
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__     :		Optional[Any]        = logging.get_logger(__name__)  # pylint: disable=invalid-name

def 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






  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 ):
    lowercase__	: List[str]     		=   [image]

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

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

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  if isinstance(lowercase_			,			torch.Tensor ):
    return mask
  elif isinstance(lowercase_			,			PIL.Image.Image ):
    lowercase__	: int     		=   [mask]

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



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  UNetaDModel
 __lowerCAmelCase	:  RePaintScheduler


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''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_ = 2_50       ,	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       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   image

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

   lowercase__	: List[Any]     		=   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.')

   lowercase__	: Dict     		=   original_image.shape
   lowercase__	: List[str]     		=   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)
   lowercase__	: Dict     		=   eta

   lowercase__	: str     		=   self.scheduler.timesteps[0] + 1
   lowercase__	: List[str]     		=   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
       lowercase__	: List[Any]     		=   self.unet(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).sample
       # compute previous image: x_t -> x_t-1
       lowercase__	: Optional[Any]     		=   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
       lowercase__	: Optional[int]     		=   self.scheduler.undo_step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   t

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

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_)







'''simple docstring'''


import requests

__snake_case						  =		"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""

def   A_      (       SCREAMING_SNAKE_CASE_						)			->None:
  # fetching a list of articles in json format
  lowercase_     =       requests.get(_NEWS_API + bbc_news_api_key						).json()
  # each article in the list is a dict
  for i, article in enumerate(bbc_news_page["""articles"""] ,	1						):
    print(f"""{i}.) {article["title"]}"""						)


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")

