





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase__  :     Union[str, Any]   			=logging.get_logger(__name__)  # pylint: disable=invalid-name

class  __lowercase       (  _UpperCAmelCase      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								super().__init__()
								self.register_modules(unet=lowercase_				,	scheduler=lowercase_							)


				@torch.no_grad()
				def __call__(						self				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 100				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	):

								if audio_length_in_s is None:
												lowerCamelCase			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase			=2 ** len(self.unet.up_blocks							)
								if sample_size < 3 * down_scale_factor:
												raise ValueError(
												    f"""{audio_length_in_s} is too small. Make sure it\'s bigger or equal to"""
												    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

								lowerCamelCase			=int(lowercase_							)
								if sample_size % down_scale_factor != 0:
												lowerCamelCase			=(
												    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
												) * down_scale_factor
												logger.info(
												    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
												    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
												    """ process."""							)
								lowerCamelCase			=int(lowercase_							)

								lowerCamelCase			=next(iter(self.unet.parameters()							)							).dtype
								lowerCamelCase			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowercase_				,	lowercase_							) and len(lowercase_							) != batch_size:
												raise ValueError(
												    f"""You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"""
												    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

								lowerCamelCase			=randn_tensor(lowercase_				,	generator=lowercase_				,	device=self.device				,	dtype=lowercase_							)

								# set step values
								self.scheduler.set_timesteps(lowercase_				,	device=audio.device							)
								lowerCamelCase			=self.scheduler.timesteps.to(lowercase_							)

								for t in self.progress_bar(self.scheduler.timesteps							):
												# 1. predict noise model_output
												lowerCamelCase			=self.unet(lowercase_				,	lowercase_							).sample

												# 2. compute previous image: x_t -> t_t-1
												lowerCamelCase			=self.scheduler.step(lowercase_				,	lowercase_				,	lowercase_							).prev_sample

								lowerCamelCase			=audio.clamp(-1				,	1							).float().cpu().numpy()

								lowerCamelCase			=audio[:, :, :original_sample_size]

								if not return_dict:
												return (audio,)

								return AudioPipelineOutput(audios=lowercase_							)












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

import requests





def        _lowercase   (  _UpperCAmelCase = "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(_UpperCAmelCase			)
				return requests.get(F"""https://openlibrary.org/{new_olid}.json"""			).json()





def        _lowercase   (  _UpperCAmelCase			)  ->					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(_UpperCAmelCase     , _UpperCAmelCase			):
												lowerCamelCase			=""", """.join(_UpperCAmelCase			)
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase__  :     List[str]   			=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__  :     Dict   			=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}.")





