



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 UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class 	a__       (       a__       ):


					"""simple docstring"""



					A__  :				Tuple					 =				42




class 	a__       (       a__  ,			a__       ):


					"""simple docstring"""



					@register_to_config
					def __init__(		self     :Optional[Any]      ,  lowercase__     :Optional[Union[int, Tuple[int, int]]] = None      ,  lowercase__     :int = 3      ,  lowercase__     :int = 3      ,  lowercase__     :bool = False      ,  lowercase__     :str = "positional"      ,  lowercase__     :int = 0      ,  lowercase__     :bool = True      ,  lowercase__     :Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")      ,  lowercase__     :Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")      ,  lowercase__     :Tuple[int] = (224, 448, 672, 896)      ,  lowercase__     :int = 2      ,  lowercase__     :float = 1      ,  lowercase__     :int = 1      ,  lowercase__     :str = "conv"      ,  lowercase__     :str = "conv"      ,  lowercase__     :str = "silu"      ,  lowercase__     :Optional[int] = 8      ,  lowercase__     :int = 32      ,  lowercase__     :float = 1E-5      ,  lowercase__     :str = "default"      ,  lowercase__     :bool = True      ,  lowercase__     :Optional[str] = None      ,  lowercase__     :Optional[int] = None      ,  ):
										super().__init__()

										lowercase						   =       sample_size
										lowercase						   =       block_out_channels[0] * 4

										# Check inputs
										if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
															raise ValueError(
															    F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""    )

										if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
															raise ValueError(
															    F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""    )

										# input
										lowercase						   =       nn.Convad(_lowerCamelCase      ,  block_out_channels[0]      ,  kernel_size=3      ,  padding=(1, 1)    )

										# time
										if time_embedding_type == "fourier":
															lowercase						   =       GaussianFourierProjection(embedding_size=block_out_channels[0]      ,  scale=16    )
															lowercase						   =       2 * block_out_channels[0]
										elif time_embedding_type == "positional":
															lowercase						   =       Timesteps(block_out_channels[0]      ,  _lowerCamelCase      ,  _lowerCamelCase    )
															lowercase						   =       block_out_channels[0]

										lowercase						   =       TimestepEmbedding(_lowerCamelCase      ,  _lowerCamelCase    )

										# class embedding
										if class_embed_type is None and num_class_embeds is not None:
															lowercase						   =       nn.Embedding(_lowerCamelCase      ,  _lowerCamelCase    )
										elif class_embed_type == "timestep":
															lowercase						   =       TimestepEmbedding(_lowerCamelCase      ,  _lowerCamelCase    )
										elif class_embed_type == "identity":
															lowercase						   =       nn.Identity(_lowerCamelCase      ,  _lowerCamelCase    )
										else:
															lowercase						   =       None

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

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

															lowercase						   =       get_down_block(
															    _lowerCamelCase      ,  num_layers=_lowerCamelCase      ,  in_channels=_lowerCamelCase      ,  out_channels=_lowerCamelCase      ,  temb_channels=_lowerCamelCase      ,  add_downsample=not is_final_block      ,  resnet_eps=_lowerCamelCase      ,  resnet_act_fn=_lowerCamelCase      ,  resnet_groups=_lowerCamelCase      ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,  downsample_padding=_lowerCamelCase      ,  resnet_time_scale_shift=_lowerCamelCase      ,  downsample_type=_lowerCamelCase      ,  )
															self.down_blocks.append(_lowerCamelCase    )

										# mid
										lowercase						   =       UNetMidBlockaD(
										    in_channels=block_out_channels[-1]      ,  temb_channels=_lowerCamelCase      ,  resnet_eps=_lowerCamelCase      ,  resnet_act_fn=_lowerCamelCase      ,  output_scale_factor=_lowerCamelCase      ,  resnet_time_scale_shift=_lowerCamelCase      ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]      ,  resnet_groups=_lowerCamelCase      ,  add_attention=_lowerCamelCase      ,  )

										# up
										lowercase						   =       list(reversed(_lowerCamelCase    )    )
										lowercase						   =       reversed_block_out_channels[0]
										for i, up_block_type in enumerate(_lowerCamelCase    ):
															lowercase						   =       output_channel
															lowercase						   =       reversed_block_out_channels[i]
															lowercase						   =       reversed_block_out_channels[min(i + 1      ,  len(_lowerCamelCase    ) - 1    )]

															lowercase						   =       i == len(_lowerCamelCase    ) - 1

															lowercase						   =       get_up_block(
															    _lowerCamelCase      ,  num_layers=layers_per_block + 1      ,  in_channels=_lowerCamelCase      ,  out_channels=_lowerCamelCase      ,  prev_output_channel=_lowerCamelCase      ,  temb_channels=_lowerCamelCase      ,  add_upsample=not is_final_block      ,  resnet_eps=_lowerCamelCase      ,  resnet_act_fn=_lowerCamelCase      ,  resnet_groups=_lowerCamelCase      ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,  resnet_time_scale_shift=_lowerCamelCase      ,  upsample_type=_lowerCamelCase      ,  )
															self.up_blocks.append(_lowerCamelCase    )
															lowercase						   =       output_channel

										# out
										lowercase						   =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,  32    )
										lowercase						   =       nn.GroupNorm(num_channels=block_out_channels[0]      ,  num_groups=_lowerCamelCase      ,  eps=_lowerCamelCase    )
										lowercase						   =       nn.SiLU()
										lowercase						   =       nn.Convad(block_out_channels[0]      ,  _lowerCamelCase      ,  kernel_size=3      ,  padding=1    )



					def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :torch.FloatTensor      ,  lowercase__     :Union[torch.Tensor, float, int]      ,  lowercase__     :Optional[torch.Tensor] = None      ,  lowercase__     :bool = True      ,  ):







										if self.config.center_input_sample:
															lowercase						   =       2 * sample - 1.0

										# 1. time
										lowercase						   =       timestep
										if not torch.is_tensor(_lowerCamelCase    ):
															lowercase						   =       torch.tensor([timesteps]      ,  dtype=torch.long      ,  device=sample.device    )
										elif torch.is_tensor(_lowerCamelCase    ) and len(timesteps.shape    ) == 0:
															lowercase						   =       timesteps[None].to(sample.device    )

										# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
										lowercase						   =       timesteps * torch.ones(sample.shape[0]      ,  dtype=timesteps.dtype      ,  device=timesteps.device    )

										lowercase						   =       self.time_proj(_lowerCamelCase    )

										# timesteps does not contain any weights and will always return f32 tensors
										# but time_embedding might actually be running in fp16. so we need to cast here.
										# there might be better ways to encapsulate this.
										lowercase						   =       t_emb.to(dtype=self.dtype    )
										lowercase						   =       self.time_embedding(_lowerCamelCase    )

										if self.class_embedding is not None:
															if class_labels is None:
																				raise ValueError('class_labels should be provided when doing class conditioning'    )

															if self.config.class_embed_type == "timestep":
																				lowercase						   =       self.time_proj(_lowerCamelCase    )

															lowercase						   =       self.class_embedding(_lowerCamelCase    ).to(dtype=self.dtype    )
															lowercase						   =       emb + class_emb

										# 2. pre-process
										lowercase						   =       sample
										lowercase						   =       self.conv_in(_lowerCamelCase    )

										# 3. down
										lowercase						   =       (sample,)
										for downsample_block in self.down_blocks:
															if hasattr(_lowerCamelCase      ,  'skip_conv'    ):
																				lowercase						   =       downsample_block(
																				    hidden_states=_lowerCamelCase      ,  temb=_lowerCamelCase      ,  skip_sample=_lowerCamelCase    )
															else:
																				lowercase						   =       downsample_block(hidden_states=_lowerCamelCase      ,  temb=_lowerCamelCase    )

															down_block_res_samples += res_samples

										# 4. mid
										lowercase						   =       self.mid_block(_lowerCamelCase      ,  _lowerCamelCase    )

										# 5. up
										lowercase						   =       None
										for upsample_block in self.up_blocks:
															lowercase						   =       down_block_res_samples[-len(upsample_block.resnets    ) :]
															lowercase						   =       down_block_res_samples[: -len(upsample_block.resnets    )]

															if hasattr(_lowerCamelCase      ,  'skip_conv'    ):
																				lowercase						   =       upsample_block(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase    )
															else:
																				lowercase						   =       upsample_block(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase    )

        # 6. post-process
										lowercase						   =       self.conv_norm_out(_lowerCamelCase    )
										lowercase						   =       self.conv_act(_lowerCamelCase    )
										lowercase						   =       self.conv_out(_lowerCamelCase    )

										if skip_sample is not None:
															sample += skip_sample

										if self.config.time_embedding_type == "fourier":
															lowercase						   =       timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]    )))    )
															lowercase						   =       sample / timesteps

										if not return_dict:
															return (sample,)

										return UNetaDOutput(sample=_lowerCamelCase    )












'''simple docstring'''




from itertools import permutations




def     snake_case__     (			lowerCamelCase__					: int = 5      ) -> int:
					if gon_side < 3 or gon_side > 5:
										raise ValueError('''gon_side must be in the range [3, 5]'''      )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					A_			:  int			=				list(range(gon_side + 1      ,		0      ,		-1      )      )
					A_			:  List[Any]			=				list(range(gon_side + 2      ,		gon_side * 2 + 1      )      )

					for perm in permutations(small_numbers + big_numbers      ):
										A_			:  Optional[int]			=				generate_gon_ring(lowerCamelCase__      ,		list(lowerCamelCase__      )      )
										if is_magic_gon(lowerCamelCase__      ):
															return int(''''''.join(str(lowerCamelCase__      ) for n in numbers      )      )

					A_			:  Optional[Any]			=				f'Magic {gon_side}-gon ring is impossible'
					raise ValueError(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  List[Any]			=				[0] * (gon_side * 3)
					A_			:  Optional[Any]			=				perm[0:3]
					perm.append(perm[1]      )

					A_			:  Optional[Any]			=				1 if gon_side < 5 else 2

					for i in range(1      ,		len(lowerCamelCase__      ) // 3 + magic_number      ):
										A_			:  List[str]			=				perm[2 * i + 1]
										A_			:  str			=				result[3 * i - 1]
										A_			:  Union[str, Any]			=				perm[2 * i + 2]

					return result




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> bool:
					if len(lowerCamelCase__      ) % 3 != 0:
										raise ValueError('''a gon ring should have a length that is a multiple of 3'''      )

					if min(numbers[::3]      ) != numbers[0]:
										return False

					A_			:  Union[str, Any]			=				sum(numbers[:3]      )

					return all(sum(numbers[i : i + 3]      ) == total for i in range(3      ,		len(lowerCamelCase__      )      ,		3      )      )


if __name__ == "__main__":
							print(solution())



