
'''simple docstring'''





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        UpperCAmelCase_       (						snake_case_							):

 '''simple docstring'''
 lowercase_				:						str				=       42






class        UpperCAmelCase_       (						snake_case_				,							snake_case_							):

 '''simple docstring'''







 @register_to_config
 def __init__(				self		:     List[str] ,							snake_case__		:     Tuple = None ,							snake_case__		:     List[Any] = 3 ,							snake_case__		:     int = 3 ,							snake_case__		:     List[str] = False ,							snake_case__		:     List[Any] = "positional" ,							snake_case__		:     Optional[Any] = 0 ,							snake_case__		:     Optional[int] = True ,							snake_case__		:     Dict = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,							snake_case__		:     Dict = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,							snake_case__		:     List[Any] = (2_24, 4_48, 6_72, 8_96) ,							snake_case__		:     int = 2 ,							snake_case__		:     List[Any] = 1 ,							snake_case__		:     List[Any] = 1 ,							snake_case__		:     Optional[Any] = "conv" ,							snake_case__		:     str = "conv" ,							snake_case__		:     Optional[int] = "silu" ,							snake_case__		:     str = 8 ,							snake_case__		:     List[Any] = 32 ,							snake_case__		:     List[str] = 1e-5 ,							snake_case__		:     str = "default" ,							snake_case__		:     str = True ,							snake_case__		:     Optional[int] = None ,							snake_case__		:     Dict = None ,							):



   '''simple docstring'''




   super().__init__()

   UpperCAmelCase__			:				Dict												=       sample_size
   UpperCAmelCase__			:				Optional[Any]												=       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__			:				int												=       nn.Convad(snake_case__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)   )

   # time
   if time_embedding_type == "fourier":
     UpperCAmelCase__			:				Optional[Any]												=       GaussianFourierProjection(embedding_size=block_out_channels[0] ,							scale=16   )
     UpperCAmelCase__			:				Optional[Any]												=       2 * block_out_channels[0]
   elif time_embedding_type == "positional":
     UpperCAmelCase__			:				Optional[Any]												=       Timesteps(block_out_channels[0] ,							snake_case__ ,							snake_case__   )
     UpperCAmelCase__			:				int												=       block_out_channels[0]

   UpperCAmelCase__			:				Optional[Any]												=       TimestepEmbedding(snake_case__ ,							snake_case__   )

   # class embedding
   if class_embed_type is None and num_class_embeds is not None:
     UpperCAmelCase__			:				Union[str, Any]												=       nn.Embedding(snake_case__ ,							snake_case__   )
   elif class_embed_type == "timestep":
     UpperCAmelCase__			:				Any												=       TimestepEmbedding(snake_case__ ,							snake_case__   )
   elif class_embed_type == "identity":
     UpperCAmelCase__			:				Optional[Any]												=       nn.Identity(snake_case__ ,							snake_case__   )
   else:
     UpperCAmelCase__			:				str												=       None

   UpperCAmelCase__			:				Optional[int]												=       nn.ModuleList([]   )
   UpperCAmelCase__			:				Optional[Any]												=       None
   UpperCAmelCase__			:				Dict												=       nn.ModuleList([]   )

   # down
   UpperCAmelCase__			:				str												=       block_out_channels[0]
   for i, down_block_type in enumerate(snake_case__   ):
     UpperCAmelCase__			:				Any												=       output_channel
     UpperCAmelCase__			:				Any												=       block_out_channels[i]
     UpperCAmelCase__			:				Union[str, Any]												=       i == len(snake_case__   ) - 1

     UpperCAmelCase__			:				Any												=       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__			:				Dict												=       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__			:				Union[str, Any]												=       list(reversed(snake_case__   )   )
   UpperCAmelCase__			:				str												=       reversed_block_out_channels[0]
   for i, up_block_type in enumerate(snake_case__   ):
     UpperCAmelCase__			:				int												=       output_channel
     UpperCAmelCase__			:				Optional[int]												=       reversed_block_out_channels[i]
     UpperCAmelCase__			:				Dict												=       reversed_block_out_channels[min(i + 1 ,							len(snake_case__   ) - 1   )]

     UpperCAmelCase__			:				Tuple												=       i == len(snake_case__   ) - 1

     UpperCAmelCase__			:				Optional[int]												=       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__			:				List[Any]												=       output_channel

   # out
   UpperCAmelCase__			:				List[Any]												=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							32   )
   UpperCAmelCase__			:				List[Any]												=       nn.GroupNorm(num_channels=block_out_channels[0] ,							num_groups=snake_case__ ,							eps=snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       nn.SiLU()
   UpperCAmelCase__			:				Any												=       nn.Convad(block_out_channels[0] ,							snake_case__ ,							kernel_size=3 ,							padding=1   )







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     List[Any] ,							snake_case__		:     Optional[int] ,							snake_case__		:     List[Any] = None ,							snake_case__		:     Any = True ,							):



   '''simple docstring'''




   if self.config.center_input_sample:
     UpperCAmelCase__			:				Optional[int]												=       2 * sample - 1.0

   # 1. time
   UpperCAmelCase__			:				Tuple												=       timestep
   if not torch.is_tensor(snake_case__   ):
     UpperCAmelCase__			:				List[str]												=       torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device   )
   elif torch.is_tensor(snake_case__   ) and len(timesteps.shape   ) == 0:
     UpperCAmelCase__			:				int												=       timesteps[None].to(sample.device   )

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

   UpperCAmelCase__			:				Union[str, Any]												=       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__			:				Tuple												=       t_emb.to(dtype=self.dtype   )
   UpperCAmelCase__			:				Optional[Any]												=       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__			:				List[str]												=       self.time_proj(snake_case__   )

     UpperCAmelCase__			:				Dict												=       self.class_embedding(snake_case__   ).to(dtype=self.dtype   )
     UpperCAmelCase__			:				Union[str, Any]												=       emb + class_emb

   # 2. pre-process
   UpperCAmelCase__			:				Union[str, Any]												=       sample
   UpperCAmelCase__			:				List[Any]												=       self.conv_in(snake_case__   )

   # 3. down
   UpperCAmelCase__			:				Optional[int]												=       (sample,)
   for downsample_block in self.down_blocks:
     if hasattr(snake_case__ ,							"skip_conv"   ):
       UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__			:				Any												=       downsample_block(
           hidden_states=snake_case__ ,							temb=snake_case__ ,							skip_sample=snake_case__   )
     else:
       UpperCAmelCase__					, UpperCAmelCase__			:				List[Any]												=       downsample_block(hidden_states=snake_case__ ,							temb=snake_case__   )

     down_block_res_samples += res_samples

   # 4. mid
   UpperCAmelCase__			:				str												=       self.mid_block(snake_case__ ,							snake_case__   )

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

     if hasattr(snake_case__ ,							"skip_conv"   ):
       UpperCAmelCase__					, UpperCAmelCase__			:				Any												=       upsample_block(snake_case__ ,							snake_case__ ,							snake_case__ ,							snake_case__   )
     else:
       UpperCAmelCase__			:				Union[str, Any]												=       upsample_block(snake_case__ ,							snake_case__ ,							snake_case__   )

        # 6. post-process
   UpperCAmelCase__			:				int												=       self.conv_norm_out(snake_case__   )
   UpperCAmelCase__			:				Optional[int]												=       self.conv_act(snake_case__   )
   UpperCAmelCase__			:				Dict												=       self.conv_out(snake_case__   )

   if skip_sample is not None:
     sample += skip_sample

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

   if not return_dict:
     return (sample,)

   return UNetaDOutput(sample=snake_case__   )





'''simple docstring'''





from math import cos, sin







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				List[Any]												=       float(lowercase__		)
  return [[scaling_factor * int(x == y		) for x in range(2		)] for y in range(2		)]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				List[Any]												=       cos(lowercase__		), sin(lowercase__		)
  return [[c, -s], [s, c]]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				Dict												=       cos(lowercase__		), sin(lowercase__		)
  UpperCAmelCase__			:				Tuple												=       c * s
  return [[c * c, cs], [cs, s * s]]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				Dict												=       cos(lowercase__		), sin(lowercase__		)
  UpperCAmelCase__			:				Any												=       c * s
  return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'    {scaling(5) = }')
print(F'  {rotation(4_5) = }')
print(F'{projection(4_5) = }')






print(F'{reflection(4_5) = }')



