






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 			SCREAMING_SNAKE_CASE      (      nn.Module       ):
     '''simple docstring'''


     def __init__(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Union[str, Any] = 16       ,			UpperCAmelCase_							:						List[Any] = 88       ,			UpperCAmelCase_							:						Tuple = None       ,			UpperCAmelCase_							:						str = 1       ,			UpperCAmelCase_							:						Any = 0.0       ,			UpperCAmelCase_							:						Tuple = 32       ,			UpperCAmelCase_							:						Union[str, Any] = None       ,			UpperCAmelCase_							:						Optional[int] = False       ,			UpperCAmelCase_							:						Dict = None       ,			UpperCAmelCase_							:						int = None       ,			UpperCAmelCase_							:						Optional[int] = "geglu"       ,			UpperCAmelCase_							:						Tuple = None       ,			):
       super().__init__()
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     nn.ModuleList(
           [
               TransformeraDModel(
                   num_attention_heads=__SCREAMING_SNAKE_CASE       ,			attention_head_dim=__SCREAMING_SNAKE_CASE       ,			in_channels=__SCREAMING_SNAKE_CASE       ,			num_layers=__SCREAMING_SNAKE_CASE       ,			dropout=__SCREAMING_SNAKE_CASE       ,			norm_num_groups=__SCREAMING_SNAKE_CASE       ,			cross_attention_dim=__SCREAMING_SNAKE_CASE       ,			attention_bias=__SCREAMING_SNAKE_CASE       ,			sample_size=__SCREAMING_SNAKE_CASE       ,			num_vector_embeds=__SCREAMING_SNAKE_CASE       ,			activation_fn=__SCREAMING_SNAKE_CASE       ,			num_embeds_ada_norm=__SCREAMING_SNAKE_CASE       ,			)
               for _ in range(2   )
           ]   )

       # Variables that can be set by a pipeline:

       # The ratio of transformer1 to transformer2's output states to be combined during inference
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     0.5

       # The shape of `encoder_hidden_states` is expected to be
       # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
       SCREAMING_SNAKE_CASE							:   Dict						    =     [77, 257]

       # Which transformer to use to encode which condition.
       # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
       SCREAMING_SNAKE_CASE							:   Dict						    =     [1, 0]






     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[int]=None       ,			UpperCAmelCase_							:						Optional[int]=None       ,			UpperCAmelCase_							:						List[Any]=None       ,			UpperCAmelCase_							:						Optional[Any] = True       ,			):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     hidden_states

       SCREAMING_SNAKE_CASE							:   str						    =     []
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     0
       # attention_mask is not used yet
       for i in range(2   ):
         # for each of the two transformers, pass the corresponding condition tokens
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
         SCREAMING_SNAKE_CASE							:   Tuple						    =     self.transformer_index_for_condition[i]
         SCREAMING_SNAKE_CASE							:   int						    =     self.transformers[transformer_index](
             __SCREAMING_SNAKE_CASE       ,			encoder_hidden_states=__SCREAMING_SNAKE_CASE       ,			timestep=__SCREAMING_SNAKE_CASE       ,			cross_attention_kwargs=__SCREAMING_SNAKE_CASE       ,			return_dict=__SCREAMING_SNAKE_CASE       ,			)[0]
         encoded_states.append(encoded_state - input_states   )
         tokens_start += self.condition_lengths[i]

       SCREAMING_SNAKE_CASE							:   str						    =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
       SCREAMING_SNAKE_CASE							:   int						    =     output_states + input_states

       if not return_dict:
         return (output_states,)

       return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE   )












'''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


UpperCAmelCase_							:	Tuple	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







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




     '''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						):
          __snake_case            =	[image]

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

          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_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						):
          __snake_case            =	[mask]

     if isinstance(mask[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	mask[0].size
          __snake_case       ,     __snake_case            =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          __snake_case            =	[np.array(m.convert('''L'''						).resize((w, h) ,						resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	mask.astype(np.floataa						) / 255.0
          __snake_case            =	0
          __snake_case            =	1
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(mask[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return mask







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




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



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

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

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

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

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






