




'''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 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= 42






class 					lowercase_			(	lowerCAmelCase_	,					lowerCAmelCase_       ):





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

           snake_case__					:      Optional[int]  			=						sample_size
           snake_case__					:      List[str]  			=						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
           snake_case__					:      int  			=						nn.Convad(__lowerCamelCase    ,       block_out_channels[0]    ,       kernel_size=3    ,       padding=(1, 1) )

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

           snake_case__					:      int  			=						TimestepEmbedding(__lowerCamelCase    ,       __lowerCamelCase )

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

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

           # down
           snake_case__					:      Optional[int]  			=						block_out_channels[0]
           for i, down_block_type in enumerate(__lowerCamelCase ):
                 snake_case__					:      Dict  			=						output_channel
                 snake_case__					:      Optional[int]  			=						block_out_channels[i]
                 snake_case__					:      List[str]  			=						i == len(__lowerCamelCase ) - 1

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

                 snake_case__					:      Union[str, Any]  			=						i == len(__lowerCamelCase ) - 1

                 snake_case__					:      int  			=						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 )
                 snake_case__					:      Optional[Any]  			=						output_channel

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





     def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Tuple = None    ,       __lowerCamelCase    :				Union[str, Any] = True    ,       ):






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

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

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

           snake_case__					:      List[str]  			=						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.
           snake_case__					:      List[str]  			=						t_emb.to(dtype=self.dtype )
           snake_case__					:      Union[str, Any]  			=						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":
                       snake_case__					:      str  			=						self.time_proj(__lowerCamelCase )

                 snake_case__					:      Any  			=						self.class_embedding(__lowerCamelCase ).to(dtype=self.dtype )
                 snake_case__					:      List[str]  			=						emb + class_emb

           # 2. pre-process
           snake_case__					:      Optional[int]  			=						sample
           snake_case__					:      str  			=						self.conv_in(__lowerCamelCase )

           # 3. down
           snake_case__					:      List[Any]  			=						(sample,)
           for downsample_block in self.down_blocks:
                 if hasattr(__lowerCamelCase    ,       'skip_conv' ):
                       snake_case__					:      Union[str, Any]  			=						downsample_block(
                           hidden_states=__lowerCamelCase    ,       temb=__lowerCamelCase    ,       skip_sample=__lowerCamelCase )
                 else:
                       snake_case__					:      Union[str, Any]  			=						downsample_block(hidden_states=__lowerCamelCase    ,       temb=__lowerCamelCase )

                 down_block_res_samples += res_samples

           # 4. mid
           snake_case__					:      Dict  			=						self.mid_block(__lowerCamelCase    ,       __lowerCamelCase )

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

                 if hasattr(__lowerCamelCase    ,       'skip_conv' ):
                       snake_case__					:      int  			=						upsample_block(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
                 else:
                       snake_case__					:      List[Any]  			=						upsample_block(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )

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

           if skip_sample is not None:
                 sample += skip_sample

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

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=__lowerCamelCase )







'''simple docstring'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


if __name__ == "__main__":
      print(F'{solution() = }')
