

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase  :      Optional[Any]			 =logging.get_logger(__name__)  # pylint: disable=invalid-name



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	warnings.warn(
	    "The preprocess method is deprecated and will be removed in a future version. Please"
	    " use VaeImageProcessor.preprocess instead"	,SCREAMING_SNAKE_CASE	,)
	if isinstance(SCREAMING_SNAKE_CASE	,torch.Tensor      ):
		return image
	elif isinstance(SCREAMING_SNAKE_CASE	,PIL.Image.Image      ):
		UpperCAmelCase__:	int									= [image]

	if isinstance(image[0]	,PIL.Image.Image      ):
		UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= image[0].size
		UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

		UpperCAmelCase__:	Union[str, Any]									= [np.array(i.resize((w, h)	,resample=PIL_INTERPOLATION["lanczos"]      )      )[None, :] for i in image]
		UpperCAmelCase__:	Dict									= np.concatenate(SCREAMING_SNAKE_CASE	,axis=0      )
		UpperCAmelCase__:	Any									= np.array(SCREAMING_SNAKE_CASE      ).astype(np.floataa      ) / 2_55.0
		UpperCAmelCase__:	Tuple									= image.transpose(0	,3	,1	,2      )
		UpperCAmelCase__:	Any									= 2.0 * image - 1.0
		UpperCAmelCase__:	Tuple									= torch.from_numpy(SCREAMING_SNAKE_CASE      )
	elif isinstance(image[0]	,torch.Tensor      ):
		UpperCAmelCase__:	Optional[int]									= torch.cat(SCREAMING_SNAKE_CASE	,dim=0      )
	return image



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if isinstance(SCREAMING_SNAKE_CASE	,torch.Tensor      ):
		return mask
	elif isinstance(SCREAMING_SNAKE_CASE	,PIL.Image.Image      ):
		UpperCAmelCase__:	int									= [mask]

	if isinstance(mask[0]	,PIL.Image.Image      ):
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= mask[0].size
		UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
		UpperCAmelCase__:	Optional[Any]									= [np.array(m.convert("L"      ).resize((w, h)	,resample=PIL_INTERPOLATION["nearest"]      )      )[None, :] for m in mask]
		UpperCAmelCase__:	List[Any]									= np.concatenate(SCREAMING_SNAKE_CASE	,axis=0      )
		UpperCAmelCase__:	Dict									= mask.astype(np.floataa      ) / 2_55.0
		UpperCAmelCase__:	Any									= 0
		UpperCAmelCase__:	List[str]									= 1
		UpperCAmelCase__:	Tuple									= torch.from_numpy(SCREAMING_SNAKE_CASE      )
	elif isinstance(mask[0]	,torch.Tensor      ):
		UpperCAmelCase__:	Dict									= torch.cat(SCREAMING_SNAKE_CASE	,dim=0      )
	return mask

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42
					__magic_name__        =   42





					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__						):
						super().__init__()
						self.register_modules(unet=lowerCamelCase__				,      scheduler=lowerCamelCase__						)





					@torch.no_grad()
					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 2_5_0				,      lowerCamelCase__ = 0.0				,      lowerCamelCase__ = 1_0				,      lowerCamelCase__ = 1_0				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "pil"				,      lowerCamelCase__ = True				,      ):
						UpperCAmelCase__:	Tuple									= image

						UpperCAmelCase__:	List[str]									= _preprocess_image(lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= original_image.to(device=self.device				,      dtype=self.unet.dtype						)
						UpperCAmelCase__:	Union[str, Any]									= _preprocess_mask(lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= mask_image.to(device=self.device				,      dtype=self.unet.dtype						)

						UpperCAmelCase__:	Union[str, Any]									= original_image.shape[0]

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

						UpperCAmelCase__:	Tuple									= original_image.shape
						UpperCAmelCase__:	Any									= randn_tensor(lowerCamelCase__				,      generator=lowerCamelCase__				,      device=self.device				,      dtype=self.unet.dtype						)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      self.device						)
						UpperCAmelCase__:	str									= eta

						UpperCAmelCase__:	Optional[Any]									= self.scheduler.timesteps[0] + 1
						UpperCAmelCase__:	List[Any]									= generator[0] if isinstance(lowerCamelCase__				,      lowerCamelCase__						) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							if t < t_last:
								# predict the noise residual
								UpperCAmelCase__:	int									= self.unet(lowerCamelCase__				,      lowerCamelCase__						).sample
								# compute previous image: x_t -> x_t-1
								UpperCAmelCase__:	Tuple									= self.scheduler.step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						).prev_sample

							else:
								# compute the reverse: x_t-1 -> x_t
								UpperCAmelCase__:	int									= self.scheduler.undo_step(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
							UpperCAmelCase__:	List[str]									= t

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

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowerCamelCase__						)


import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= torch.cat([classifier_free_guidance_embeddings, image_embeddings]				,      dim=0						)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						UpperCAmelCase__:	str									= prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings