



'''simple docstring'''



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	:		Union[str, Any]				=   logging.get_logger(__name__)  # pylint: disable=invalid-name



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[List, PIL.Image.Image, torch.Tensor]					)     ->   Tuple:






	'''simple docstring'''




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

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

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



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[List, PIL.Image.Image, torch.Tensor]					)     ->   Optional[Any]:






	'''simple docstring'''




	if isinstance(lowerCamelCase				,		torch.Tensor					):
		return mask
	elif isinstance(lowerCamelCase				,		PIL.Image.Image					):
		__lowerCAmelCase												=  [mask]

	if isinstance(mask[0]				,		PIL.Image.Image					):
		__lowerCAmelCase							,       __lowerCAmelCase												=  mask[0].size
		__lowerCAmelCase							,       __lowerCAmelCase												=  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		__lowerCAmelCase												=  [np.array(m.convert("L"					).resize((w, h)				,		resample=PIL_INTERPOLATION["nearest"]					)					)[None, :] for m in mask]
		__lowerCAmelCase												=  np.concatenate(lowerCamelCase				,		axis=0					)
		__lowerCAmelCase												=  mask.astype(np.floataa					) / 2_55.0
		__lowerCAmelCase												=  0
		__lowerCAmelCase												=  1
		__lowerCAmelCase												=  torch.from_numpy(lowerCamelCase					)
	elif isinstance(mask[0]				,		torch.Tensor					):
		__lowerCAmelCase												=  torch.cat(lowerCamelCase				,		dim=0					)
	return mask






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     UNetaDModel
					a      :     RePaintScheduler



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[str]:
						super().__init__()
						self.register_modules(unet=UpperCamelCase			,				scheduler=UpperCamelCase     )



					@torch.no_grad()
					def __call__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = 250			,				UpperCamelCase = 0.0			,				UpperCamelCase = 10			,				UpperCamelCase = 10			,				UpperCamelCase = None			,				UpperCamelCase = "pil"			,				UpperCamelCase = True			,				)     ->			Union[ImagePipelineOutput, Tuple]:
						__lowerCAmelCase												=  image

						__lowerCAmelCase												=  _preprocess_image(UpperCamelCase     )
						__lowerCAmelCase												=  original_image.to(device=self.device			,				dtype=self.unet.dtype     )
						__lowerCAmelCase												=  _preprocess_mask(UpperCamelCase     )
						__lowerCAmelCase												=  mask_image.to(device=self.device			,				dtype=self.unet.dtype     )

						__lowerCAmelCase												=  original_image.shape[0]

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

						__lowerCAmelCase												=  original_image.shape
						__lowerCAmelCase												=  randn_tensor(UpperCamelCase			,				generator=UpperCamelCase			,				device=self.device			,				dtype=self.unet.dtype     )

						# set step values
						self.scheduler.set_timesteps(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				self.device     )
						__lowerCAmelCase												=  eta

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

							else:
								# compute the reverse: x_t-1 -> x_t
								__lowerCAmelCase												=  self.scheduler.undo_step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
							__lowerCAmelCase												=  t

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

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=UpperCamelCase     )




'''simple docstring'''



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):



					@register_to_config
					def __init__(    self			,				UpperCamelCase = 16			,				UpperCamelCase = 88			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = 1			,				UpperCamelCase = 0.0			,				UpperCamelCase = 32			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = "geglu"			,				UpperCamelCase = True			,				UpperCamelCase = True			,				)     ->			List[str]:
						super().__init__()
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  attention_head_dim
						__lowerCAmelCase												=  num_attention_heads * attention_head_dim

						__lowerCAmelCase												=  in_channels

						__lowerCAmelCase												=  torch.nn.GroupNorm(num_groups=UpperCamelCase			,				num_channels=UpperCamelCase			,				eps=1E-6			,				affine=UpperCamelCase     )
						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )

						# 3. Define transformers blocks
						__lowerCAmelCase												=  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				dropout=UpperCamelCase			,				cross_attention_dim=UpperCamelCase			,				activation_fn=UpperCamelCase			,				attention_bias=UpperCamelCase			,				double_self_attention=UpperCamelCase			,				norm_elementwise_affine=UpperCamelCase			,				)
						        for d in range(UpperCamelCase     )
						    ]     )

						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=1			,				UpperCamelCase=None			,				UpperCamelCase = True			,				)     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  hidden_states.shape
						__lowerCAmelCase												=  batch_frames // num_frames

						__lowerCAmelCase												=  hidden_states

						__lowerCAmelCase												=  hidden_states[None, :].reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				2			,				1			,				3			,				4     )

						__lowerCAmelCase												=  self.norm(UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				3			,				4			,				2			,				1     ).reshape(batch_size * height * width			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  self.proj_in(UpperCamelCase     )

						# 2. Blocks
						for block in self.transformer_blocks:
							__lowerCAmelCase												=  block(
							    UpperCamelCase			,				encoder_hidden_states=UpperCamelCase			,				timestep=UpperCamelCase			,				cross_attention_kwargs=UpperCamelCase			,				class_labels=UpperCamelCase			,				)

						# 3. Output
						__lowerCAmelCase												=  self.proj_out(UpperCamelCase     )
						__lowerCAmelCase												=  (
						    hidden_states[None, None, :]
						    .reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						    .permute(0			,				3			,				4			,				1			,				2     )
						    .contiguous()
						)
						__lowerCAmelCase												=  hidden_states.reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=UpperCamelCase     )