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

							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])
							lowercase__		:Union[str, Any]			  =   np.array(train_x)
							lowercase__		:Tuple			  =   np.array(test_x)
							lowercase__		:Optional[int]			  =   np.array([list(i.ravel()) for i in train_y])
							lowercase__		:Dict			  =   np.array([list(i.ravel()) for i in test_y])

							lowercase__		:Dict			  =   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")
							lowercase__		:Optional[Any]			  =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							lowercase__		:List[Any]			  =   model.predict(x_test)






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__		:Union[str, Any]			  =   {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[Any]			  =   ["DPTFeatureExtractor"]
							lowercase__		:List[Any]			  =   ["DPTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DPTForDepthEstimation",
							    "DPTForSemanticSegmentation",
							    "DPTModel",
							    "DPTPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_dpt import DPTFeatureExtractor
														from .image_processing_dpt import DPTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dpt import (
														    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPTForDepthEstimation,
														    DPTForSemanticSegmentation,
														    DPTModel,
														    DPTPreTrainedModel,
														)


else:
							import sys

							lowercase__		:List[Any]			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




