



"""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


SCREAMING_SNAKE_CASE_				= transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		if isinstance(SCREAMING_SNAKE_CASE__, torch.Tensor	):
				return image
		elif isinstance(SCREAMING_SNAKE_CASE__, PIL.Image.Image	):
				a_     :      Optional[Any] 						=     [image]

		a_     :      Union[str, Any] 						=     [trans(img.convert("RGB"	)	) for img in image]
		a_     :      int 						=     torch.stack(SCREAMING_SNAKE_CASE__	)
		return image




class 			snake_case_						(						a_				):






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

									# make sure scheduler can always be converted to DDIM
									a_     :      Optional[Any] 						=     DDIMScheduler.from_config(scheduler.config      )

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






							def snake_case_  (   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 snake_case_  (   self			,     a_			,     a_			,     a_      ):
									# get the original timestep using init_timestep
									a_     :      Tuple 						=     min(int(num_inference_steps * strength      )			,     a_      )

									a_     :      Dict 						=     max(num_inference_steps - init_timestep			,     0      )
									a_     :      Union[str, Any] 						=     self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






							def snake_case_  (   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_      )}"""      )

									a_     :      Optional[Any] 						=     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."""      )

									a_     :      List[Any] 						=     init_latents.shape
									a_     :      str 						=     randn_tensor(a_			,     generator=a_			,     device=a_			,     dtype=a_      )

									# get latents
									print("add noise to latents at timestep"			,     a_      )
									a_     :      Tuple 						=     self.scheduler.add_noise(a_			,     a_			,     a_      )
									a_     :      List[str] 						=     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
									a_     :      Dict 						=     preprocess(a_      )

									# 3. set timesteps
									self.scheduler.set_timesteps(a_			,     device=self.device      )
									a_     :      Optional[int] 						=     self.get_timesteps(a_			,     a_			,     self.device      )
									a_     :      str 						=     timesteps[:1].repeat(a_      )

									# 4. Prepare latent variables
									a_     :      List[Any] 						=     self.prepare_latents(a_			,     a_			,     a_			,     self.unet.dtype			,     self.device			,     a_      )
									a_     :      Dict 						=     latents

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

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

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

									return ImagePipelineOutput(images=a_      )





"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ViltImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."			,     a_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."      )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."      )

									super().__init__(a_			,     a_      )
									a_     :      Dict 						=     self.image_processor






							def __call__(   self			,     a_			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     self.tokenizer(
									    text=a_			,     add_special_tokens=a_			,     padding=a_			,     truncation=a_			,     max_length=a_			,     stride=a_			,     pad_to_multiple_of=a_			,     return_token_type_ids=a_			,     return_attention_mask=a_			,     return_overflowing_tokens=a_			,     return_special_tokens_mask=a_			,     return_offsets_mapping=a_			,     return_length=a_			,     verbose=a_			,     return_tensors=a_			,     **a_			,     )
									# add pixel_values + pixel_mask
									a_     :      List[Any] 						=     self.image_processor(a_			,     return_tensors=a_      )
									encoding.update(a_      )

									return encoding






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.tokenizer.model_input_names
									a_     :      Dict 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor