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

						lowerCAmelCase					:    Tuple           = 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					:    Optional[int]           = model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						lowerCAmelCase					:    List[str]           = model.predict(x_test)









"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





