





"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[str]     ,    SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				Tuple ):


							'''simple docstring'''







							__lowerCamelCase   :   str       =OmegaConf.load(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Dict       =torch.load(SCREAMING_SNAKE_CASE     ,    map_location='''cpu''' )['''model''']
							__lowerCamelCase   :   Optional[Any]       =list(state_dict.keys() )

							# extract state_dict for VQVAE
							__lowerCamelCase   :   int       ={}
							__lowerCamelCase   :   str       ='''first_stage_model.'''
							for key in keys:
														if key.startswith(SCREAMING_SNAKE_CASE ):
																					__lowerCamelCase   :   List[Any]       =state_dict[key]

    # extract state_dict for UNetLDM
							__lowerCamelCase   :   List[Any]       ={}
							__lowerCamelCase   :   Optional[Any]       ='''model.diffusion_model.'''
							for key in keys:
														if key.startswith(SCREAMING_SNAKE_CASE ):
																					__lowerCamelCase   :   Optional[Any]       =state_dict[key]

							__lowerCamelCase   :   Union[str, Any]       =config.model.params.first_stage_config.params
							__lowerCamelCase   :   Tuple       =config.model.params.unet_config.params

							__lowerCamelCase   :   Union[str, Any]       =VQModel(**SCREAMING_SNAKE_CASE ).eval()
							vqvae.load_state_dict(SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   List[Any]       =UNetLDMModel(**SCREAMING_SNAKE_CASE ).eval()
							unet.load_state_dict(SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   str       =DDIMScheduler(
							    timesteps=config.model.params.timesteps     ,    beta_schedule='''scaled_linear'''     ,    beta_start=config.model.params.linear_start     ,    beta_end=config.model.params.linear_end     ,    clip_sample=SCREAMING_SNAKE_CASE     ,    )

							__lowerCamelCase   :   Union[str, Any]       =LDMPipeline(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							pipeline.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()
							parser.add_argument('--checkpoint_path', type=str, required=True)
							parser.add_argument('--config_path', type=str, required=True)
							parser.add_argument('--output_path', type=str, required=True)
							_UpperCamelCase					      =   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)











"""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)




