





"""simple docstring"""





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Union[str, Any]									=		(DDPMParallelScheduler,)




				def        __lowercase	(  self	:Dict    ,     **__lowercase	:List[Any]      ):
											__lowerCamelCase   :   Dict       ={
											    '''num_train_timesteps''': 1000,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__lowercase      )
											return config




				def        __lowercase	(  self	:Any      ):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											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=__lowercase    ,     beta_end=__lowercase      )




				def        __lowercase	(  self	:Optional[Any]      ):
											for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=__lowercase      )




				def        __lowercase	(  self	:List[Any]      ):
											for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=__lowercase      )




				def        __lowercase	(  self	:List[str]      ):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase      )




				def        __lowercase	(  self	:List[str]      ):
											self.check_over_configs(thresholding=__lowercase      )
											for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=__lowercase    ,     prediction_type=__lowercase    ,     sample_max_value=__lowercase    ,     )




				def        __lowercase	(  self	:Any      ):
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(prediction_type=__lowercase      )




				def        __lowercase	(  self	:Optional[int]      ):
											for t in [0, 500, 999]:
																		self.check_over_forward(time_step=__lowercase      )




				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   List[Any]       =self.scheduler_classes[0]
											__lowerCamelCase   :   List[Any]       =self.get_scheduler_config()
											__lowerCamelCase   :   Tuple       =scheduler_class(**__lowercase      )

											assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.00979      )      ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Optional[int]       =self.scheduler_classes[0]
											__lowerCamelCase   :   Optional[int]       =self.get_scheduler_config()
											__lowerCamelCase   :   Union[str, Any]       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   str       =len(__lowercase      )

											__lowerCamelCase   :   Optional[Any]       =self.dummy_model()
											__lowerCamelCase   :   Any       =self.dummy_sample_deter
											__lowerCamelCase   :   str       =self.dummy_sample_deter + 0.1
											__lowerCamelCase   :   List[Any]       =self.dummy_sample_deter - 0.1

											__lowerCamelCase   :   Tuple       =samplea.shape[0]
											__lowerCamelCase   :   List[Any]       =torch.stack([samplea, samplea, samplea]    ,     dim=0      )
											__lowerCamelCase   :   List[Any]       =torch.arange(__lowercase      )[0:3, None].repeat(1    ,     __lowercase      )

											__lowerCamelCase   :   Tuple       =model(samples.flatten(0    ,     1      )    ,     timesteps.flatten(0    ,     1      )      )
											__lowerCamelCase   :   Union[str, Any]       =scheduler.batch_step_no_noise(__lowercase    ,     timesteps.flatten(0    ,     1      )    ,     samples.flatten(0    ,     1      )      )

											__lowerCamelCase   :   Any       =torch.sum(torch.abs(__lowercase      )      )
											__lowerCamelCase   :   Optional[int]       =torch.mean(torch.abs(__lowercase      )      )

											assert abs(result_sum.item() - 1153.1833      ) < 1e-2
											assert abs(result_mean.item() - 0.5005      ) < 1e-3




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Union[str, Any]       =self.scheduler_classes[0]
											__lowerCamelCase   :   str       =self.get_scheduler_config()
											__lowerCamelCase   :   Union[str, Any]       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   Union[str, Any]       =len(__lowercase      )

											__lowerCamelCase   :   Optional[Any]       =self.dummy_model()
											__lowerCamelCase   :   List[Any]       =self.dummy_sample_deter
											__lowerCamelCase   :   Dict       =torch.manual_seed(0      )

											for t in reversed(range(__lowercase      )      ):
																		# 1. predict noise residual
																		__lowerCamelCase   :   Optional[int]       =model(__lowercase    ,     __lowercase      )

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase   :   Tuple       =scheduler.step(__lowercase    ,     __lowercase    ,     __lowercase    ,     generator=__lowercase      ).prev_sample

																		__lowerCamelCase   :   Union[str, Any]       =pred_prev_sample

											__lowerCamelCase   :   List[Any]       =torch.sum(torch.abs(__lowercase      )      )
											__lowerCamelCase   :   List[Any]       =torch.mean(torch.abs(__lowercase      )      )

											assert abs(result_sum.item() - 258.9606      ) < 1e-2
											assert abs(result_mean.item() - 0.3372      ) < 1e-3




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Tuple       =self.scheduler_classes[0]
											__lowerCamelCase   :   Optional[Any]       =self.get_scheduler_config(prediction_type='''v_prediction'''      )
											__lowerCamelCase   :   Tuple       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   Optional[int]       =len(__lowercase      )

											__lowerCamelCase   :   Optional[int]       =self.dummy_model()
											__lowerCamelCase   :   List[str]       =self.dummy_sample_deter
											__lowerCamelCase   :   Dict       =torch.manual_seed(0      )

											for t in reversed(range(__lowercase      )      ):
																		# 1. predict noise residual
																		__lowerCamelCase   :   Any       =model(__lowercase    ,     __lowercase      )

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase   :   Dict       =scheduler.step(__lowercase    ,     __lowercase    ,     __lowercase    ,     generator=__lowercase      ).prev_sample

																		__lowerCamelCase   :   Tuple       =pred_prev_sample

											__lowerCamelCase   :   Any       =torch.sum(torch.abs(__lowercase      )      )
											__lowerCamelCase   :   Optional[Any]       =torch.mean(torch.abs(__lowercase      )      )

											assert abs(result_sum.item() - 202.0296      ) < 1e-2
											assert abs(result_mean.item() - 0.2631      ) < 1e-3




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Optional[Any]       =self.scheduler_classes[0]
											__lowerCamelCase   :   List[Any]       =self.get_scheduler_config()
											__lowerCamelCase   :   str       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   List[Any]       =[100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__lowercase      )

											__lowerCamelCase   :   List[Any]       =scheduler.timesteps

											for i, timestep in enumerate(__lowercase      ):
																		if i == len(__lowercase      ) - 1:
																									__lowerCamelCase   :   List[Any]       =-1
																		else:
																									__lowerCamelCase   :   int       =timesteps[i + 1]

																		__lowerCamelCase   :   int       =scheduler.previous_timestep(__lowercase      )
																		__lowerCamelCase   :   Any       =prev_t.item()

																		self.assertEqual(__lowercase    ,     __lowercase      )




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   Dict       =self.scheduler_classes[0]
											__lowerCamelCase   :   Union[str, Any]       =self.get_scheduler_config()
											__lowerCamelCase   :   str       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   List[str]       =[100, 87, 50, 51, 0]

											with self.assertRaises(__lowercase    ,     msg='''`custom_timesteps` must be in descending order.'''      ):
																		scheduler.set_timesteps(timesteps=__lowercase      )




				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   Tuple       =self.scheduler_classes[0]
											__lowerCamelCase   :   Any       =self.get_scheduler_config()
											__lowerCamelCase   :   int       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   Dict       =[100, 87, 50, 1, 0]
											__lowerCamelCase   :   int       =len(__lowercase      )

											with self.assertRaises(__lowercase    ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''      ):
																		scheduler.set_timesteps(num_inference_steps=__lowercase    ,     timesteps=__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   int       =self.scheduler_classes[0]
											__lowerCamelCase   :   str       =self.get_scheduler_config()
											__lowerCamelCase   :   List[Any]       =scheduler_class(**__lowercase      )

											__lowerCamelCase   :   int       =[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __lowercase    ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,     ):
																		scheduler.set_timesteps(timesteps=__lowercase      )











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

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

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




