
"""simple docstring"""






import operator as op


_lowercase							=				'''scaler.pt'''
_lowercase							=				'''pytorch_model'''
_lowercase							=				'''random_states'''
_lowercase							=				'''optimizer'''
_lowercase							=				'''scheduler'''
_lowercase							=				'''pytorch_model.bin'''
_lowercase							=				'''pytorch_model.bin.index.json'''
_lowercase							=				'''model.safetensors'''
_lowercase							=				'''model.safetensors.index.json'''
_lowercase							=				'''1.10.2'''
_lowercase							=				'''py38'''
_lowercase							=				'''4.17.0'''
_lowercase							=				['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
_lowercase							=				['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
_lowercase							=				['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
_lowercase							=				['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
_lowercase							=				['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
_lowercase							=				'''2.0.1'''
_lowercase							=				['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
_lowercase							=				['''default''', '''reduce-overhead''', '''max-autotune''']

_lowercase							=				{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
_lowercase							=				[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

_lowercase							=				['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
_lowercase							=				['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']







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

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

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





