from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__								={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=[
         '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BigBirdPegasusForCausalLM''',
         '''BigBirdPegasusForConditionalGeneration''',
         '''BigBirdPegasusForQuestionAnswering''',
         '''BigBirdPegasusForSequenceClassification''',
         '''BigBirdPegasusModel''',
         '''BigBirdPegasusPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_bigbird_pegasus import (
         BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BigBirdPegasusConfig,
         BigBirdPegasusOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bigbird_pegasus import (
              BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
              BigBirdPegasusForCausalLM,
              BigBirdPegasusForConditionalGeneration,
              BigBirdPegasusForQuestionAnswering,
              BigBirdPegasusForSequenceClassification,
              BigBirdPegasusModel,
              BigBirdPegasusPreTrainedModel,
          )


else:
     import sys

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




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

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

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



