



import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
 __snake_case						:Optional[Any]      		=     pd.read_csv('''sample_data.csv''', header=None)
 __snake_case						:Optional[Any]      		=     df.shape[:1][0]
 # If you're using some other dataset input the target column
 __snake_case						:Optional[Any]      		=     df.iloc[:, 1:2]
 __snake_case						:int      		=     actual_data.values.reshape(len_data, 1)
 __snake_case						:Optional[Any]      		=     MinMaxScaler().fit_transform(actual_data)
 __snake_case						:int      		=     10
 __snake_case						:Tuple      		=     5
 __snake_case						:Optional[Any]      		=     20
 __snake_case						:Optional[int]      		=     len_data - periods * look_back
 __snake_case						:int      		=     actual_data[:division]
 __snake_case						:List[str]      		=     actual_data[division - look_back :]
 __snake_case		,__snake_case						:Dict      		=     [], []
 __snake_case		,__snake_case						:Any      		=     [], []

 for i in range(0, len(train_data) - forward_days - look_back + 1):
  train_x.append(train_data[i : i + look_back])
  train_y.append(train_data[i + look_back : i + look_back + forward_days])
 for i in range(0, len(test_data) - forward_days - look_back + 1):
  test_x.append(test_data[i : i + look_back])
  test_y.append(test_data[i + look_back : i + look_back + forward_days])
 __snake_case						:int      		=     np.array(train_x)
 __snake_case						:Optional[Any]      		=     np.array(test_x)
 __snake_case						:List[Any]      		=     np.array([list(i.ravel()) for i in train_y])
 __snake_case						:Optional[int]      		=     np.array([list(i.ravel()) for i in test_y])

 __snake_case						:int      		=     Sequential()
 model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
 model.add(LSTM(64, input_shape=(128, 1)))
 model.add(Dense(forward_days))
 model.compile(loss='''mean_squared_error''', optimizer='''adam''')
 __snake_case						:int      		=     model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __snake_case						:Union[str, Any]      		=     model.predict(x_test)










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

import requests





def      __snake_case	(			_UpperCAmelCase = "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(_UpperCAmelCase				)
    return requests.get(f'https://openlibrary.org/{new_olid}.json'				).json()





def      __snake_case	(			_UpperCAmelCase				):
    __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(_UpperCAmelCase   , _UpperCAmelCase				):
            __a						       =				''', '''.join(_UpperCAmelCase				)
    return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  __snake_case						:List[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:
   __snake_case						:Optional[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}.')





