





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					__a      (unittest.TestCase     ):

							@property
							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  UNetaDModel(
													    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,     )
													return model



							def     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Dict     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Union[str, Any]     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[int]     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      str     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''       ,     return_dict=__magic_name__   )[0]

													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Dict     						=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2





@slow
@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''google/ncsnpp-celebahq-256'''
													UpperCAmelCase_			:      List[str]     						=  UNetaDModel.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Any     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe(num_inference_steps=20       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													assert image.shape == (1, 2_56, 2_56, 3)
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2












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

					snake_case_     :       List[Any]						=  Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





