



from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block





@dataclass
class  __magic_name__   (							UpperCamelCase_						):
		lowercase      :       int          =42





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     Union[str, Any]=3				,		UpperCamelCase__      :     List[Any]=3				,		UpperCamelCase__      :     Union[str, Any]=("DownEncoderBlock2D",)				,		UpperCamelCase__      :     str=(64,)				,		UpperCamelCase__      :     Optional[int]=2				,		UpperCamelCase__      :     Union[str, Any]=32				,		UpperCamelCase__      :     Any="silu"				,		UpperCamelCase__      :     Optional[Any]=True				,		)				->					str:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     layers_per_block

								UpperCAmelCase         =     torch.nn.Convad(
								    __A				,		block_out_channels[0]				,		kernel_size=3				,		stride=1				,		padding=1				,		)

								UpperCAmelCase         =     None
								UpperCAmelCase         =     nn.ModuleList([]      )

								# down
								UpperCAmelCase         =     block_out_channels[0]
								for i, down_block_type in enumerate(__A      ):
														UpperCAmelCase         =     output_channel
														UpperCAmelCase         =     block_out_channels[i]
														UpperCAmelCase         =     i == len(__A      ) - 1

														UpperCAmelCase         =     get_down_block(
														    __A				,		num_layers=self.layers_per_block				,		in_channels=__A				,		out_channels=__A				,		add_downsample=not is_final_block				,		resnet_eps=1e-6				,		downsample_padding=0				,		resnet_act_fn=__A				,		resnet_groups=__A				,		attention_head_dim=__A				,		temb_channels=__A				,		)
														self.down_blocks.append(__A      )

								# mid
								UpperCAmelCase         =     UNetMidBlockaD(
								    in_channels=block_out_channels[-1]				,		resnet_eps=1e-6				,		resnet_act_fn=__A				,		output_scale_factor=1				,		resnet_time_scale_shift="default"				,		attention_head_dim=block_out_channels[-1]				,		resnet_groups=__A				,		temb_channels=__A				,		)

								# out
								UpperCAmelCase         =     nn.GroupNorm(num_channels=block_out_channels[-1]				,		num_groups=__A				,		eps=1e-6      )
								UpperCAmelCase         =     nn.SiLU()

								UpperCAmelCase         =     2 * out_channels if double_z else out_channels
								UpperCAmelCase         =     nn.Convad(block_out_channels[-1]				,		__A				,		3				,		padding=1      )

								UpperCAmelCase         =     False







		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     x
								UpperCAmelCase         =     self.conv_in(__A      )

								if self.training and self.gradient_checkpointing:

														def create_custom_forward(UpperCamelCase__      :     Optional[int]      ):
																				def custom_forward(*UpperCamelCase__      :     Tuple      ):
																										return module(*__A      )

																				return custom_forward

														# down
														if is_torch_version(">="				,		"1.11.0"      ):
																				for down_block in self.down_blocks:
																										UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(__A      )				,		__A				,		use_reentrant=__A      )
																				# middle
																				UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block      )				,		__A				,		use_reentrant=__A      )
														else:
																				for down_block in self.down_blocks:
																										UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(create_custom_forward(__A      )				,		__A      )
																				# middle
																				UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block      )				,		__A      )

								else:
														# down
														for down_block in self.down_blocks:
																				UpperCAmelCase         =     down_block(__A      )

														# middle
														UpperCAmelCase         =     self.mid_block(__A      )

								# post-process
								UpperCAmelCase         =     self.conv_norm_out(__A      )
								UpperCAmelCase         =     self.conv_act(__A      )
								UpperCAmelCase         =     self.conv_out(__A      )

								return sample





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     Optional[int]=3				,		UpperCamelCase__      :     List[str]=3				,		UpperCamelCase__      :     str=("UpDecoderBlock2D",)				,		UpperCamelCase__      :     List[str]=(64,)				,		UpperCamelCase__      :     List[str]=2				,		UpperCamelCase__      :     str=32				,		UpperCamelCase__      :     int="silu"				,		UpperCamelCase__      :     Tuple="group"				,		)				->					List[str]:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     layers_per_block

								UpperCAmelCase         =     nn.Convad(
								    __A				,		block_out_channels[-1]				,		kernel_size=3				,		stride=1				,		padding=1				,		)

								UpperCAmelCase         =     None
								UpperCAmelCase         =     nn.ModuleList([]      )

								UpperCAmelCase         =     in_channels if norm_type == "spatial" else None

								# mid
								UpperCAmelCase         =     UNetMidBlockaD(
								    in_channels=block_out_channels[-1]				,		resnet_eps=1e-6				,		resnet_act_fn=__A				,		output_scale_factor=1				,		resnet_time_scale_shift="default" if norm_type == "group" else norm_type				,		attention_head_dim=block_out_channels[-1]				,		resnet_groups=__A				,		temb_channels=__A				,		)

								# up
								UpperCAmelCase         =     list(reversed(__A      )      )
								UpperCAmelCase         =     reversed_block_out_channels[0]
								for i, up_block_type in enumerate(__A      ):
														UpperCAmelCase         =     output_channel
														UpperCAmelCase         =     reversed_block_out_channels[i]

														UpperCAmelCase         =     i == len(__A      ) - 1

														UpperCAmelCase         =     get_up_block(
														    __A				,		num_layers=self.layers_per_block + 1				,		in_channels=__A				,		out_channels=__A				,		prev_output_channel=__A				,		add_upsample=not is_final_block				,		resnet_eps=1e-6				,		resnet_act_fn=__A				,		resnet_groups=__A				,		attention_head_dim=__A				,		temb_channels=__A				,		resnet_time_scale_shift=__A				,		)
														self.up_blocks.append(__A      )
														UpperCAmelCase         =     output_channel

								# out
								if norm_type == "spatial":
														UpperCAmelCase         =     SpatialNorm(block_out_channels[0]				,		__A      )
								else:
														UpperCAmelCase         =     nn.GroupNorm(num_channels=block_out_channels[0]				,		num_groups=__A				,		eps=1e-6      )
								UpperCAmelCase         =     nn.SiLU()
								UpperCAmelCase         =     nn.Convad(block_out_channels[0]				,		__A				,		3				,		padding=1      )

								UpperCAmelCase         =     False







		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     int=None      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     z
								UpperCAmelCase         =     self.conv_in(__A      )

								UpperCAmelCase         =     next(iter(self.up_blocks.parameters()      )      ).dtype
								if self.training and self.gradient_checkpointing:

														def create_custom_forward(UpperCamelCase__      :     Optional[int]      ):
																				def custom_forward(*UpperCamelCase__      :     Tuple      ):
																										return module(*__A      )

																				return custom_forward

														if is_torch_version(">="				,		"1.11.0"      ):
																				# middle
																				UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block      )				,		__A				,		__A				,		use_reentrant=__A      )
																				UpperCAmelCase         =     sample.to(__A      )

																				# up
																				for up_block in self.up_blocks:
																										UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(__A      )				,		__A				,		__A				,		use_reentrant=__A      )
														else:
																				# middle
																				UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block      )				,		__A				,		__A      )
																				UpperCAmelCase         =     sample.to(__A      )

																				# up
																				for up_block in self.up_blocks:
																										UpperCAmelCase         =     torch.utils.checkpoint.checkpoint(create_custom_forward(__A      )				,		__A				,		__A      )
								else:
														# middle
														UpperCAmelCase         =     self.mid_block(__A				,		__A      )
														UpperCAmelCase         =     sample.to(__A      )

														# up
														for up_block in self.up_blocks:
																				UpperCAmelCase         =     up_block(__A				,		__A      )

        # post-process
								if latent_embeds is None:
														UpperCAmelCase         =     self.conv_norm_out(__A      )
								else:
														UpperCAmelCase         =     self.conv_norm_out(__A				,		__A      )
								UpperCAmelCase         =     self.conv_act(__A      )
								UpperCAmelCase         =     self.conv_out(__A      )

								return sample





class  __magic_name__   (							nn.Module						):






		def __init__(						self      :     Any				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Optional[Any]=None				,		UpperCamelCase__      :     Union[str, Any]="random"				,		UpperCamelCase__      :     str=False				,		UpperCamelCase__      :     Any=True      )				->					List[Any]:
								'''simple docstring'''







								super().__init__()
								UpperCAmelCase         =     n_e
								UpperCAmelCase         =     vq_embed_dim
								UpperCAmelCase         =     beta
								UpperCAmelCase         =     legacy

								UpperCAmelCase         =     nn.Embedding(self.n_e				,		self.vq_embed_dim      )
								self.embedding.weight.data.uniform_(-1.0 / self.n_e				,		1.0 / self.n_e      )

								UpperCAmelCase         =     remap
								if self.remap is not None:
														self.register_buffer("used"				,		torch.tensor(np.load(self.remap      )      )      )
														UpperCAmelCase         =     self.used.shape[0]
														UpperCAmelCase         =     unknown_index  # "random" or "extra" or integer
														if self.unknown_index == "extra":
																				UpperCAmelCase         =     self.re_embed
																				UpperCAmelCase         =     self.re_embed + 1
														print(
														    F'Remapping {self.n_e} indices to {self.re_embed} indices. '
														    F'Using {self.unknown_index} for unknown indices.'      )
								else:
														UpperCAmelCase         =     n_e

								UpperCAmelCase         =     sane_index_shape






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     inds.shape
								assert len(__A      ) > 1
								UpperCAmelCase         =     inds.reshape(ishape[0]				,		-1      )
								UpperCAmelCase         =     self.used.to(__A      )
								UpperCAmelCase         =     (inds[:, :, None] == used[None, None, ...]).long()
								UpperCAmelCase         =     match.argmax(-1      )
								UpperCAmelCase         =     match.sum(2      ) < 1
								if self.unknown_index == "random":
														UpperCAmelCase         =     torch.randint(0				,		self.re_embed				,		size=new[unknown].shape      ).to(device=new.device      )
								else:
														UpperCAmelCase         =     self.unknown_index
								return new.reshape(__A      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     int      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     inds.shape
								assert len(__A      ) > 1
								UpperCAmelCase         =     inds.reshape(ishape[0]				,		-1      )
								UpperCAmelCase         =     self.used.to(__A      )
								if self.re_embed > self.used.shape[0]:  # extra token
														UpperCAmelCase         =     0  # simply set to zero
								UpperCAmelCase         =     torch.gather(used[None, :][inds.shape[0] * [0], :]				,		1				,		__A      )
								return back.reshape(__A      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     z.permute(0				,		2				,		3				,		1      ).contiguous()
								UpperCAmelCase         =     z.view(-1				,		self.vq_embed_dim      )

								# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
								UpperCAmelCase         =     torch.argmin(torch.cdist(__A				,		self.embedding.weight      )				,		dim=1      )

								UpperCAmelCase         =     self.embedding(__A      ).view(z.shape      )
								UpperCAmelCase         =     None
								UpperCAmelCase         =     None

								# compute loss for embedding
								if not self.legacy:
														UpperCAmelCase         =     self.beta * torch.mean((z_q.detach() - z) ** 2      ) + torch.mean((z_q - z.detach()) ** 2      )
								else:
														UpperCAmelCase         =     torch.mean((z_q.detach() - z) ** 2      ) + self.beta * torch.mean((z_q - z.detach()) ** 2      )

								# preserve gradients
								UpperCAmelCase         =     z + (z_q - z).detach()

								# reshape back to match original input shape
								UpperCAmelCase         =     z_q.permute(0				,		3				,		1				,		2      ).contiguous()

								if self.remap is not None:
														UpperCAmelCase         =     min_encoding_indices.reshape(z.shape[0]				,		-1      )  # add batch axis
														UpperCAmelCase         =     self.remap_to_used(__A      )
														UpperCAmelCase         =     min_encoding_indices.reshape(-1				,		1      )  # flatten

								if self.sane_index_shape:
														UpperCAmelCase         =     min_encoding_indices.reshape(z_q.shape[0]				,		z_q.shape[2]				,		z_q.shape[3]      )

								return z_q, loss, (perplexity, min_encodings, min_encoding_indices)







		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     int      )				->					List[str]:
								'''simple docstring'''







								if self.remap is not None:
														UpperCAmelCase         =     indices.reshape(shape[0]				,		-1      )  # add batch axis
														UpperCAmelCase         =     self.unmap_to_all(__A      )
														UpperCAmelCase         =     indices.reshape(-1      )  # flatten again

								# get quantized latent vectors
								UpperCAmelCase         =     self.embedding(__A      )

								if shape is not None:
														UpperCAmelCase         =     z_q.view(__A      )
														# reshape back to match original input shape
														UpperCAmelCase         =     z_q.permute(0				,		3				,		1				,		2      ).contiguous()

								return z_q





class  __magic_name__   (							UpperCamelCase_						):






		def __init__(						self      :     Dict				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Union[str, Any]=False      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     parameters
								UpperCAmelCase         =     torch.chunk(__A				,		2				,		dim=1      )
								UpperCAmelCase         =     torch.clamp(self.logvar				,		-30.0				,		20.0      )
								UpperCAmelCase         =     deterministic
								UpperCAmelCase         =     torch.exp(0.5 * self.logvar      )
								UpperCAmelCase         =     torch.exp(self.logvar      )
								if self.deterministic:
														UpperCAmelCase         =     torch.zeros_like(
														    self.mean				,		device=self.parameters.device				,		dtype=self.parameters.dtype      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Optional[torch.Generator] = None      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     randn_tensor(
								    self.mean.shape				,		generator=__A				,		device=self.parameters.device				,		dtype=self.parameters.dtype      )
								UpperCAmelCase         =     self.mean + self.std * sample
								return x






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[int]=None      )				->					Union[str, Any]:
								'''simple docstring'''







								if self.deterministic:
														return torch.Tensor([0.0]      )
								else:
														if other is None:
																				return 0.5 * torch.sum(torch.pow(self.mean				,		2      ) + self.var - 1.0 - self.logvar				,		dim=[1, 2, 3]      )
														else:
																				return 0.5 * torch.sum(
																				    torch.pow(self.mean - other.mean				,		2      ) / other.var
																				    + self.var / other.var
																				    - 1.0
																				    - self.logvar
																				    + other.logvar				,		dim=[1, 2, 3]				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Any=[1, 2, 3]      )				->					List[str]:
								'''simple docstring'''







								if self.deterministic:
														return torch.Tensor([0.0]      )
								UpperCAmelCase         =     np.log(2.0 * np.pi      )
								return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean				,		2      ) / self.var				,		dim=__A      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								return self.mean








def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     int(lowerCamelCase_    )

						# Initialize Result
						UpperCAmelCase         =     []

						# Traverse through all denomination
						for denomination in reversed(lowerCamelCase_    ):
												# Find denominations
												while int(lowerCamelCase_    ) >= int(lowerCamelCase_    ):
																		total_value -= int(lowerCamelCase_    )
																		answer.append(lowerCamelCase_    )  # Append the "answers" array

						return answer


# Driver Code
if __name__ == "__main__":
		__lowerCamelCase     :							Optional[int]  							=				[]
		__lowerCamelCase     :							Dict  							=				"0"

		if (
		    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
		    == "y"
		):
				__lowerCamelCase     :							List[Any]  							=				int(input("Enter the number of denominations you want to add: ").strip())

				for i in range(0, n):
						denominations.append(int(input(F'''Denomination {i}: ''').strip()))
				__lowerCamelCase     :							str  							=				input("Enter the change you want to make in Indian Currency: ").strip()
		else:
				# All denominations of Indian Currency if user does not enter
				__lowerCamelCase     :							Optional[int]  							=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
				__lowerCamelCase     :							Optional[int]  							=				input("Enter the change you want to make: ").strip()

		if int(value) == 0 or int(value) < 0:
				print("The total value cannot be zero or negative.")

		else:
				print(F'''Following is minimal change for {value}: ''')
				__lowerCamelCase     :							Union[str, Any]  							=				find_minimum_change(denominations, value)
				# Print result
				for i in range(len(answer)):
						print(answer[i], end=" ")



