




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

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

     lowerCAmelCase_									= 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""")
     lowerCAmelCase_									= model.fit(
         x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
     )
     lowerCAmelCase_									= model.predict(x_test)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_					    = {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LiltForQuestionAnswering""",
         """LiltForSequenceClassification""",
         """LiltForTokenClassification""",
         """LiltModel""",
         """LiltPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lilt import (
              LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
              LiltForQuestionAnswering,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltModel,
              LiltPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase_					    = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

