


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

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowerCamelCase	:							int				={
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ASTForAudioClassification''',
     '''ASTModel''',
     '''ASTPreTrainedModel''',
 ]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Optional[int]				=['''ASTFeatureExtractor''']

if TYPE_CHECKING:
 from .configuration_audio_spectrogram_transformer import (
     AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ASTConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ASTForAudioClassification,
      ASTModel,
      ASTPreTrainedModel,
  )

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
 import sys

 lowerCamelCase	:							Optional[int]				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)