





"""simple docstring"""




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

import requests





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





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   {
						    """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)""",
						}
						_UpperCamelCase      : Any         =   {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
						_UpperCamelCase      : int         =   [
						    get_openlibrary_data(author['''key''']       )["""name"""] for author in data["""Authors"""]
						]
						_UpperCamelCase      : Union[str, Any]         =   data["""First sentence"""]["""value"""]
						for key, value in data.items():
												if isinstance(lowercase__	,						lowercase__       ):
																		_UpperCamelCase      : int         =   """, """.join(lowercase__       )
						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"""




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_									=0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCamelCase_									=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class 			_a						:






				def __init__(			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   WATERMARK_BITS
										_UpperCamelCase      : Optional[int]         =   WatermarkEncoder()

										self.encoder.set_watermark('''bits''',			self.watermark							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   torch.FloatTensor							)     ->				int:




										'''simple docstring'''



										if images.shape[-1] < 2_5_6:
																return images

										_UpperCamelCase      : Union[str, Any]         =   (2_5_5 * (images / 2 + 0.5)).cpu().permute(0,			2,			3,			1							).float().numpy()

										_UpperCamelCase      : List[str]         =   [self.encoder.encode(lowerCAmelCase__,			'''dwtDct'''							) for image in images]

										_UpperCamelCase      : Dict         =   torch.from_numpy(np.array(lowerCAmelCase__							)							).permute(0,			3,			1,			2							)

										_UpperCamelCase      : Optional[int]         =   torch.clamp(2 * (images / 2_5_5 - 0.5),			min=-1.0,			max=1.0							)
										return images



