





'''simple docstring'''







import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


snake_case_     :       Optional[int]						=  logging.get_logger(__name__)

enable_full_determinism()


class 					__a      (lowercase__       ,		lowercase__       ,		unittest.TestCase     ):
							__a					:   Union[str, Any]									=       UNetaDModel
							__a					:   str									=       'sample'

							@property
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  4
													UpperCAmelCase_			:      Optional[int]     						=  3
													UpperCAmelCase_			:      Union[str, Any]     						=  (32, 32)

													UpperCAmelCase_			:      str     						=  floats_tensor((batch_size, num_channels) + sizes   ).to(__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  torch.tensor([10]   ).to(__magic_name__   )

													return {"sample": noise, "timestep": time_step}

							@property
							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Dict:







													"""simple docstring"""
													return (3, 32, 32)

							@property
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													return (3, 32, 32)



							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  {
													    '''block_out_channels''': (32, 64),
													    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
													    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
													    '''attention_head_dim''': 3,
													    '''out_channels''': 3,
													    '''in_channels''': 3,
													    '''layers_per_block''': 2,
													    '''sample_size''': 32,
													}
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_input
													return init_dict, inputs_dict






class 					__a      (lowercase__       ,		lowercase__       ,		unittest.TestCase     ):
							__a					:   str									=       UNetaDModel
							__a					:   Tuple									=       'sample'

							@property
							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  4
													UpperCAmelCase_			:      str     						=  4
													UpperCAmelCase_			:      Optional[Any]     						=  (32, 32)

													UpperCAmelCase_			:      Dict     						=  floats_tensor((batch_size, num_channels) + sizes   ).to(__magic_name__   )
													UpperCAmelCase_			:      int     						=  torch.tensor([10]   ).to(__magic_name__   )

													return {"sample": noise, "timestep": time_step}

							@property
							def     UpperCAmelCase__	(							self							:     int   )					->       Optional[Any]:







													"""simple docstring"""
													return (4, 32, 32)

							@property
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													return (4, 32, 32)

							def     UpperCAmelCase__	(							self							:     Dict   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  {
													    '''sample_size''': 32,
													    '''in_channels''': 4,
													    '''out_channels''': 4,
													    '''layers_per_block''': 2,
													    '''block_out_channels''': (32, 64),
													    '''attention_head_dim''': 32,
													    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
													    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
													}
													UpperCAmelCase_			:      Optional[Any]     						=  self.dummy_input
													return init_dict, inputs_dict

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       ,     output_loading_info=__magic_name__   )

													self.assertIsNotNone(__magic_name__   )
													self.assertEqual(len(loading_info['''missing_keys''']   )       ,     0   )

													model.to(__magic_name__   )
													UpperCAmelCase_			:      int     						=  model(**self.dummy_input   ).sample

													assert image is not None, "Make sure output is not None"

							@unittest.skipIf(torch_device != '''cuda'''       ,     '''This test is supposed to run on GPU'''   )
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       ,     output_loading_info=__magic_name__   )
													model.to(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  model(**self.dummy_input   ).sample

													assert image is not None, "Make sure output is not None"

							@unittest.skipIf(torch_device != '''cuda'''       ,     '''This test is supposed to run on GPU'''   )
							def     UpperCAmelCase__	(							self							:     Any   )					->       int:







													"""simple docstring"""
													# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
													UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       ,     output_loading_info=__magic_name__   )
													model_accelerate.to(__magic_name__   )
													model_accelerate.eval()

													UpperCAmelCase_			:      str     						=  torch.randn(
													    1       ,     model_accelerate.config.in_channels       ,     model_accelerate.config.sample_size       ,     model_accelerate.config.sample_size       ,     generator=torch.manual_seed(0   )       ,     )
													UpperCAmelCase_			:      str     						=  noise.to(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  torch.tensor([10] * noise.shape[0]   ).to(__magic_name__   )

													UpperCAmelCase_			:      Optional[int]     						=  model_accelerate(__magic_name__       ,     __magic_name__   )['''sample''']

													# two models don't need to stay in the device at the same time
													del model_accelerate
													torch.cuda.empty_cache()
													gc.collect()

													UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  UNetaDModel.from_pretrained(
													    '''fusing/unet-ldm-dummy-update'''       ,     output_loading_info=__magic_name__       ,     low_cpu_mem_usage=__magic_name__   )
													model_normal_load.to(__magic_name__   )
													model_normal_load.eval()
													UpperCAmelCase_			:      List[Any]     						=  model_normal_load(__magic_name__       ,     __magic_name__   )['''sample''']

													assert torch_all_close(__magic_name__       ,     __magic_name__       ,     rtol=1E-3   )



							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   )
													model.eval()
													model.to(__magic_name__   )

													UpperCAmelCase_			:      str     						=  torch.randn(
													    1       ,     model.config.in_channels       ,     model.config.sample_size       ,     model.config.sample_size       ,     generator=torch.manual_seed(0   )       ,     )
													UpperCAmelCase_			:      List[Any]     						=  noise.to(__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  torch.tensor([10] * noise.shape[0]   ).to(__magic_name__   )

													with torch.no_grad():
																			UpperCAmelCase_			:      List[Any]     						=  model(__magic_name__       ,     __magic_name__   ).sample

													UpperCAmelCase_			:      Dict     						=  output[0, -1, -3:, -3:].flatten().cpu()
													# fmt: off
													UpperCAmelCase_			:      str     						=  torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]   )
													# fmt: on

													self.assertTrue(torch_all_close(__magic_name__       ,     __magic_name__       ,     rtol=1E-3   )   )






class 					__a      (lowercase__       ,		lowercase__       ,		unittest.TestCase     ):
							__a					:   Optional[int]									=       UNetaDModel
							__a					:   str									=       'sample'

							@property
							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Optional[Any]=(32, 32)   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  4
													UpperCAmelCase_			:      str     						=  3

													UpperCAmelCase_			:      Optional[int]     						=  floats_tensor((batch_size, num_channels) + sizes   ).to(__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  torch.tensor(batch_size * [10]   ).to(dtype=torch.intaa       ,     device=__magic_name__   )

													return {"sample": noise, "timestep": time_step}

							@property
							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Union[str, Any]:







													"""simple docstring"""
													return (3, 32, 32)

							@property
							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Any:







													"""simple docstring"""
													return (3, 32, 32)

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  {
													    '''block_out_channels''': [32, 64, 64, 64],
													    '''in_channels''': 3,
													    '''layers_per_block''': 1,
													    '''out_channels''': 3,
													    '''time_embedding_type''': '''fourier''',
													    '''norm_eps''': 1E-6,
													    '''mid_block_scale_factor''': math.sqrt(2.0   ),
													    '''norm_num_groups''': None,
													    '''down_block_types''': [
													        '''SkipDownBlock2D''',
													        '''AttnSkipDownBlock2D''',
													        '''SkipDownBlock2D''',
													        '''SkipDownBlock2D''',
													    ],
													    '''up_block_types''': [
													        '''SkipUpBlock2D''',
													        '''SkipUpBlock2D''',
													        '''AttnSkipUpBlock2D''',
													        '''SkipUpBlock2D''',
													    ],
													}
													UpperCAmelCase_			:      Union[str, Any]     						=  self.dummy_input
													return init_dict, inputs_dict

							@slow
							def     UpperCAmelCase__	(							self							:     int   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''       ,     output_loading_info=__magic_name__   )
													self.assertIsNotNone(__magic_name__   )
													self.assertEqual(len(loading_info['''missing_keys''']   )       ,     0   )

													model.to(__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  self.dummy_input
													UpperCAmelCase_			:      str     						=  floats_tensor((4, 3) + (2_56, 2_56)   ).to(__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  noise
													UpperCAmelCase_			:      List[str]     						=  model(**__magic_name__   )

													assert image is not None, "Make sure output is not None"

							@slow
							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   )
													model.to(__magic_name__   )

													UpperCAmelCase_			:      Any     						=  4
													UpperCAmelCase_			:      List[str]     						=  3
													UpperCAmelCase_			:      int     						=  (2_56, 2_56)

													UpperCAmelCase_			:      Optional[Any]     						=  torch.ones((batch_size, num_channels) + sizes   ).to(__magic_name__   )
													UpperCAmelCase_			:      Any     						=  torch.tensor(batch_size * [1E-4]   ).to(__magic_name__   )

													with torch.no_grad():
																			UpperCAmelCase_			:      Tuple     						=  model(__magic_name__       ,     __magic_name__   ).sample

													UpperCAmelCase_			:      Dict     						=  output[0, -3:, -3:, -1].flatten().cpu()
													# fmt: off
													UpperCAmelCase_			:      int     						=  torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]   )
													# fmt: on

													self.assertTrue(torch_all_close(__magic_name__       ,     __magic_name__       ,     rtol=1E-2   )   )

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







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''   )
													model.to(__magic_name__   )

													UpperCAmelCase_			:      Optional[int]     						=  4
													UpperCAmelCase_			:      Tuple     						=  3
													UpperCAmelCase_			:      Optional[Any]     						=  (32, 32)

													UpperCAmelCase_			:      Optional[int]     						=  torch.ones((batch_size, num_channels) + sizes   ).to(__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  torch.tensor(batch_size * [1E-4]   ).to(__magic_name__   )

													with torch.no_grad():
																			UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     __magic_name__   ).sample

													UpperCAmelCase_			:      int     						=  output[0, -3:, -3:, -1].flatten().cpu()
													# fmt: off
													UpperCAmelCase_			:      int     						=  torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]   )
													# fmt: on

													self.assertTrue(torch_all_close(__magic_name__       ,     __magic_name__       ,     rtol=1E-2   )   )



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







													"""simple docstring"""
													# not required for this model
													pass












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

					snake_case_     :       List[Any]						=  Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





