import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Any						=(EulerDiscreteScheduler,)
  SCREAMING_SNAKE_CASE_	:	List[str]						=10






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      {
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**SCREAMING_SNAKE_CASE_	)
       return config






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





       '''simple docstring'''





       for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]   ,				[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_   ,				beta_end=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config()
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       scheduler.set_timesteps(self.num_inference_steps	)

       UpperCamelCase__										=      torch.manual_seed(0	)

       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCamelCase__										=      sample.to(SCREAMING_SNAKE_CASE_	)

       for i, t in enumerate(scheduler.timesteps	):
            UpperCamelCase__										=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      output.prev_sample

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 10.0807	) < 1E-2
       assert abs(result_mean.item() - 0.0131	) < 1E-3






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config(prediction_type='''v_prediction'''	)
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       scheduler.set_timesteps(self.num_inference_steps	)

       UpperCamelCase__										=      torch.manual_seed(0	)

       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCamelCase__										=      sample.to(SCREAMING_SNAKE_CASE_	)

       for i, t in enumerate(scheduler.timesteps	):
            UpperCamelCase__										=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      output.prev_sample

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 0.0002	) < 1E-2
       assert abs(result_mean.item() - 2.2676E-06	) < 1E-3






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config()
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       scheduler.set_timesteps(self.num_inference_steps   ,				device=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.manual_seed(0	)

       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCamelCase__										=      sample.to(SCREAMING_SNAKE_CASE_	)

       for t in scheduler.timesteps:
            UpperCamelCase__										=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      output.prev_sample

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 10.0807	) < 1E-2
       assert abs(result_mean.item() - 0.0131	) < 1E-3






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config()
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_   ,				use_karras_sigmas=SCREAMING_SNAKE_CASE_	)

       scheduler.set_timesteps(self.num_inference_steps   ,				device=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.manual_seed(0	)

       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCamelCase__										=      sample.to(SCREAMING_SNAKE_CASE_	)

       for t in scheduler.timesteps:
            UpperCamelCase__										=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      output.prev_sample

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 124.52299499511719	) < 1E-2
       assert abs(result_mean.item() - 0.16213932633399963	) < 1E-3




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

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

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



