





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	:	str		     =       logging.get_logger(__name__)  # pylint: disable=invalid-name
def      _A       (		SCREAMING_SNAKE_CASE     :   Union[List, PIL.Image.Image, torch.Tensor]						):





  """simple docstring"""



  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						):
    a__	:					Optional[Any]										=[image]

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

    a__	:					Union[str, Any]										=[np.array(i.resize((w, h)       ,       resample=PIL_INTERPOLATION["lanczos"]						)						)[None, :] for i in image]
    a__	:					Optional[Any]										=np.concatenate(SCREAMING_SNAKE_CASE       ,       axis=0						)
    a__	:					Optional[Any]										=np.array(SCREAMING_SNAKE_CASE						).astype(np.floataa						) / 2_5_5.0
    a__	:					List[Any]										=image.transpose(0       ,       3       ,       1       ,       2						)
    a__	:					Tuple										=2.0 * image - 1.0
    a__	:					Optional[int]										=torch.from_numpy(SCREAMING_SNAKE_CASE						)
  elif isinstance(image[0]       ,       torch.Tensor						):
    a__	:					List[str]										=torch.cat(SCREAMING_SNAKE_CASE       ,       dim=0						)
  return image
def      _A       (		SCREAMING_SNAKE_CASE     :   Union[List, PIL.Image.Image, torch.Tensor]						):





  """simple docstring"""



  if isinstance(SCREAMING_SNAKE_CASE       ,       torch.Tensor						):
    return mask
  elif isinstance(SCREAMING_SNAKE_CASE       ,       PIL.Image.Image						):
    a__	:					Dict										=[mask]

  if isinstance(mask[0]       ,       PIL.Image.Image						):
    a__					,							a__	:					Optional[int]										=mask[0].size
    a__					,							a__	:					Dict										=(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    a__	:					List[str]										=[np.array(m.convert("L"						).resize((w, h)       ,       resample=PIL_INTERPOLATION["nearest"]						)						)[None, :] for m in mask]
    a__	:					int										=np.concatenate(SCREAMING_SNAKE_CASE       ,       axis=0						)
    a__	:					List[Any]										=mask.astype(np.floataa						) / 2_5_5.0
    a__	:					str										=0
    a__	:					int										=1
    a__	:					Union[str, Any]										=torch.from_numpy(SCREAMING_SNAKE_CASE						)
  elif isinstance(mask[0]       ,       torch.Tensor						):
    a__	:					Any										=torch.cat(SCREAMING_SNAKE_CASE       ,       dim=0						)
  return mask







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					UNetaDModel
      _lowercase				:					RePaintScheduler







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



        '''simple docstring'''




        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		,      )   ->						Union[ImagePipelineOutput, Tuple]:



        '''simple docstring'''




        a__	:					List[str]										=image

        a__	:					Tuple										=_preprocess_image(lowerCAmelCase__							)
        a__	:					int										=original_image.to(device=self.device		,      dtype=self.unet.dtype							)
        a__	:					Tuple										=_preprocess_mask(lowerCAmelCase__							)
        a__	:					Optional[int]										=mask_image.to(device=self.device		,      dtype=self.unet.dtype							)

        a__	:					Dict										=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.'''							)

        a__	:					Union[str, Any]										=original_image.shape
        a__	:					Dict										=randn_tensor(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      device=self.device		,      dtype=self.unet.dtype							)

        # set step values
        self.scheduler.set_timesteps(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      self.device							)
        a__	:					int										=eta

        a__	:					Tuple										=self.scheduler.timesteps[0] + 1
        a__	:					Optional[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
            a__	:					str										=self.unet(lowerCAmelCase__		,      lowerCAmelCase__							).sample
            # compute previous image: x_t -> x_t-1
            a__	:					int										=self.scheduler.step(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							).prev_sample

          else:
            # compute the reverse: x_t-1 -> x_t
            a__	:					Any										=self.scheduler.undo_step(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)
          a__	:					List[Any]										=t

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

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase__							)













from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer







class 				__lowerCAmelCase       :







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        raise NotImplementedError()







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					str										=tokenizer
        a__	:					List[str]										=skip_prompt
        a__	:					List[Any]										=decode_kwargs

        # variables used in the streaming process
        a__	:					Dict										=[]
        a__	:					int										=0
        a__	:					str										=True







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        if len(value.shape							) > 1 and value.shape[0] > 1:
          raise ValueError("TextStreamer only supports batch size 1"							)
        elif len(value.shape							) > 1:
          a__	:					Any										=value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
          a__	:					Dict										=False
          return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()							)
        a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)

        # After the symbol for a new line, we flush the cache.
        if text.endswith("\n"							):
          a__	:					List[Any]										=text[self.print_len :]
          a__	:					List[str]										=[]
          a__	:					Optional[int]										=0
        # If the last token is a CJK character, we print the characters.
        elif len(lowerCAmelCase__							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
          a__	:					List[str]										=text[self.print_len :]
          self.print_len += len(lowerCAmelCase__							)
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
          a__	:					str										=text[self.print_len : text.rfind(" "							) + 1]
          self.print_len += len(lowerCAmelCase__							)

        self.on_finalized_text(lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        if len(self.token_cache							) > 0:
          a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)
          a__	:					List[Any]										=text[self.print_len :]
          a__	:					List[str]										=[]
          a__	:					Optional[int]										=0
        else:
          a__	:					Union[str, Any]										=""

        a__	:					Any										=True
        self.on_finalized_text(lowerCAmelCase__		,      stream_end=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						Optional[Any]:



        '''simple docstring'''




        print(lowerCAmelCase__		,      flush=lowerCAmelCase__		,      end="" if not stream_end else None							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        if (
            (cp >= 0X4E_00 and cp <= 0X9F_FF)
            or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
            or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
            or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
            or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
            or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
            or (cp >= 0XF9_00 and cp <= 0XFA_FF)
            or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
        ):  #
          return True

        return False







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					str										=Queue()
        a__	:					Optional[Any]										=None
        a__	:					Any										=timeout







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						List[str]:



        '''simple docstring'''




        self.text_queue.put(lowerCAmelCase__		,      timeout=self.timeout							)
        if stream_end:
          self.text_queue.put(self.stop_signal		,      timeout=self.timeout							)







      def __iter__(    self							)   ->						Dict:



        '''simple docstring'''




        return self







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					int										=self.text_queue.get(timeout=self.timeout							)
        if value == self.stop_signal:
          raise StopIteration()
        else:
          return value






