





'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 					__SCREAMING_SNAKE_CASE	(						__A			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   (EulerDiscreteScheduler,)
     UpperCAmelCase__       :						Union[str, Any]      =   10

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       _snake_case     		=				{
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**lowerCamelCase	)
       return config

     def 	UpperCamelCase(					self	):





       for timesteps in [10, 50, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase	)

     def 	UpperCamelCase(					self	):





       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=lowerCamelCase						,   beta_end=lowerCamelCase	)

     def 	UpperCamelCase(					self	):





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

     def 	UpperCamelCase(					self	):





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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config()
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

       scheduler.set_timesteps(self.num_inference_steps	)

       _snake_case     		=				torch.manual_seed(0	)

       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter * scheduler.init_noise_sigma
       _snake_case     		=				sample.to(lowerCamelCase	)

       for i, t in enumerate(scheduler.timesteps	):
         _snake_case     		=				scheduler.scale_model_input(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   generator=lowerCamelCase	)
         _snake_case     		=				output.prev_sample

       _snake_case     		=				torch.sum(torch.abs(lowerCamelCase	)	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config(prediction_type="v_prediction"	)
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

       scheduler.set_timesteps(self.num_inference_steps	)

       _snake_case     		=				torch.manual_seed(0	)

       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter * scheduler.init_noise_sigma
       _snake_case     		=				sample.to(lowerCamelCase	)

       for i, t in enumerate(scheduler.timesteps	):
         _snake_case     		=				scheduler.scale_model_input(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   generator=lowerCamelCase	)
         _snake_case     		=				output.prev_sample

       _snake_case     		=				torch.sum(torch.abs(lowerCamelCase	)	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config()
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

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

       _snake_case     		=				torch.manual_seed(0	)

       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       _snake_case     		=				sample.to(lowerCamelCase	)

       for t in scheduler.timesteps:
         _snake_case     		=				scheduler.scale_model_input(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   generator=lowerCamelCase	)
         _snake_case     		=				output.prev_sample

       _snake_case     		=				torch.sum(torch.abs(lowerCamelCase	)	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config()
       _snake_case     		=				scheduler_class(**lowerCamelCase						,   use_karras_sigmas=lowerCamelCase	)

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

       _snake_case     		=				torch.manual_seed(0	)

       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       _snake_case     		=				sample.to(lowerCamelCase	)

       for t in scheduler.timesteps:
         _snake_case     		=				scheduler.scale_model_input(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   generator=lowerCamelCase	)
         _snake_case     		=				output.prev_sample

       _snake_case     		=				torch.sum(torch.abs(lowerCamelCase	)	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_sum.item() - 124.52299499511719	) < 1e-2
       assert abs(result_mean.item() - 0.16213932633399963	) < 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__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						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])
 __magic_name__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

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


