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

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

							lowercase__		:Union[str, Any]   =    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")
							lowercase__		:str   =    model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							lowercase__		:Dict   =    model.predict(x_test)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




