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

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

				lowercase__							:							Dict									=			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")
				lowercase__							:							Union[str, Any]									=			model.fit(
				    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
				)
				lowercase__							:							str									=			model.predict(x_test)






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
																			raise ValueError(
																			    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
																			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=self.unet.dtype			)

													# set step values
													self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_sample

													__UpperCamelCase       =			(image / 2 + 0.5).clamp(0     ,	1			)
													__UpperCamelCase       =			image.cpu().permute(0     ,	2     ,	3     ,	1			).numpy()
													if output_type == "pil":
																			__UpperCamelCase       =			self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




