



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       :     Dict					)       ->				str:







			'''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     :						List[Any]				,	A     :						Optional[Any]				,	A     :						int)     ->					Optional[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     :						Any)     ->					List[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     :						List[str]				,	A     :						Any				,	A     :						Tuple				,	A     :						Dict)     ->					Union[str, 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     :						Dict				,	A     :						List[str]				,	A     :						Any				,	A     :						List[Any]				,	A     :						List[str]				,	A     :						str				,	A     :						Any=None)     ->					int:



						"""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     :						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 OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__     = {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CTRLForSequenceClassification",
							    "CTRLLMHeadModel",
							    "CTRLModel",
							    "CTRLPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCTRLForSequenceClassification",
							    "TFCTRLLMHeadModel",
							    "TFCTRLModel",
							    "TFCTRLPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

else:
							import sys

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