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

	_SCREAMING_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""")
	_SCREAMING_SNAKE_CASE            =		model.fit(
	    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
	)
	_SCREAMING_SNAKE_CASE            =		model.predict(x_test)







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase            =		{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

	_UpperCAmelCase            =		ValueGuidedRLPipeline.from_pretrained(
	    """bglick13/hopper-medium-v2-value-function-hor32""",
	    env=env,
	)

	env.seed(0)
	_UpperCAmelCase            =		env.reset()
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		1000
	_UpperCAmelCase            =		[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase            =		pipeline(obs, planning_horizon=32)

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		env.get_normalized_score(total_reward)

			# update return
			total_reward += reward
			total_score += score
			print(
			    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
			    F""" {total_score}"""
			)

			# save observations for rendering
			rollout.append(next_observation.copy())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

	print(F"""Total reward: {total_reward}""")






