



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





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

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

      SCREAMING_SNAKE_CASE_: Dict     			=[np.array(i.resize((w, h)							,			resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=np.concatenate(lowercase							,			axis=0 )
      SCREAMING_SNAKE_CASE_: Dict     			=np.array(lowercase ).astype(np.floataa ) / 255.0
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image.transpose(0							,			3							,			1							,			2 )
      SCREAMING_SNAKE_CASE_: List[Any]     			=2.0 * image - 1.0
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.from_numpy(lowercase )
   elif isinstance(image[0]							,			torch.Tensor ):
      SCREAMING_SNAKE_CASE_: str     			=torch.cat(lowercase							,			dim=0 )
   return image





def      __magic_name__				(							lowercase ):
   if isinstance(lowercase							,			torch.Tensor ):
      return mask
   elif isinstance(lowercase							,			PIL.Image.Image ):
      SCREAMING_SNAKE_CASE_: Optional[int]     			=[mask]

   if isinstance(mask[0]							,			PIL.Image.Image ):
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=mask[0].size
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      SCREAMING_SNAKE_CASE_: Dict     			=[np.array(m.convert("""L""" ).resize((w, h)							,			resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
      SCREAMING_SNAKE_CASE_: Dict     			=np.concatenate(lowercase							,			axis=0 )
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=mask.astype(np.floataa ) / 255.0
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=0
      SCREAMING_SNAKE_CASE_: int     			=1
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.from_numpy(lowercase )
   elif isinstance(mask[0]							,			torch.Tensor ):
      SCREAMING_SNAKE_CASE_: Dict     			=torch.cat(lowercase							,			dim=0 )
   return mask
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				UNetaDModel
  UpperCamelCase	:				RePaintScheduler



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					List[Any]		)     ->					List[str]:


     '''simple docstring'''




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





  @torch.no_grad()
  def __call__(						self :					str       ,		lowerCAmelCase :					Union[torch.Tensor, PIL.Image.Image]       ,		lowerCAmelCase :					Union[torch.Tensor, PIL.Image.Image]       ,		lowerCAmelCase :					int = 250       ,		lowerCAmelCase :					float = 0.0       ,		lowerCAmelCase :					int = 10       ,		lowerCAmelCase :					int = 10       ,		lowerCAmelCase :					Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		lowerCAmelCase :					Optional[str] = "pil"       ,		lowerCAmelCase :					bool = True       ,		)     ->					Union[ImagePipelineOutput, Tuple]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=image

     SCREAMING_SNAKE_CASE_: int     			=_preprocess_image(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=original_image.to(device=self.device       ,		dtype=self.unet.dtype		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_preprocess_mask(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=mask_image.to(device=self.device       ,		dtype=self.unet.dtype		)

     SCREAMING_SNAKE_CASE_: List[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.'''		)

     SCREAMING_SNAKE_CASE_: Any     			=original_image.shape
     SCREAMING_SNAKE_CASE_: List[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		)
     SCREAMING_SNAKE_CASE_: List[str]     			=eta

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

        else:
           # compute the reverse: x_t-1 -> x_t
           SCREAMING_SNAKE_CASE_: List[Any]     			=self.scheduler.undo_step(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=t

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase		)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   if upper_limit < 0:
      raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=[0] * (upper_limit + 1)

   # Base case: C(0) = C(1) = 1
   SCREAMING_SNAKE_CASE_: Any     			=1
   if upper_limit > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			=1

   # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
   for i in range(2							,			upper_limit + 1 ):
      for j in range(lowercase ):
         catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

   return catalan_list


if __name__ == "__main__":
   print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
   print("""\n*** Enter -1 at any time to quit ***""")
   print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
   try:
      while True:
         _UpperCAmelCase     =			int(input().strip())
         if N < 0:
            print("""\n********* Goodbye!! ************""")
            break
         else:
            print(f"""The Catalan numbers from 0 through {N} are:""")
            print(catalan_numbers(N))
            print("""Try another upper limit for the sequence: """, end="""""")
   except (NameError, ValueError):
      print("""\n********* Invalid input, goodbye! ************\n""")

   import doctest

   doctest.testmod()



