



'''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       snake_case__		(  _SCREAMING_SNAKE_CASE							):
 A__							=      42



class       snake_case__		(  _SCREAMING_SNAKE_CASE   ,			_SCREAMING_SNAKE_CASE							):
 @register_to_config
 def __init__(					self		:				Optional[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	,			)	->					Dict:







       '''simple docstring'''
       super().__init__()

       __snake_case      :					List[str]						=  sample_size
       __snake_case      :					Any						=  block_out_channels[0] * 4

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

       # time
       if time_embedding_type == "fourier":
             __snake_case      :					Dict						=  GaussianFourierProjection(embedding_size=block_out_channels[0]	,			scale=16   )
             __snake_case      :					Union[str, Any]						=  2 * block_out_channels[0]
       elif time_embedding_type == "positional":
             __snake_case      :					Tuple						=  Timesteps(block_out_channels[0]	,			__a	,			__a   )
             __snake_case      :					List[Any]						=  block_out_channels[0]

       __snake_case      :					Tuple						=  TimestepEmbedding(__a	,			__a   )

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

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

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

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

       # mid
       __snake_case      :					Optional[int]						=  UNetMidBlockaD(
           in_channels=block_out_channels[-1]	,			temb_channels=__a	,			resnet_eps=__a	,			resnet_act_fn=__a	,			output_scale_factor=__a	,			resnet_time_scale_shift=__a	,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]	,			resnet_groups=__a	,			add_attention=__a	,			)

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

             __snake_case      :					int						=  i == len(__a   ) - 1

             __snake_case      :					Optional[Any]						=  get_up_block(
                 __a	,			num_layers=layers_per_block + 1	,			in_channels=__a	,			out_channels=__a	,			prev_output_channel=__a	,			temb_channels=__a	,			add_upsample=not is_final_block	,			resnet_eps=__a	,			resnet_act_fn=__a	,			resnet_groups=__a	,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,			resnet_time_scale_shift=__a	,			upsample_type=__a	,			)
             self.up_blocks.append(__a   )
             __snake_case      :					Dict						=  output_channel

       # out
       __snake_case      :					List[str]						=  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,			32   )
       __snake_case      :					Tuple						=  nn.GroupNorm(num_channels=block_out_channels[0]	,			num_groups=__a	,			eps=__a   )
       __snake_case      :					int						=  nn.SiLU()
       __snake_case      :					Optional[int]						=  nn.Convad(block_out_channels[0]	,			__a	,			kernel_size=3	,			padding=1   )





 def A_					(					self		:				Any	,			__a		:				torch.FloatTensor	,			__a		:				Union[torch.Tensor, float, int]	,			__a		:				Optional[torch.Tensor] = None	,			__a		:				bool = True	,			)	->					Tuple:







       '''simple docstring'''
       # 0. center input if necessary
       if self.config.center_input_sample:
             __snake_case      :					Optional[int]						=  2 * sample - 1.0

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

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

       __snake_case      :					Dict						=  self.time_proj(__a   )

       # 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      :					str						=  t_emb.to(dtype=self.dtype   )
       __snake_case      :					int						=  self.time_embedding(__a   )

       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      :					List[str]						=  self.time_proj(__a   )

             __snake_case      :					List[Any]						=  self.class_embedding(__a   ).to(dtype=self.dtype   )
             __snake_case      :					List[Any]						=  emb + class_emb

       # 2. pre-process
       __snake_case      :					Any						=  sample
       __snake_case      :					Dict						=  self.conv_in(__a   )

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

             down_block_res_samples += res_samples

       # 4. mid
       __snake_case      :					str						=  self.mid_block(__a	,			__a   )

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

             if hasattr(__a	,			'skip_conv'   ):
                   __snake_case				,			__snake_case      :					List[Any]						=  upsample_block(__a	,			__a	,			__a	,			__a   )
             else:
                   __snake_case      :					List[str]						=  upsample_block(__a	,			__a	,			__a   )

        # 6. post-process
       __snake_case      :					Tuple						=  self.conv_norm_out(__a   )
       __snake_case      :					int						=  self.conv_act(__a   )
       __snake_case      :					Optional[int]						=  self.conv_out(__a   )

       if skip_sample is not None:
             sample += skip_sample

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

       if not return_dict:
             return (sample,)

       return UNetaDOutput(sample=__a   )









def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  1.0
    for i in range(1    ,   r + 1					):
        ret *= (n + 1 - i) / i
    return round(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return choose(8 + n    ,   lowercase_					) + choose(9 + n    ,   lowercase_					) - 10




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return sum(non_bouncy_exact(lowercase_					) for i in range(1    ,   n + 1					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_ = 1_00					)				->		int:

    """simple docstring"""


    return non_bouncy_upto(lowercase_					)


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






