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

						UpperCAmelCase_							:      Tuple      =  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_							:      Tuple      =  model.fit(
						    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
						)
						UpperCAmelCase_							:      Union[str, Any]      =  model.predict(x_test)










from manim import *






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def 			__A							(       self		):
							A__							=       Rectangle(height=0.5			,	width=0.5		)
							A__							=       Rectangle(height=0.46			,	width=0.46		).set_stroke(width=0		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       VGroup(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       Text("CPU"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							cpu.move_to([-2.5, -0.5, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(4		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)
							A__							=       Text("GPU"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							gpu.move_to([-1, -1, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)

							A__							=       Text("Model"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0.5			,	aligned_edge=UpperCAmelCase__		)
							model.move_to([3, -1.0, 0]		)
							self.add(UpperCAmelCase__		)

							A__							=       []
							for i, rect in enumerate(UpperCAmelCase__		):
													rect.set_stroke(UpperCAmelCase__		)
													# target = fill.copy().set_fill(YELLOW, opacity=0.7)
													# target.move_to(rect)
													# self.add(target)

													A__							=       Rectangle(height=0.46 / 4			,	width=0.46 / 3		).set_stroke(width=0.0		).set_fill(UpperCAmelCase__			,	opacity=0.7		)

													if i == 0:
																			cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)			,	buff=0.02			,	direction=UpperCAmelCase__		)
																			cpu_target.set_x(cpu_target.get_x() + 0.1		)
													elif i == 3:
																			cpu_target.next_to(cpu_targs[0]			,	direction=UpperCAmelCase__			,	buff=0.0		)
													else:
																			cpu_target.next_to(cpu_targs[i - 1]			,	direction=UpperCAmelCase__			,	buff=0.0		)
													self.add(UpperCAmelCase__		)
													cpu_targs.append(UpperCAmelCase__		)

							A__							=       [mem.copy() for i in range(6		)]
							A__							=       VGroup(*UpperCAmelCase__		).arrange(UpperCAmelCase__			,	buff=0		)

							A__							=       Text("Loaded Checkpoint"			,	font_size=24		)
							A__							=       Group(UpperCAmelCase__			,	UpperCAmelCase__		).arrange(UpperCAmelCase__			,	aligned_edge=UpperCAmelCase__			,	buff=0.4		)
							checkpoint.move_to([3, 0.5, 0]		)

							A__							=       Square(side_length=2.2		)
							key.move_to([-5, 2, 0]		)

							A__							=       MarkupText(
							    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""			,	font_size=18			,	)

							key_text.move_to([-5, 2.4, 0]		)

							self.add(UpperCAmelCase__			,	UpperCAmelCase__		)

							A__							=       MarkupText(
							    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""			,	font_size=18			,	)

							blue_text.next_to(UpperCAmelCase__			,	DOWN * 2.4			,	aligned_edge=key_text.get_left()		)

							A__							=       MarkupText(
							    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""			,	font_size=24			,	)
							step_a.move_to([2, 2, 0]		)
							self.play(Write(UpperCAmelCase__		)			,	Write(UpperCAmelCase__		)		)

							self.play(Write(UpperCAmelCase__			,	run_time=1		)			,	Create(UpperCAmelCase__			,	run_time=1		)		)

							A__							=       []
							A__							=       []
							for i, rect in enumerate(UpperCAmelCase__		):
													A__							=       fill.copy().set_fill(UpperCAmelCase__			,	opacity=0.7		)
													target.move_to(UpperCAmelCase__		)
													first_animations.append(GrowFromCenter(UpperCAmelCase__			,	run_time=1		)		)

													A__							=       target.copy()
													cpu_target.generate_target()
													if i < 5:
																			cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
													else:
																			cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
													second_animations.append(MoveToTarget(UpperCAmelCase__			,	run_time=1.5		)		)

							self.play(*UpperCAmelCase__		)
							self.play(*UpperCAmelCase__		)
							self.wait()





