

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

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

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












'''simple docstring'''




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


lowercase__       : List[Any]         =       logging.get_logger(__name__)

lowercase__       : Optional[int]         =       {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''lxmert'''
     lowerCAmelCase						     =			{}


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=9500		,      _UpperCAmelCase=1600		,      _UpperCAmelCase=400		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=512		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=9		,      _UpperCAmelCase=5		,      _UpperCAmelCase=5		,      _UpperCAmelCase=2048		,      _UpperCAmelCase=4		,      _UpperCAmelCase=6.67		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			vocab_size
          __A		:					int						=			hidden_size
          __A		:					str						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					int						=			intermediate_size
          __A		:					str						=			hidden_dropout_prob
          __A		:					Optional[int]						=			attention_probs_dropout_prob
          __A		:					Optional[Any]						=			max_position_embeddings
          __A		:					Tuple						=			type_vocab_size
          __A		:					Optional[int]						=			initializer_range
          __A		:					Any						=			layer_norm_eps
          __A		:					Optional[Any]						=			num_qa_labels
          __A		:					Optional[int]						=			num_object_labels
          __A		:					Any						=			num_attr_labels
          __A		:					Union[str, Any]						=			l_layers
          __A		:					Optional[int]						=			x_layers
          __A		:					List[Any]						=			r_layers
          __A		:					Tuple						=			visual_feat_dim
          __A		:					Tuple						=			visual_pos_dim
          __A		:					Optional[int]						=			visual_loss_normalizer
          __A		:					int						=			task_matched
          __A		:					List[Any]						=			task_mask_lm
          __A		:					Optional[Any]						=			task_obj_predict
          __A		:					str						=			task_qa
          __A		:					List[Any]						=			visual_obj_loss
          __A		:					Optional[Any]						=			visual_attr_loss
          __A		:					Union[str, Any]						=			visual_feat_loss
          __A		:					Union[str, Any]						=			{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
          super().__init__(**_UpperCAmelCase)