






'''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__":
  a_ 		=       pd.read_csv('sample_data.csv', header=None)
  a_ 		=       df.shape[:1][0]
  # If you're using some other dataset input the target column
  a_ 		=       df.iloc[:, 1:2]
  a_ 		=       actual_data.values.reshape(len_data, 1)
  a_ 		=       MinMaxScaler().fit_transform(actual_data)
  a_ 		=       1_0
  a_ 		=       5
  a_ 		=       2_0
  a_ 		=       len_data - periods * look_back
  a_ 		=       actual_data[:division]
  a_ 		=       actual_data[division - look_back :]
  a_ 		=       [], []
  a_ 		=       [], []

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

  a_ 		=       Sequential()
  model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='mean_squared_error', optimizer='adam')
  a_ 		=       model.fit(
      x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
  )
  a_ 		=       model.predict(x_test)


'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase :      Optional[int]					 =     False







class 							A__    (       unittest.TestCase  ):
  pass







@slow
@require_torch_gpu
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion' )
       pipe.to(_a )
       pipe.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =pipe(
           image=_a							,					generator=_a							,					guidance_scale=7.5							,					num_inference_steps=50							,					output_type='numpy'							,					).images

       _SCREAMING_SNAKE_CASE							    =image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )

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