


from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests






def       lowerCamelCase					(a_						:str = "isbn/0140328726")     ->							dict:
       lowercase		:List[Any]       							=				olid.strip().strip('''/''')  # Remove leading/trailing whitespace & slashes
       if new_olid.count('''/''') != 1:
              lowercase		:Optional[int]       							=				F"""{olid} is not a valid Open Library olid"""
              raise ValueError(a_)
       return requests.get(F"""https://openlibrary.org/{new_olid}.json""").json()






def       lowerCamelCase					(a_						:dict)     ->							dict:
       lowercase		:str       							=				{
           '''title''': '''Title''',
           '''publish_date''': '''Publish date''',
           '''authors''': '''Authors''',
           '''number_of_pages''': '''Number of pages:''',
           '''first_sentence''': '''First sentence''',
           '''isbn_10''': '''ISBN (10)''',
           '''isbn_13''': '''ISBN (13)''',
       }
       lowercase		:str       							=				{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
       lowercase		:Dict       							=				[
           get_openlibrary_data(author['''key'''])['''name'''] for author in data['''Authors''']
       ]
       lowercase		:Optional[Any]       							=				data['''First sentence''']['''value''']
       for key, value in data.items():
              if isinstance(a_				,				a_):
                     lowercase		:int       							=				''', '''.join(a_)
       return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  UpperCAmelCase      		=			input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
  if isbn.lower() in ("", "q", "quit", "exit", "stop"):
   break

  if len(isbn) not in (10, 13) or not isbn.isdigit():
   print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
   continue

  print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

  try:
   UpperCAmelCase      		=			summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
   print('''\n'''.join(F"""{key}: {value}""" for key, value in book_summary.items()))
  except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
   print(F"""Sorry, there are no results for ISBN: {isbn}.""")





"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''simple docstring'''






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







       @torch.no_grad()
       def __call__(   self  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

