docstring'''
			if isinstance(_UpperCAmelCase							,       torch.Tensor   ):
						return image
			elif isinstance(_UpperCAmelCase							,       PIL.Image.Image   ):
						_UpperCAmelCase			=       [image]

			_UpperCAmelCase			=       [trans(img.convert('RGB'   )   ) for img in image]
			_UpperCAmelCase			=       torch.stack(_UpperCAmelCase   )
			return image







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  Any			,		A       :  Tuple			,		A       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						super().__init__()

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

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



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  Dict)       ->   str:



						"""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    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  int			,		A       :  Union[str, Any]			,		A       :  Any)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       min(int(num_inference_steps * strength)			,		A)

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

						return timesteps, num_inference_steps - t_start



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  Union[str, Any]			,		A       :  Any			,		A       :  Optional[Any]			,		A       :  Optional[Any]			,		A       :  Dict			,		A       :  str=None)       ->   Any:



						"""simple docstring"""






						if not isinstance(A			,		(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(A)}")

						_UpperCAmelCase			=       image.to(device=A			,		dtype=A)

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

						_UpperCAmelCase			=       init_latents.shape
						_UpperCAmelCase			=       randn_tensor(A			,		generator=A			,		device=A			,		dtype=A)

						# get latents
						print('add noise to latents at timestep'			,		A)
						_UpperCAmelCase			=       self.scheduler.add_noise(A			,		A			,		A)
						_UpperCAmelCase			=       init_latents

						return latents



			@torch.no_grad()
			def __call__(						self       :  Union[str, Any]			,		A       :  Union[torch.FloatTensor, PIL.Image.Image] = None			,		A       :  float = 0.8			,		A       :  int = 1			,		A       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		A       :  float = 0.0			,		A       :  int = 50			,		A       :  Optional[bool] = None			,		A       :  Optional[str] = "pil"			,		A       :  bool = True			,		)       ->   Union[ImagePipelineOutput, Tuple]:



						"""simple docstring"""






						self.check_inputs(A)

						# 2. Preprocess image
						_UpperCAmelCase			=       preprocess(A)

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

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

						# 5. Denoising loop
						for t in self.progress_bar(A):
									# 1. predict noise model_output
									_UpperCAmelCase			=       self.unet(A			,		A).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
									_UpperCAmelCase			=       self.scheduler.step(
									    A			,		A			,		A			,		eta=A			,		use_clipped_model_output=A			,		generator=A			,		).prev_sample

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

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

						return ImagePipelineOutput(images=A)





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase__       =	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
							from .tokenization_bertweet import BertweetTokenizer

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
