



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


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





def      __snake_case	(			_UpperCAmelCase				):
    warnings.warn(
        '''The preprocess method is deprecated and will be removed in a future version. Please'''
        ''' use VaeImageProcessor.preprocess instead'''   , _UpperCAmelCase   , )
    if isinstance(_UpperCAmelCase   , torch.Tensor				):
        return image
    elif isinstance(_UpperCAmelCase   , PIL.Image.Image				):
        __a						       =				[image]

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

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





def      __snake_case	(			_UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , torch.Tensor				):
        return mask
    elif isinstance(_UpperCAmelCase   , PIL.Image.Image				):
        __a						       =				[mask]

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

class     _A       (	a__    ):
       UpperCamelCase__  :    Optional[int]											=						42
       UpperCamelCase__  :    Tuple											=						42



       def __init__(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

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





       @torch.no_grad()
       def __call__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[str] = 250     ,		__SCREAMING_SNAKE_CASE							:     int = 0.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 10     ,		__SCREAMING_SNAKE_CASE							:     Dict = 10     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     int = "pil"     ,		__SCREAMING_SNAKE_CASE							:     Any = True     ,		):


           '''simple docstring'''

           __a						       =				image

           __a						       =				_preprocess_image(__lowerCAmelCase)
           __a						       =				original_image.to(device=self.device     ,		dtype=self.unet.dtype)
           __a						       =				_preprocess_mask(__lowerCAmelCase)
           __a						       =				mask_image.to(device=self.device     ,		dtype=self.unet.dtype)

           __a						       =				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						       =				original_image.shape
           __a						       =				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						       =				eta

           __a						       =				self.scheduler.timesteps[0] + 1
           __a						       =				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						       =				self.unet(__lowerCAmelCase     ,		__lowerCAmelCase).sample
                   # compute previous image: x_t -> x_t-1
                   __a						       =				self.scheduler.step(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase).prev_sample

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

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

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__lowerCAmelCase)






'''simple docstring'''

import requests

__a: str          =   """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 # fetching a list of articles in json format
 lowercase__				:    Optional[Any]  =					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>""")

