






'''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


_A     :      Dict   					=logging.get_logger(__name__)

enable_full_determinism()

class _lowercase     (    _lowercase    ,				_lowercase    ,				unittest.TestCase		):
							a    				=	UNetaDModel
							a    				=	"""sample"""




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     int	  =	4
														lowerCamelCase__ :     str	  =	3
														lowerCamelCase__ :     Optional[int]	  =	(32, 32)

														lowerCamelCase__ :     int	  =	floats_tensor((batch_size, num_channels) + sizes					).to(UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	torch.tensor([10]					).to(UpperCamelCase__					)

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




							@property
							def 		lowerCamelCase_    (							self:    int					):
														return (3, 32, 32)




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														return (3, 32, 32)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Optional[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,
														}
														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_input
														return init_dict, inputs_dict



class _lowercase     (    _lowercase    ,				_lowercase    ,				unittest.TestCase		):
							a    				=	UNetaDModel
							a    				=	"""sample"""




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Tuple	  =	4
														lowerCamelCase__ :     int	  =	4
														lowerCamelCase__ :     Tuple	  =	(32, 32)

														lowerCamelCase__ :     Optional[int]	  =	floats_tensor((batch_size, num_channels) + sizes					).to(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	torch.tensor([10]					).to(UpperCamelCase__					)

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




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return (4, 32, 32)




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return (4, 32, 32)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Tuple	  =	{
														    """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"""),
														}
														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_input
														return init_dict, inputs_dict




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,							output_loading_info=UpperCamelCase__					)

														self.assertIsNotNone(UpperCamelCase__					)
														self.assertEqual(len(loading_info["""missing_keys"""]					)      ,							0					)

														model.to(UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	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 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,							output_loading_info=UpperCamelCase__					)
														model.to(UpperCamelCase__					)
														lowerCamelCase__ :     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 		lowerCamelCase_    (							self:    str					):
														# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,							output_loading_info=UpperCamelCase__					)
														model_accelerate.to(UpperCamelCase__					)
														model_accelerate.eval()

														lowerCamelCase__ :     str	  =	torch.randn(
														    1      ,							model_accelerate.config.in_channels      ,							model_accelerate.config.sample_size      ,							model_accelerate.config.sample_size      ,							generator=torch.manual_seed(0					)      ,							)
														lowerCamelCase__ :     List[Any]	  =	noise.to(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	torch.tensor([10] * noise.shape[0]					).to(UpperCamelCase__					)

														lowerCamelCase__ :     List[str]	  =	model_accelerate(UpperCamelCase__      ,							UpperCamelCase__					)["""sample"""]

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

														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	UNetaDModel.from_pretrained(
														    """fusing/unet-ldm-dummy-update"""      ,							output_loading_info=UpperCamelCase__      ,							low_cpu_mem_usage=UpperCamelCase__					)
														model_normal_load.to(UpperCamelCase__					)
														model_normal_load.eval()
														lowerCamelCase__ :     str	  =	model_normal_load(UpperCamelCase__      ,							UpperCamelCase__					)["""sample"""]

														assert torch_all_close(UpperCamelCase__      ,							UpperCamelCase__      ,							rtol=1e-3					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Union[str, Any]	  =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""					)
														model.eval()
														model.to(UpperCamelCase__					)

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

														with torch.no_grad():
																					lowerCamelCase__ :     str	  =	model(UpperCamelCase__      ,							UpperCamelCase__					).sample

														lowerCamelCase__ :     Dict	  =	output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														lowerCamelCase__ :     Optional[Any]	  =	torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]					)
														# fmt: on

														self.assertTrue(torch_all_close(UpperCamelCase__      ,							UpperCamelCase__      ,							rtol=1e-3					)					)



class _lowercase     (    _lowercase    ,				_lowercase    ,				unittest.TestCase		):
							a    				=	UNetaDModel
							a    				=	"""sample"""




							@property
							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    List[Any]=(32, 32)					):
														lowerCamelCase__ :     Any	  =	4
														lowerCamelCase__ :     List[str]	  =	3

														lowerCamelCase__ :     List[str]	  =	floats_tensor((batch_size, num_channels) + sizes					).to(UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	torch.tensor(batch_size * [10]					).to(dtype=torch.intaa      ,							device=UpperCamelCase__					)

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




							@property
							def 		lowerCamelCase_    (							self:    int					):
														return (3, 32, 32)




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return (3, 32, 32)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Optional[int]	  =	{
														    """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""",
														    ],
														}
														lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_input
														return init_dict, inputs_dict




							@slow
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__  ,		lowerCamelCase__ :     List[Any]	  =	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""      ,							output_loading_info=UpperCamelCase__					)
														self.assertIsNotNone(UpperCamelCase__					)
														self.assertEqual(len(loading_info["""missing_keys"""]					)      ,							0					)

														model.to(UpperCamelCase__					)
														lowerCamelCase__ :     List[Any]	  =	self.dummy_input
														lowerCamelCase__ :     Union[str, Any]	  =	floats_tensor((4, 3) + (256, 256)					).to(UpperCamelCase__					)
														lowerCamelCase__ :     Union[str, Any]	  =	noise
														lowerCamelCase__ :     List[Any]	  =	model(**UpperCamelCase__					)

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




							@slow
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     int	  =	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""					)
														model.to(UpperCamelCase__					)

														lowerCamelCase__ :     Union[str, Any]	  =	4
														lowerCamelCase__ :     Any	  =	3
														lowerCamelCase__ :     Dict	  =	(256, 256)

														lowerCamelCase__ :     Optional[int]	  =	torch.ones((batch_size, num_channels) + sizes					).to(UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	torch.tensor(batch_size * [1e-4]					).to(UpperCamelCase__					)

														with torch.no_grad():
																					lowerCamelCase__ :     Optional[int]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					).sample

														lowerCamelCase__ :     Any	  =	output[0, -3:, -3:, -1].flatten().cpu()
														# fmt: off
														lowerCamelCase__ :     List[Any]	  =	torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]					)
														# fmt: on

														self.assertTrue(torch_all_close(UpperCamelCase__      ,							UpperCamelCase__      ,							rtol=1e-2					)					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Dict	  =	UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""					)
														model.to(UpperCamelCase__					)

														lowerCamelCase__ :     List[Any]	  =	4
														lowerCamelCase__ :     Optional[int]	  =	3
														lowerCamelCase__ :     List[str]	  =	(32, 32)

														lowerCamelCase__ :     int	  =	torch.ones((batch_size, num_channels) + sizes					).to(UpperCamelCase__					)
														lowerCamelCase__ :     Any	  =	torch.tensor(batch_size * [1e-4]					).to(UpperCamelCase__					)

														with torch.no_grad():
																					lowerCamelCase__ :     List[Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					).sample

														lowerCamelCase__ :     Optional[int]	  =	output[0, -3:, -3:, -1].flatten().cpu()
														# fmt: off
														lowerCamelCase__ :     str	  =	torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]					)
														# fmt: on

														self.assertTrue(torch_all_close(UpperCamelCase__      ,							UpperCamelCase__      ,							rtol=1e-2					)					)




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

			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])
			_A     :      List[Any]   					=np.array(train_x)
			_A     :      str   					=np.array(test_x)
			_A     :      List[Any]   					=np.array([list(i.ravel()) for i in train_y])
			_A     :      Any   					=np.array([list(i.ravel()) for i in test_y])

			_A     :      Optional[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''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

