





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase__    :      Any						=    {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Optional[Any]						=    ['''LayoutXLMTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      str						=    ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

  lowercase__    :      str						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''




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

  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])
  lowercase__    :      List[Any]						=    np.array(train_x)
  lowercase__    :      List[Any]						=    np.array(test_x)
  lowercase__    :      str						=    np.array([list(i.ravel()) for i in train_y])
  lowercase__    :      Any						=    np.array([list(i.ravel()) for i in test_y])

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)