




"""simple docstring"""




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

import requests



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



def 					a__				(       snake_case__       )		->      dict:
			lowerCamelCase       =      {
			    """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)""",
			}
			lowerCamelCase       =      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
			lowerCamelCase       =      [
			    get_openlibrary_data(author["""key"""]       )["""name"""] for author in data["""Authors"""]
			]
			lowerCamelCase       =      data["""First sentence"""]["""value"""]
			for key, value in data.items():
						if isinstance(snake_case__ ,				snake_case__       ):
									lowerCamelCase       =      """, """.join(snake_case__       )
			return data


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		while True:
				lowerCAmelCase							:    Union[str, Any]  							=							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:
						lowerCAmelCase							:    str  							=							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 unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      UNetaDModel(
							    block_out_channels=(32, 64)	,				layers_per_block=2	,				sample_size=32	,				in_channels=3	,				out_channels=3	,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,				)
							return model





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.dummy_uncond_unet
							lowerCamelCase       =      ScoreSdeVeScheduler()

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a	,				return_dict=_a				)[
							    0
							]

							lowerCamelCase       =      image[0, -3:, -3:, -1]
							lowerCamelCase       =      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCamelCase       =      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






@slow
@require_torch
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """google/ncsnpp-church-256"""
							lowerCamelCase       =      UNetaDModel.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVeScheduler.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=10	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							lowerCamelCase       =      np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2



