






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					=	logging.get_logger(__name__)

__A					=	{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class 				_A			(     _UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= 'fnet'



    def __init__(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=32000 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=768 ,     __SCREAMING_SNAKE_CASE      :     Any=12 ,     __SCREAMING_SNAKE_CASE      :     Tuple=3072 ,     __SCREAMING_SNAKE_CASE      :     List[Any]="gelu_new" ,     __SCREAMING_SNAKE_CASE      :     Dict=0.1 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=512 ,     __SCREAMING_SNAKE_CASE      :     str=4 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=1e-12 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=False ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=512 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=3 ,     __SCREAMING_SNAKE_CASE      :     Tuple=1 ,     __SCREAMING_SNAKE_CASE      :     int=2 ,     **__SCREAMING_SNAKE_CASE      :     Any ,     )      ->							Optional[int]:
           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,     bos_token_id=__SCREAMING_SNAKE_CASE ,     eos_token_id=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=max_position_embeddings
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=intermediate_size
           __UpperCAmelCase     				=hidden_act
           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=initializer_range
           __UpperCAmelCase     				=type_vocab_size
           __UpperCAmelCase     				=layer_norm_eps
           __UpperCAmelCase     				=use_tpu_fourier_optimizations
           __UpperCAmelCase     				=tpu_short_seq_length










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

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

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






