



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		:				Optional[Any]      		=transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	List[str]:







			"""simple docstring"""
			if isinstance(_lowercase						,       torch.Tensor):
						return image
			elif isinstance(_lowercase						,       PIL.Image.Image):
						a__       :						Optional[Any]							=  [image]

			a__       :						int							=  [trans(img.convert("""RGB""")) for img in image]
			a__       :						Optional[Any]							=  torch.stack(_lowercase)
			return image





class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									super().__init__()

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

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  min(int(num_inference_steps * strength  ) ,       __lowercase  )

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

									return timesteps, num_inference_steps - t_start

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase=None  ) ->				Any:
									"""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  )}'''  )

									a__       :						List[str]							=  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.'''  )

									a__       :						Tuple							=  init_latents.shape
									a__       :						Optional[int]							=  randn_tensor(__lowercase ,       generator=__lowercase ,       device=__lowercase ,       dtype=__lowercase  )

									# get latents
									print("""add noise to latents at timestep""" ,       __lowercase  )
									a__       :						List[str]							=  self.scheduler.add_noise(__lowercase ,       __lowercase ,       __lowercase  )
									a__       :						Optional[int]							=  init_latents

									return latents

						@torch.no_grad()
						def __call__(       self ,       __lowercase = None ,       __lowercase = 0.8 ,       __lowercase = 1 ,       __lowercase = None ,       __lowercase = 0.0 ,       __lowercase = 5_0 ,       __lowercase = None ,       __lowercase = "pil" ,       __lowercase = True ,       ) ->				Union[ImagePipelineOutput, Tuple]:
									"""simple docstring"""




									self.check_inputs(__lowercase  )

									# 2. Preprocess image
									a__       :						Union[str, Any]							=  preprocess(__lowercase  )

									# 3. set timesteps
									self.scheduler.set_timesteps(__lowercase ,       device=self.device  )
									a__       :						Tuple							=  self.get_timesteps(__lowercase ,       __lowercase ,       self.device  )
									a__       :						Union[str, Any]							=  timesteps[:1].repeat(__lowercase  )

									# 4. Prepare latent variables
									a__       :						Union[str, Any]							=  self.prepare_latents(__lowercase ,       __lowercase ,       __lowercase ,       self.unet.dtype ,       self.device ,       __lowercase  )
									a__       :						Union[str, Any]							=  latents

									# 5. Denoising loop
									for t in self.progress_bar(__lowercase  ):
												# 1. predict noise model_output
												a__       :						List[Any]							=  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
												a__       :						List[str]							=  self.scheduler.step(
												    __lowercase ,       __lowercase ,       __lowercase ,       eta=__lowercase ,       use_clipped_model_output=__lowercase ,       generator=__lowercase ,       ).prev_sample

									a__       :						List[str]							=  (image / 2 + 0.5).clamp(0 ,       1  )
									a__       :						Tuple							=  image.cpu().permute(0 ,       2 ,       3 ,       1  ).numpy()
									if output_type == "pil":
												a__       :						Union[str, Any]							=  self.numpy_to_pil(__lowercase  )

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

									return ImagePipelineOutput(images=__lowercase  )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_lowercase		:				List[Any]      		={}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				int      		=["BartphoTokenizer"]

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bartpho import BartphoTokenizer

else:
		import sys

		_lowercase		:				Tuple      		=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




