





'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       (DDIMParallelScheduler,)
     lowerCAmelCase					=       (('''eta''', 0.0), ('''num_inference_steps''', 50))


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[Any]      =       {
              'num_train_timesteps': 1000,
              'beta_start': 0.0001,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'clip_sample': True,
          }

          config.update(**_UpperCAmelCase)
          return config


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          __A						: Tuple      =       self.scheduler_classes[0]
          __A						: str      =       self.get_scheduler_config(**_UpperCAmelCase)
          __A						: int      =       scheduler_class(**_UpperCAmelCase)

          __A ,__A						: Optional[int]      =       10, 0.0

          __A						: str      =       self.dummy_model()
          __A						: Any      =       self.dummy_sample_deter

          scheduler.set_timesteps(_UpperCAmelCase)

          for t in scheduler.timesteps:
               __A						: List[Any]      =       model(_UpperCAmelCase						,	_UpperCAmelCase)
               __A						: Union[str, Any]      =       scheduler.step(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase).prev_sample

          return sample


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=_UpperCAmelCase)

          __A						: Tuple      =       self.scheduler_classes[0]
          __A						: int      =       self.get_scheduler_config(steps_offset=1)
          __A						: List[str]      =       scheduler_class(**_UpperCAmelCase)
          scheduler.set_timesteps(5)
          assert torch.equal(scheduler.timesteps						,	torch.LongTensor([801, 601, 401, 201, 1]))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,	[0.002, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=_UpperCAmelCase						,	beta_end=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.check_over_configs(thresholding=_UpperCAmelCase)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=_UpperCAmelCase						,	prediction_type=_UpperCAmelCase						,	sample_max_value=_UpperCAmelCase						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for t in [1, 10, 49]:
               self.check_over_forward(time_step=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for t, num_inference_steps in zip([1, 10, 50]						,	[10, 50, 500]):
               self.check_over_forward(time_step=_UpperCAmelCase						,	num_inference_steps=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for t, eta in zip([1, 10, 49]						,	[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=_UpperCAmelCase						,	eta=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.scheduler_classes[0]
          __A						: List[str]      =       self.get_scheduler_config()
          __A						: Optional[int]      =       scheduler_class(**_UpperCAmelCase)

          assert torch.sum(torch.abs(scheduler._get_variance(0						,	0) - 0.0)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420						,	400) - 0.14771)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980						,	960) - 0.32460)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0						,	0) - 0.0)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487						,	486) - 0.00979)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999						,	998) - 0.02)) < 1e-5


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.scheduler_classes[0]
          __A						: Optional[int]      =       self.get_scheduler_config()
          __A						: Optional[Any]      =       scheduler_class(**_UpperCAmelCase)

          __A ,__A						: Union[str, Any]      =       10, 0.0
          scheduler.set_timesteps(_UpperCAmelCase)

          __A						: int      =       self.dummy_model()
          __A						: Tuple      =       self.dummy_sample_deter
          __A						: Optional[Any]      =       self.dummy_sample_deter + 0.1
          __A						: Union[str, Any]      =       self.dummy_sample_deter - 0.1

          __A						: str      =       samplea.shape[0]
          __A						: Optional[Any]      =       torch.stack([samplea, samplea, samplea]						,	dim=0)
          __A						: Union[str, Any]      =       torch.arange(_UpperCAmelCase)[0:3, None].repeat(1						,	_UpperCAmelCase)

          __A						: int      =       model(samples.flatten(0						,	1)						,	timesteps.flatten(0						,	1))
          __A						: Dict      =       scheduler.batch_step_no_noise(_UpperCAmelCase						,	timesteps.flatten(0						,	1)						,	samples.flatten(0						,	1)						,	_UpperCAmelCase)

          __A						: List[Any]      =       torch.sum(torch.abs(_UpperCAmelCase))
          __A						: Any      =       torch.mean(torch.abs(_UpperCAmelCase))

          assert abs(result_sum.item() - 1147.7904) < 1e-2
          assert abs(result_mean.item() - 0.4982) < 1e-3


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.full_loop()

          __A						: str      =       torch.sum(torch.abs(_UpperCAmelCase))
          __A						: int      =       torch.mean(torch.abs(_UpperCAmelCase))

          assert abs(result_sum.item() - 172.0067) < 1e-2
          assert abs(result_mean.item() - 0.223967) < 1e-3


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.full_loop(prediction_type='v_prediction')

          __A						: Union[str, Any]      =       torch.sum(torch.abs(_UpperCAmelCase))
          __A						: Dict      =       torch.mean(torch.abs(_UpperCAmelCase))

          assert abs(result_sum.item() - 52.5302) < 1e-2
          assert abs(result_mean.item() - 0.0684) < 1e-3


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.full_loop(set_alpha_to_one=_UpperCAmelCase						,	beta_start=0.01)
          __A						: Optional[Any]      =       torch.sum(torch.abs(_UpperCAmelCase))
          __A						: List[Any]      =       torch.mean(torch.abs(_UpperCAmelCase))

          assert abs(result_sum.item() - 149.8295) < 1e-2
          assert abs(result_mean.item() - 0.1951) < 1e-3


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.full_loop(set_alpha_to_one=_UpperCAmelCase						,	beta_start=0.01)
          __A						: Dict      =       torch.sum(torch.abs(_UpperCAmelCase))
          __A						: Any      =       torch.mean(torch.abs(_UpperCAmelCase))

          assert abs(result_sum.item() - 149.0784) < 1e-2
          assert abs(result_mean.item() - 0.1941) < 1e-3






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

  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__    :      List[Any]						=    np.array(train_x)
  lowercase__    :      List[Any]						=    np.array(test_x)
  lowercase__    :      str						=    np.array([list(i.ravel()) for i in train_y])
  lowercase__    :      Any						=    np.array([list(i.ravel()) for i in test_y])

  lowercase__    :      Optional[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''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)