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



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

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







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__):
												super().__init__()

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

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




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




							def 			A__				(    self       ,A__       ,A__       ,A__):
												# get the original timestep using init_timestep
												lowercase						     =						min(int(num_inference_steps * strength)       ,A__)

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

												return timesteps, num_inference_steps - t_start




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__=None):
												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__)}')

												lowercase						     =						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.')

												lowercase						     =						init_latents.shape
												lowercase						     =						randn_tensor(A__       ,generator=A__       ,device=A__       ,dtype=A__)

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

												return latents





							@torch.no_grad()
							def __call__(    self       ,A__ = None       ,A__ = 0.8       ,A__ = 1       ,A__ = None       ,A__ = 0.0       ,A__ = 5_0       ,A__ = None       ,A__ = "pil"       ,A__ = True       ,):
												self.check_inputs(A__)

												# 2. Preprocess image
												lowercase						     =						preprocess(A__)

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

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

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

												lowercase						     =						(image / 2 + 0.5).clamp(0       ,1)
												lowercase						     =						image.cpu().permute(0       ,2       ,3       ,1).numpy()
												if output_type == "pil":
																	lowercase						     =						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_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__		:str			  =   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str			  =   ["ViTFeatureExtractor"]
							lowercase__		:int			  =   ["ViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:int			  =   [
							    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTForImageClassification",
							    "ViTForMaskedImageModeling",
							    "ViTModel",
							    "ViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Any			  =   [
							    "TFViTForImageClassification",
							    "TFViTModel",
							    "TFViTPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "FlaxViTForImageClassification",
							    "FlaxViTModel",
							    "FlaxViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vit import ViTFeatureExtractor
														from .image_processing_vit import ViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit import (
														    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTForImageClassification,
														    ViTForMaskedImageModeling,
														    ViTModel,
														    ViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
							import sys

							lowercase__		:Dict			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




