
"""simple docstring"""


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    (DDPMParallelScheduler,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,**A_     :      int      )     ->   Dict:
							A						   =   {
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**A_      )
							return config


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[str]:
							for timesteps in [1, 5, 100, 1000]:
								self.check_over_configs(num_train_timesteps=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,[0.0_02, 0.02, 0.2, 2]      ):
								self.check_over_configs(beta_start=A_  ,beta_end=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[Any]:
							for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							self.check_over_configs(thresholding=A_      )
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(
									    thresholding=A_  ,prediction_type=A_  ,sample_max_value=A_  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[Any]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(prediction_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[str]:
							for t in [0, 500, 999]:
								self.check_over_forward(time_step=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_09_79      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   str:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   len(A_      )

							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							A						   =   self.dummy_sample_deter + 0.1
							A						   =   self.dummy_sample_deter - 0.1

							A						   =   samplea.shape[0]
							A						   =   torch.stack([samplea, samplea, samplea]  ,dim=0      )
							A						   =   torch.arange(A_      )[0:3, None].repeat(1  ,A_      )

							A						   =   model(samples.flatten(0  ,1      )  ,timesteps.flatten(0  ,1      )      )
							A						   =   scheduler.batch_step_no_noise(A_  ,timesteps.flatten(0  ,1      )  ,samples.flatten(0  ,1      )      )

							A						   =   torch.sum(torch.abs(A_      )      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_sum.item() - 1153.1833      ) < 1e-2
							assert abs(result_mean.item() - 0.50_05      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   len(A_      )

							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							A						   =   torch.manual_seed(0      )

							for t in reversed(range(A_      )      ):
								# 1. predict noise residual
								A						   =   model(A_  ,A_      )

								# 2. predict previous mean of sample x_t-1
								A						   =   scheduler.step(A_  ,A_  ,A_  ,generator=A_      ).prev_sample

								A						   =   pred_prev_sample

							A						   =   torch.sum(torch.abs(A_      )      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_sum.item() - 258.9606      ) < 1e-2
							assert abs(result_mean.item() - 0.33_72      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config(prediction_type='v_prediction'      )
							A						   =   scheduler_class(**A_      )

							A						   =   len(A_      )

							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							A						   =   torch.manual_seed(0      )

							for t in reversed(range(A_      )      ):
								# 1. predict noise residual
								A						   =   model(A_  ,A_      )

								# 2. predict previous mean of sample x_t-1
								A						   =   scheduler.step(A_  ,A_  ,A_  ,generator=A_      ).prev_sample

								A						   =   pred_prev_sample

							A						   =   torch.sum(torch.abs(A_      )      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_sum.item() - 202.0296      ) < 1e-2
							assert abs(result_mean.item() - 0.26_31      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=A_      )

							A						   =   scheduler.timesteps

							for i, timestep in enumerate(A_      ):
								if i == len(A_      ) - 1:
									A						   =   -1
								else:
									A						   =   timesteps[i + 1]

								A						   =   scheduler.previous_timestep(A_      )
								A						   =   prev_t.item()

								self.assertEqual(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [100, 87, 50, 51, 0]

							with self.assertRaises(A_  ,msg='`custom_timesteps` must be in descending order.'      ):
								scheduler.set_timesteps(timesteps=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [100, 87, 50, 1, 0]
							A						   =   len(A_      )

							with self.assertRaises(A_  ,msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'      ):
								scheduler.set_timesteps(num_inference_steps=A_  ,timesteps=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Any:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config()
							A						   =   scheduler_class(**A_      )

							A						   =   [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    A_  ,msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'  ,):
								scheduler.set_timesteps(timesteps=A_      )

"""simple docstring"""


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

import requests






def       _snake_case				(		snake_case__   :     str = "isbn/0140328726"     ):
	A						   =   olid.strip().strip('/'     )  # Remove leading/trailing whitespace & slashes
	if new_olid.count('/'     ) != 1:
		A						   =   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       _snake_case				(		snake_case__   :     dict     ):
	A						   =   {
	    '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)',
	}
	A						   =   {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
	A						   =   [
	    get_openlibrary_data(author['key']     )['name'] for author in data['Authors']
	]
	A						   =   data['First sentence']['value']
	for key, value in data.items():
		if isinstance(snake_case__	,      snake_case__     ):
			A						   =   ', '.join(snake_case__     )
	return data


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		while True:
				_lowercase							   =				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:
						_lowercase							   =				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}.""")