



'''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							       =			pd.read_csv('''sample_data.csv''', header=None)
  lowercase							       =			df.shape[:1][0]
  # If you're using some other dataset input the target column
  lowercase							       =			df.iloc[:, 1:2]
  lowercase							       =			actual_data.values.reshape(len_data, 1)
  lowercase							       =			MinMaxScaler().fit_transform(actual_data)
  lowercase							       =			10
  lowercase							       =			5
  lowercase							       =			20
  lowercase							       =			len_data - periods * look_back
  lowercase							       =			actual_data[:division]
  lowercase							       =			actual_data[division - look_back :]
  lowercase       ,      lowercase							       =			[], []
  lowercase       ,      lowercase							       =			[], []

  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							       =			np.array(train_x)
  lowercase							       =			np.array(test_x)
  lowercase							       =			np.array([list(i.ravel()) for i in train_y])
  lowercase							       =			np.array([list(i.ravel()) for i in test_y])

  lowercase							       =			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''')
  lowercase							       =			model.fit(
      x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
  )
  lowercase							       =			model.predict(x_test)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase							       =			{
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''VisionEncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''TFVisionEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


