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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class   __lowercase						(						__snake_case       ):
	_A       =   42




class   __lowercase						(						__snake_case   , __snake_case       ):






	@register_to_config
	def __init__(self				: List[Any]      ,	snake_case				: int = 6_5536      ,	snake_case				: Optional[int] = None      ,	snake_case				: int = 2      ,	snake_case				: int = 2      ,	snake_case				: int = 0      ,	snake_case				: str = "fourier"      ,	snake_case				: bool = True      ,	snake_case				: bool = False      ,	snake_case				: float = 0.0      ,	snake_case				: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,	snake_case				: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,	snake_case				: Tuple[str] = "UNetMidBlock1D"      ,	snake_case				: str = None      ,	snake_case				: Tuple[int] = (32, 32, 64)      ,	snake_case				: str = None      ,	snake_case				: int = 8      ,	snake_case				: int = 1      ,	snake_case				: bool = False      ,	) ->			int:
							super().__init__()
							_lowercase       :	Tuple           =   sample_size

							# time
							if time_embedding_type == "fourier":
													_lowercase       :	Optional[int]           =   GaussianFourierProjection(
													    embedding_size=8      ,	set_W_to_weight=snake_case      ,	log=snake_case      ,	flip_sin_to_cos=snake_case    )
													_lowercase       :	Optional[Any]           =   2 * block_out_channels[0]
							elif time_embedding_type == "positional":
													_lowercase       :	str           =   Timesteps(
													    block_out_channels[0]      ,	flip_sin_to_cos=snake_case      ,	downscale_freq_shift=snake_case    )
													_lowercase       :	Union[str, Any]           =   block_out_channels[0]

							if use_timestep_embedding:
													_lowercase       :	List[Any]           =   block_out_channels[0] * 4
													_lowercase       :	List[Any]           =   TimestepEmbedding(
													    in_channels=snake_case      ,	time_embed_dim=snake_case      ,	act_fn=snake_case      ,	out_dim=block_out_channels[0]      ,	)

							_lowercase       :	List[Any]           =   nn.ModuleList([]    )
							_lowercase       :	List[str]           =   None
							_lowercase       :	str           =   nn.ModuleList([]    )
							_lowercase       :	str           =   None

							# down
							_lowercase       :	Optional[Any]           =   in_channels
							for i, down_block_type in enumerate(snake_case    ):
													_lowercase       :	List[str]           =   output_channel
													_lowercase       :	str           =   block_out_channels[i]

													if i == 0:
																			input_channel += extra_in_channels

													_lowercase       :	List[str]           =   i == len(snake_case    ) - 1

													_lowercase       :	Dict           =   get_down_block(
													    snake_case      ,	num_layers=snake_case      ,	in_channels=snake_case      ,	out_channels=snake_case      ,	temb_channels=block_out_channels[0]      ,	add_downsample=not is_final_block or downsample_each_block      ,	)
													self.down_blocks.append(snake_case    )

							# mid
							_lowercase       :	Optional[Any]           =   get_mid_block(
							    snake_case      ,	in_channels=block_out_channels[-1]      ,	mid_channels=block_out_channels[-1]      ,	out_channels=block_out_channels[-1]      ,	embed_dim=block_out_channels[0]      ,	num_layers=snake_case      ,	add_downsample=snake_case      ,	)

							# up
							_lowercase       :	Optional[int]           =   list(reversed(snake_case    )    )
							_lowercase       :	Any           =   reversed_block_out_channels[0]
							if out_block_type is None:
													_lowercase       :	Optional[Any]           =   out_channels
							else:
													_lowercase       :	Any           =   block_out_channels[0]

							for i, up_block_type in enumerate(snake_case    ):
													_lowercase       :	int           =   output_channel
													_lowercase       :	int           =   (
													    reversed_block_out_channels[i + 1] if i < len(snake_case    ) - 1 else final_upsample_channels
													)

													_lowercase       :	int           =   i == len(snake_case    ) - 1

													_lowercase       :	Optional[int]           =   get_up_block(
													    snake_case      ,	num_layers=snake_case      ,	in_channels=snake_case      ,	out_channels=snake_case      ,	temb_channels=block_out_channels[0]      ,	add_upsample=not is_final_block      ,	)
													self.up_blocks.append(snake_case    )
													_lowercase       :	List[str]           =   output_channel

							# out
							_lowercase       :	str           =   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,	32    )
							_lowercase       :	Optional[Any]           =   get_out_block(
							    out_block_type=snake_case      ,	num_groups_out=snake_case      ,	embed_dim=block_out_channels[0]      ,	out_channels=snake_case      ,	act_fn=snake_case      ,	fc_dim=block_out_channels[-1] // 4      ,	)






	def       _a(self				: Any      ,	snake_case				: torch.FloatTensor      ,	snake_case				: Union[torch.Tensor, float, int]      ,	snake_case				: bool = True      ,	) ->			Union[UNetaDOutput, Tuple]:
							_lowercase       :	Union[str, Any]           =   timestep
							if not torch.is_tensor(snake_case    ):
													_lowercase       :	Optional[Any]           =   torch.tensor([timesteps]      ,	dtype=torch.long      ,	device=sample.device    )
							elif torch.is_tensor(snake_case    ) and len(timesteps.shape    ) == 0:
													_lowercase       :	List[Any]           =   timesteps[None].to(sample.device    )

							_lowercase       :	Dict           =   self.time_proj(snake_case    )
							if self.config.use_timestep_embedding:
													_lowercase       :	List[Any]           =   self.time_mlp(snake_case    )
							else:
													_lowercase       :	Tuple           =   timestep_embed[..., None]
													_lowercase       :	int           =   timestep_embed.repeat([1, 1, sample.shape[2]]    ).to(sample.dtype    )
													_lowercase       :	Union[str, Any]           =   timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])    )

							# 2. down
							_lowercase       :	Optional[Any]           =   ()
							for downsample_block in self.down_blocks:
													_lowercase      ,    _lowercase       :	List[Any]           =   downsample_block(hidden_states=snake_case      ,	temb=snake_case    )
													down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
													_lowercase       :	List[str]           =   self.mid_block(snake_case      ,	snake_case    )

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks    ):
													_lowercase       :	Dict           =   down_block_res_samples[-1:]
													_lowercase       :	List[str]           =   down_block_res_samples[:-1]
													_lowercase       :	List[str]           =   upsample_block(snake_case      ,	res_hidden_states_tuple=snake_case      ,	temb=snake_case    )

							# 5. post-process
							if self.out_block:
													_lowercase       :	Any           =   self.out_block(snake_case      ,	snake_case    )

							if not return_dict:
													return (sample,)

							return UNetaDOutput(sample=snake_case    )






def 		UpperCamelCase     (       _UpperCAmelCase  :   list[int] ,			_UpperCAmelCase  :   list[int]  )   ->			tuple[float, float]:






						'''simple docstring'''

						if not len(_UpperCAmelCase  ) == len(_UpperCAmelCase  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa

						# Calculate the determinants of the matrices
						_lowercase       :	List[Any]           =   aa * ba - aa * ba
						_lowercase       :	Tuple           =   ca * ba - ca * ba
						_lowercase       :	Union[str, Any]           =   aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowercase       :	str           =   determinant_x / determinant
																		_lowercase       :	Union[str, Any]           =   determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)




