



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			  =			logging.get_logger(__name__)  # pylint: disable=invalid-name
def 						UpperCamelCase      (  snake_case__			:       Union[List, PIL.Image.Image, torch.Tensor]      )      ->	Tuple:
   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      ):
      UpperCamelCase       :    Optional[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       :    List[str]		      =      [np.array(i.resize((w, h)			,	resample=PIL_INTERPOLATION['lanczos']      )      )[None, :] for i in image]
      UpperCamelCase       :    List[Any]		      =      np.concatenate(_lowerCamelCase			,	axis=0      )
      UpperCamelCase       :    Any		      =      np.array(_lowerCamelCase      ).astype(np.floataa      ) / 255.0
      UpperCamelCase       :    Union[str, Any]		      =      image.transpose(0			,	3			,	1			,	2      )
      UpperCamelCase       :    List[Any]		      =      2.0 * image - 1.0
      UpperCamelCase       :    Optional[int]		      =      torch.from_numpy(_lowerCamelCase      )
   elif isinstance(image[0]			,	torch.Tensor      ):
      UpperCamelCase       :    Optional[Any]		      =      torch.cat(_lowerCamelCase			,	dim=0      )
   return image
def 						UpperCamelCase      (  snake_case__			:       Union[List, PIL.Image.Image, torch.Tensor]      )      ->	Optional[int]:
   if isinstance(_lowerCamelCase			,	torch.Tensor      ):
      return mask
   elif isinstance(_lowerCamelCase			,	PIL.Image.Image      ):
      UpperCamelCase       :    Optional[int]		      =      [mask]

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



class 							lowerCAmelCase_						(    UpperCamelCase__				):
       UpperCAmelCase__							:   str  	=    42
       UpperCAmelCase__							:   Tuple  	=    42
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[Any]:
          super().__init__()
          self.register_modules(unet=lowercase_,     scheduler=lowercase_			)






       @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]:
          UpperCamelCase       :    int		      =      image

          UpperCamelCase       :    Union[str, Any]		      =      _preprocess_image(lowercase_			)
          UpperCamelCase       :    List[str]		      =      original_image.to(device=self.device,     dtype=self.unet.dtype			)
          UpperCamelCase       :    List[Any]		      =      _preprocess_mask(lowercase_			)
          UpperCamelCase       :    Optional[int]		      =      mask_image.to(device=self.device,     dtype=self.unet.dtype			)

          UpperCamelCase       :    List[str]		      =      original_image.shape[0]

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

          UpperCamelCase       :    List[Any]		      =      original_image.shape
          UpperCamelCase       :    Dict		      =      randn_tensor(lowercase_,     generator=lowercase_,     device=self.device,     dtype=self.unet.dtype			)

          # set step values
          self.scheduler.set_timesteps(lowercase_,     lowercase_,     lowercase_,     self.device			)
          UpperCamelCase       :    Union[str, Any]		      =      eta

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

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

          UpperCamelCase       :    Dict		      =      (image / 2 + 0.5).clamp(0,     1			)
          UpperCamelCase       :    Optional[int]		      =      image.cpu().permute(0,     2,     3,     1			).numpy()
          if output_type == "pil":
             UpperCamelCase       :    Any		      =      self.numpy_to_pil(lowercase_			)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=lowercase_			)










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     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__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



