




import torch
from torch import nn





class 	UpperCAmelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		List[str]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		List[str]=1    ,    _lowerCAmelCase  :		int=False						):
       super().__init__()

       SCREAMING_SNAKE_CASE_     						=      n_token
       SCREAMING_SNAKE_CASE_     						=      d_embed
       SCREAMING_SNAKE_CASE_     						=      d_proj

       SCREAMING_SNAKE_CASE_     						=      cutoffs + [n_token]
       SCREAMING_SNAKE_CASE_     						=      [0] + self.cutoffs
       SCREAMING_SNAKE_CASE_     						=      div_val

       SCREAMING_SNAKE_CASE_     						=      self.cutoffs[0]
       SCREAMING_SNAKE_CASE_     						=      len(self.cutoffs						) - 1
       SCREAMING_SNAKE_CASE_     						=      self.shortlist_size + self.n_clusters

       if self.n_clusters > 0:
             SCREAMING_SNAKE_CASE_     						=      nn.Parameter(torch.zeros(self.n_clusters    ,    self.d_embed						)						)
             SCREAMING_SNAKE_CASE_     						=      nn.Parameter(torch.zeros(self.n_clusters						)						)

       SCREAMING_SNAKE_CASE_     						=      nn.ModuleList()
       SCREAMING_SNAKE_CASE_     						=      nn.ParameterList()

       if div_val == 1:
             for i in range(len(self.cutoffs						)						):
                   if d_proj != d_embed:
                         self.out_projs.append(nn.Parameter(torch.FloatTensor(_lowerCAmelCase    ,    _lowerCAmelCase						)						)						)
                   else:
                         self.out_projs.append(_lowerCAmelCase						)

             self.out_layers.append(nn.Linear(_lowerCAmelCase    ,    _lowerCAmelCase						)						)
       else:
             for i in range(len(self.cutoffs						)						):
                   SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                   SCREAMING_SNAKE_CASE_     						=      d_embed // (div_val**i)

                   self.out_projs.append(nn.Parameter(torch.FloatTensor(_lowerCAmelCase    ,    _lowerCAmelCase						)						)						)

                   self.out_layers.append(nn.Linear(_lowerCAmelCase    ,    r_idx - l_idx						)						)

       SCREAMING_SNAKE_CASE_     						=      keep_order



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Dict						):
       if proj is None:
             SCREAMING_SNAKE_CASE_     						=      nn.functional.linear(_lowerCAmelCase    ,    _lowerCAmelCase    ,    bias=_lowerCAmelCase						)
       else:
             # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
             SCREAMING_SNAKE_CASE_     						=      nn.functional.linear(_lowerCAmelCase    ,    proj.t().contiguous()						)
             SCREAMING_SNAKE_CASE_     						=      nn.functional.linear(_lowerCAmelCase    ,    _lowerCAmelCase    ,    bias=_lowerCAmelCase						)
             # else:
             #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
             #     if bias is not None:
             #         logit = logit + bias

       return logit



 def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[int]=None    ,    _lowerCAmelCase  :		Tuple=False						):

       if labels is not None:
             # Shift so that tokens < n predict n
             SCREAMING_SNAKE_CASE_     						=      hidden[..., :-1, :].contiguous()
             SCREAMING_SNAKE_CASE_     						=      labels[..., 1:].contiguous()
             SCREAMING_SNAKE_CASE_     						=      hidden.view(-1    ,    hidden.size(-1						)						)
             SCREAMING_SNAKE_CASE_     						=      labels.view(-1						)
             if hidden.size(0						) != labels.size(0						):
                   raise RuntimeError('Input and labels should have the same size in the batch dimension.'						)
       else:
             SCREAMING_SNAKE_CASE_     						=      hidden.view(-1    ,    hidden.size(-1						)						)

       if self.n_clusters == 0:
             SCREAMING_SNAKE_CASE_     						=      self._compute_logit(_lowerCAmelCase    ,    self.out_layers[0].weight    ,    self.out_layers[0].bias    ,    self.out_projs[0]						)
             if labels is not None:
                   SCREAMING_SNAKE_CASE_     						=      labels != -100
                   SCREAMING_SNAKE_CASE_     						=      torch.zeros_like(_lowerCAmelCase    ,    dtype=hidden.dtype    ,    device=hidden.device						)
                   SCREAMING_SNAKE_CASE_     						=      (
                       -nn.functional.log_softmax(_lowerCAmelCase    ,    dim=-1						)[mask].gather(1    ,    labels[mask].unsqueeze(1						)						).squeeze(1						)
                   )
             else:
                   SCREAMING_SNAKE_CASE_     						=      nn.functional.log_softmax(_lowerCAmelCase    ,    dim=-1						)
       else:
             # construct weights and biases
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      [], []
             for i in range(len(self.cutoffs						)						):
                   if self.div_val == 1:
                         SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[0].weight[l_idx:r_idx]
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[0].bias[l_idx:r_idx]
                   else:
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[i].weight
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[i].bias

                   if i == 0:
                         SCREAMING_SNAKE_CASE_     						=      torch.cat([weight_i, self.cluster_weight]    ,    dim=0						)
                         SCREAMING_SNAKE_CASE_     						=      torch.cat([bias_i, self.cluster_bias]    ,    dim=0						)

                   weights.append(_lowerCAmelCase						)
                   biases.append(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      weights[0], biases[0], self.out_projs[0]

             SCREAMING_SNAKE_CASE_     						=      self._compute_logit(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      nn.functional.log_softmax(_lowerCAmelCase    ,    dim=1						)

             if labels is None:
                   SCREAMING_SNAKE_CASE_     						=      hidden.new_empty((head_logit.size(0						), self.n_token)						)
             else:
                   SCREAMING_SNAKE_CASE_     						=      torch.zeros_like(_lowerCAmelCase    ,    dtype=hidden.dtype    ,    device=hidden.device						)

             SCREAMING_SNAKE_CASE_     						=      0
             SCREAMING_SNAKE_CASE_     						=      [0] + self.cutoffs
             for i in range(len(_lowerCAmelCase						) - 1						):
                   SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      cutoff_values[i], cutoff_values[i + 1]

                   if labels is not None:
                         SCREAMING_SNAKE_CASE_     						=      (labels >= l_idx) & (labels < r_idx)
                         SCREAMING_SNAKE_CASE_     						=      mask_i.nonzero().squeeze()

                         if indices_i.numel() == 0:
                               continue

                         SCREAMING_SNAKE_CASE_     						=      labels.index_select(0    ,    _lowerCAmelCase						) - l_idx
                         SCREAMING_SNAKE_CASE_     						=      head_logprob.index_select(0    ,    _lowerCAmelCase						)
                         SCREAMING_SNAKE_CASE_     						=      hidden.index_select(0    ,    _lowerCAmelCase						)
                   else:
                         SCREAMING_SNAKE_CASE_     						=      hidden

                   if i == 0:
                         if labels is not None:
                               SCREAMING_SNAKE_CASE_     						=      head_logprob_i.gather(1    ,    target_i[:, None]						).squeeze(1						)
                         else:
                               SCREAMING_SNAKE_CASE_     						=      head_logprob[:, : self.cutoffs[0]]
                   else:
                         SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      weights[i], biases[i], self.out_projs[i]

                         SCREAMING_SNAKE_CASE_     						=      self._compute_logit(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
                         SCREAMING_SNAKE_CASE_     						=      nn.functional.log_softmax(_lowerCAmelCase    ,    dim=1						)
                         SCREAMING_SNAKE_CASE_     						=      self.cutoffs[0] + i - 1  # No probability for the head cluster
                         if labels is not None:
                               SCREAMING_SNAKE_CASE_     						=      head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                                   1    ,    target_i[:, None]						).squeeze(1						)
                         else:
                               SCREAMING_SNAKE_CASE_     						=      head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                               SCREAMING_SNAKE_CASE_     						=      logprob_i

                   if labels is not None:
                         if (hasattr(self    ,    'keep_order'						) and self.keep_order) or keep_order:
                               out.index_copy_(0    ,    _lowerCAmelCase    ,    -logprob_i						)
                         else:
                               out[offset : offset + logprob_i.size(0						)].copy_(-logprob_i						)
                         offset += logprob_i.size(0						)

       return out




 def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		Optional[int]						):
       if self.n_clusters == 0:
             SCREAMING_SNAKE_CASE_     						=      self._compute_logit(_lowerCAmelCase    ,    self.out_layers[0].weight    ,    self.out_layers[0].bias    ,    self.out_projs[0]						)
             return nn.functional.log_softmax(_lowerCAmelCase    ,    dim=-1						)
       else:
             # construct weights and biases
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      [], []
             for i in range(len(self.cutoffs						)						):
                   if self.div_val == 1:
                         SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[0].weight[l_idx:r_idx]
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[0].bias[l_idx:r_idx]
                   else:
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[i].weight
                         SCREAMING_SNAKE_CASE_     						=      self.out_layers[i].bias

                   if i == 0:
                         SCREAMING_SNAKE_CASE_     						=      torch.cat([weight_i, self.cluster_weight]    ,    dim=0						)
                         SCREAMING_SNAKE_CASE_     						=      torch.cat([bias_i, self.cluster_bias]    ,    dim=0						)

                   weights.append(_lowerCAmelCase						)
                   biases.append(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      weights[0], biases[0], self.out_projs[0]
             SCREAMING_SNAKE_CASE_     						=      self._compute_logit(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      hidden.new_empty((head_logit.size(0						), self.n_token)						)
             SCREAMING_SNAKE_CASE_     						=      nn.functional.log_softmax(_lowerCAmelCase    ,    dim=1						)

             SCREAMING_SNAKE_CASE_     						=      [0] + self.cutoffs
             for i in range(len(_lowerCAmelCase						) - 1						):
                   SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      cutoff_values[i], cutoff_values[i + 1]

                   if i == 0:
                         SCREAMING_SNAKE_CASE_     						=      head_logprob[:, : self.cutoffs[0]]
                   else:
                         SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      weights[i], biases[i], self.out_projs[i]

                         SCREAMING_SNAKE_CASE_     						=      self._compute_logit(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
                         SCREAMING_SNAKE_CASE_     						=      nn.functional.log_softmax(_lowerCAmelCase    ,    dim=1						)

                         SCREAMING_SNAKE_CASE_     						=      head_logprob[:, -i] + tail_logprob_i
                         SCREAMING_SNAKE_CASE_     						=      logprob_i

             return out





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

import requests

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[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__			:       Union[str, Any]         =							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}.''')