


"""simple docstring"""



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"""



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

              self.set_sigmas(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if self.timesteps is None:
                     self.set_timesteps(snake_case__  ,     snake_case__				)

              lowercase		:Union[str, Any]       							=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
              lowercase		:str       							=				torch.exp(torch.linspace(math.log(snake_case__				)  ,     math.log(snake_case__				)  ,     snake_case__				)				)
              lowercase		:List[str]       							=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






              return torch.where(
                  timesteps == 0  ,     torch.zeros_like(t.to(timesteps.device				)				)  ,     self.discrete_sigmas[timesteps - 1].to(timesteps.device				)  ,     )






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






              if self.timesteps is None:
                     raise ValueError(
                         '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''				)

              lowercase		:Optional[int]       							=				timestep * torch.ones(
                  sample.shape[0]  ,     device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
              lowercase		:Optional[int]       							=				(timestep * (len(self.timesteps				) - 1)).long()

              # mps requires indices to be in the same device, so we use cpu as is the default with cuda
              lowercase		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

              lowercase		:List[Any]       							=				self.discrete_sigmas[timesteps].to(sample.device				)
              lowercase		:str       							=				self.get_adjacent_sigma(snake_case__  ,     snake_case__				).to(sample.device				)
              lowercase		:Optional[Any]       							=				torch.zeros_like(snake_case__				)
              lowercase		:List[Any]       							=				(sigma**2 - adjacent_sigma**2) ** 0.5

              # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
              # also equation 47 shows the analog from SDE models to ancestral sampling methods
              lowercase		:Union[str, Any]       							=				diffusion.flatten()
              while len(diffusion.shape				) < len(sample.shape				):
                     lowercase		:Dict       							=				diffusion.unsqueeze(-1				)
              lowercase		:List[Any]       							=				drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              lowercase		:List[Any]       							=				randn_tensor(
                  sample.shape  ,     layout=sample.layout  ,     generator=snake_case__  ,     device=sample.device  ,     dtype=sample.dtype				)
              lowercase		:int       							=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              lowercase		:Any       							=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

              if not return_dict:
                     return (prev_sample, prev_sample_mean)

              return SdeVeOutput(prev_sample=snake_case__  ,     prev_sample_mean=snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






              if self.timesteps is None:
                     raise ValueError(
                         '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''				)

              # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
              # sample noise for correction
              lowercase		:Tuple       							=				randn_tensor(sample.shape  ,     layout=sample.layout  ,     generator=snake_case__				).to(sample.device				)

              # compute step size from the model_output, the noise, and the snr
              lowercase		:List[Any]       							=				torch.norm(model_output.reshape(model_output.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:List[str]       							=				torch.norm(noise.reshape(noise.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:int       							=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              lowercase		:Optional[Any]       							=				step_size * torch.ones(sample.shape[0]				).to(sample.device				)
              # self.repeat_scalar(step_size, sample.shape[0])

              # compute corrected sample: model_output term and noise term
              lowercase		:Union[str, Any]       							=				step_size.flatten()
              while len(step_size.shape				) < len(sample.shape				):
                     lowercase		:Union[str, Any]       							=				step_size.unsqueeze(-1				)
              lowercase		:Union[str, Any]       							=				sample + step_size * model_output
              lowercase		:Any       							=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				timesteps.to(original_samples.device				)
              lowercase		:str       							=				self.discrete_sigmas.to(original_samples.device				)[timesteps]
              lowercase		:Optional[int]       							=				(
                  noise * sigmas[:, None, None, None]
                  if noise is not None
                  else torch.randn_like(snake_case__				) * sigmas[:, None, None, None]
              )
              lowercase		:str       							=				noise + original_samples
              return noisy_samples







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

