from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder



@dataclass
class     _A   (    __UpperCamelCase							):
		SCREAMING_SNAKE_CASE_	:	"DiagonalGaussianDistribution"



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):
		SCREAMING_SNAKE_CASE_	:	List[str]						=True






		@register_to_config
		def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 3   ,				SCREAMING_SNAKE_CASE_ = 3   ,				SCREAMING_SNAKE_CASE_ = ("DownEncoderBlock2D",)   ,				SCREAMING_SNAKE_CASE_ = ("UpDecoderBlock2D",)   ,				SCREAMING_SNAKE_CASE_ = (64,)   ,				SCREAMING_SNAKE_CASE_ = 1   ,				SCREAMING_SNAKE_CASE_ = "silu"   ,				SCREAMING_SNAKE_CASE_ = 4   ,				SCREAMING_SNAKE_CASE_ = 32   ,				SCREAMING_SNAKE_CASE_ = 32   ,				SCREAMING_SNAKE_CASE_ = 0.18215   ,				)		->      Optional[int]:





							'''simple docstring'''





							super().__init__()

							# pass init params to Encoder
							UpperCamelCase__										=      Encoder(
							    in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				down_block_types=SCREAMING_SNAKE_CASE_   ,				block_out_channels=SCREAMING_SNAKE_CASE_   ,				layers_per_block=SCREAMING_SNAKE_CASE_   ,				act_fn=SCREAMING_SNAKE_CASE_   ,				norm_num_groups=SCREAMING_SNAKE_CASE_   ,				double_z=SCREAMING_SNAKE_CASE_   ,				)

							# pass init params to Decoder
							UpperCamelCase__										=      Decoder(
							    in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				up_block_types=SCREAMING_SNAKE_CASE_   ,				block_out_channels=SCREAMING_SNAKE_CASE_   ,				layers_per_block=SCREAMING_SNAKE_CASE_   ,				norm_num_groups=SCREAMING_SNAKE_CASE_   ,				act_fn=SCREAMING_SNAKE_CASE_   ,				)

							UpperCamelCase__										=      nn.Convad(2 * latent_channels   ,				2 * latent_channels   ,				1	)
							UpperCamelCase__										=      nn.Convad(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				1	)

							UpperCamelCase__										=      False
							UpperCamelCase__										=      False

							# only relevant if vae tiling is enabled
							UpperCamelCase__										=      self.config.sample_size
							UpperCamelCase__										=      (
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size   ,				(list, tuple)	)
							    else self.config.sample_size
							)
							UpperCamelCase__										=      int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
							UpperCamelCase__										=      0.25






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      Dict:





							'''simple docstring'''





							if isinstance(SCREAMING_SNAKE_CASE_   ,				(Encoder, Decoder)	):
												UpperCamelCase__										=      value






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[str, Any]:





							'''simple docstring'''





							UpperCamelCase__										=      use_tiling






		def 		_a					(self	)		->      int:





							'''simple docstring'''





							self.enable_tiling(SCREAMING_SNAKE_CASE_	)






		def 		_a					(self	)		->      Any:





							'''simple docstring'''





							UpperCamelCase__										=      True






		def 		_a					(self	)		->      List[Any]:





							'''simple docstring'''





							UpperCamelCase__										=      False






		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def 		_a					(self	)		->      Dict[str, AttentionProcessor]:





							'''simple docstring'''





							UpperCamelCase__										=      {}

							def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
												if hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_processor'''	):
																	UpperCamelCase__										=      module.processor

												for sub_name, child in module.named_children():
																	fn_recursive_add_processors(F"{name}.{sub_name}"   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

												return processors

							for name, module in self.named_children():
												fn_recursive_add_processors(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

							return processors






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





							'''simple docstring'''





							UpperCamelCase__										=      len(self.attn_processors.keys()	)

							if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) != count:
												raise ValueError(
												    F"A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE_	)} does not match the"
												    F" number of attention layers: {count}. Please make sure to pass {count} processor classes."	)

							def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
												if hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_processor'''	):
																	if not isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
																						module.set_processor(SCREAMING_SNAKE_CASE_	)
																	else:
																						module.set_processor(processor.pop(F"{name}.processor"	)	)

												for sub_name, child in module.named_children():
																	fn_recursive_attn_processor(F"{name}.{sub_name}"   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

							for name, module in self.named_children():
												fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






		def 		_a					(self	)		->      Tuple:





							'''simple docstring'''





							self.set_attn_processor(AttnProcessor()	)






		@apply_forward_hook
		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      AutoencoderKLOutput:





							'''simple docstring'''





							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
												return self.tiled_encode(SCREAMING_SNAKE_CASE_   ,				return_dict=SCREAMING_SNAKE_CASE_	)

							if self.use_slicing and x.shape[0] > 1:
												UpperCamelCase__										=      [self.encoder(SCREAMING_SNAKE_CASE_	) for x_slice in x.split(1	)]
												UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_	)
							else:
												UpperCamelCase__										=      self.encoder(SCREAMING_SNAKE_CASE_	)

							UpperCamelCase__										=      self.quant_conv(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE_	)

							if not return_dict:
												return (posterior,)

							return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





							'''simple docstring'''





							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
												return self.tiled_decode(SCREAMING_SNAKE_CASE_   ,				return_dict=SCREAMING_SNAKE_CASE_	)

							UpperCamelCase__										=      self.post_quant_conv(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      self.decoder(SCREAMING_SNAKE_CASE_	)

							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






		@apply_forward_hook
		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





							'''simple docstring'''





							if self.use_slicing and z.shape[0] > 1:
												UpperCamelCase__										=      [self._decode(SCREAMING_SNAKE_CASE_	).sample for z_slice in z.split(1	)]
												UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_	)
							else:
												UpperCamelCase__										=      self._decode(SCREAMING_SNAKE_CASE_	).sample

							if not return_dict:
												return (decoded,)

							return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





							'''simple docstring'''





							UpperCamelCase__										=      min(a.shape[2]   ,				b.shape[2]   ,				SCREAMING_SNAKE_CASE_	)
							for y in range(SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





							'''simple docstring'''





							UpperCamelCase__										=      min(a.shape[3]   ,				b.shape[3]   ,				SCREAMING_SNAKE_CASE_	)
							for x in range(SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      AutoencoderKLOutput:





							'''simple docstring'''





							UpperCamelCase__										=      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
							UpperCamelCase__										=      int(self.tile_latent_min_size * self.tile_overlap_factor	)
							UpperCamelCase__										=      self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							UpperCamelCase__										=      []
							for i in range(0   ,				x.shape[2]   ,				SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      []
												for j in range(0   ,				x.shape[3]   ,				SCREAMING_SNAKE_CASE_	):
																	UpperCamelCase__										=      x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																	UpperCamelCase__										=      self.encoder(SCREAMING_SNAKE_CASE_	)
																	UpperCamelCase__										=      self.quant_conv(SCREAMING_SNAKE_CASE_	)
																	row.append(SCREAMING_SNAKE_CASE_	)
												rows.append(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      []
							for i, row in enumerate(SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      []
												for j, tile in enumerate(SCREAMING_SNAKE_CASE_	):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																						UpperCamelCase__										=      self.blend_v(rows[i - 1][j]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
																	if j > 0:
																						UpperCamelCase__										=      self.blend_h(row[j - 1]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
																	result_row.append(tile[:, :, :row_limit, :row_limit]	)
												result_rows.append(torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=3	)	)

							UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=2	)
							UpperCamelCase__										=      DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE_	)

							if not return_dict:
												return (posterior,)

							return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





							'''simple docstring'''





							UpperCamelCase__										=      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
							UpperCamelCase__										=      int(self.tile_sample_min_size * self.tile_overlap_factor	)
							UpperCamelCase__										=      self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							UpperCamelCase__										=      []
							for i in range(0   ,				z.shape[2]   ,				SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      []
												for j in range(0   ,				z.shape[3]   ,				SCREAMING_SNAKE_CASE_	):
																	UpperCamelCase__										=      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																	UpperCamelCase__										=      self.post_quant_conv(SCREAMING_SNAKE_CASE_	)
																	UpperCamelCase__										=      self.decoder(SCREAMING_SNAKE_CASE_	)
																	row.append(SCREAMING_SNAKE_CASE_	)
												rows.append(SCREAMING_SNAKE_CASE_	)
							UpperCamelCase__										=      []
							for i, row in enumerate(SCREAMING_SNAKE_CASE_	):
												UpperCamelCase__										=      []
												for j, tile in enumerate(SCREAMING_SNAKE_CASE_	):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																						UpperCamelCase__										=      self.blend_v(rows[i - 1][j]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
																	if j > 0:
																						UpperCamelCase__										=      self.blend_h(row[j - 1]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
																	result_row.append(tile[:, :, :row_limit, :row_limit]	)
												result_rows.append(torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=3	)	)

							UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=2	)
							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Union[DecoderOutput, torch.FloatTensor]:





							'''simple docstring'''





							UpperCamelCase__										=      sample
							UpperCamelCase__										=      self.encode(SCREAMING_SNAKE_CASE_	).latent_dist
							if sample_posterior:
												UpperCamelCase__										=      posterior.sample(generator=SCREAMING_SNAKE_CASE_	)
							else:
												UpperCamelCase__										=      posterior.mode()
							UpperCamelCase__										=      self.decode(SCREAMING_SNAKE_CASE_	).sample

							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)




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

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

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



