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      __lowercase  (       _A    ):




				'''simple docstring'''


				_A	:						torch.FloatTensor






class      __lowercase  (       _A,  _A    ):




				'''simple docstring'''


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

								UpperCamelCase__       						=							sample_size
								UpperCamelCase__       						=							block_out_channels[0] * 4

								# Check inputs
								if len(snake_case_							) != len(snake_case_							):
												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(snake_case_							) != len(snake_case_							):
												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
								UpperCamelCase__       						=							nn.Convad(snake_case_	,				block_out_channels[0]	,				kernel_size=3	,				padding=(1, 1)							)

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

								UpperCamelCase__       						=							TimestepEmbedding(snake_case_	,				snake_case_							)

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

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

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

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

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

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

												UpperCamelCase__       						=							i == len(snake_case_							) - 1

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

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





				def 			A_				(						self  :						int	,				_a  :						torch.FloatTensor	,				_a  :						Union[torch.Tensor, float, int]	,				_a  :						Optional[torch.Tensor] = None	,				_a  :						bool = True	,				):
								# 0. center input if necessary
								if self.config.center_input_sample:
												UpperCamelCase__       						=							2 * sample - 1.0

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

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

								UpperCamelCase__       						=							self.time_proj(snake_case_							)

								# 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.
								UpperCamelCase__       						=							t_emb.to(dtype=self.dtype							)
								UpperCamelCase__       						=							self.time_embedding(snake_case_							)

								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":
																UpperCamelCase__       						=							self.time_proj(snake_case_							)

												UpperCamelCase__       						=							self.class_embedding(snake_case_							).to(dtype=self.dtype							)
												UpperCamelCase__       						=							emb + class_emb

								# 2. pre-process
								UpperCamelCase__       						=							sample
								UpperCamelCase__       						=							self.conv_in(snake_case_							)

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

												down_block_res_samples += res_samples

								# 4. mid
								UpperCamelCase__       						=							self.mid_block(snake_case_	,				snake_case_							)

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

												if hasattr(snake_case_	,				'''skip_conv'''							):
																UpperCamelCase__       						=							upsample_block(snake_case_	,				snake_case_	,				snake_case_	,				snake_case_							)
												else:
																UpperCamelCase__       						=							upsample_block(snake_case_	,				snake_case_	,				snake_case_							)

        # 6. post-process
								UpperCamelCase__       						=							self.conv_norm_out(snake_case_							)
								UpperCamelCase__       						=							self.conv_act(snake_case_							)
								UpperCamelCase__       						=							self.conv_out(snake_case_							)

								if skip_sample is not None:
												sample += skip_sample

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

								if not return_dict:
												return (sample,)

								return UNetaDOutput(sample=snake_case_							)





from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






