



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 get_down_block, get_mid_block, get_out_block, get_up_block


@dataclass
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: torch.FloatTensor


class 	lowerCamelCase__	(       _A							,  _A):



 """simple docstring"""

 @register_to_config
 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					int = 6_55_36			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					int = 2			,						__lowerCAmelCase					:					int = 2			,						__lowerCAmelCase					:					int = 0			,						__lowerCAmelCase					:					str = "fourier"			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,						__lowerCAmelCase					:					Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,						__lowerCAmelCase					:					Tuple[str] = "UNetMidBlock1D"			,						__lowerCAmelCase					:					str = None			,						__lowerCAmelCase					:					Tuple[int] = (32, 32, 64)			,						__lowerCAmelCase					:					str = None			,						__lowerCAmelCase					:					int = 8			,						__lowerCAmelCase					:					int = 1			,						__lowerCAmelCase					:					bool = False			,						)   ->	Optional[int]:
    super().__init__()
    _A      =       sample_size

    # time
    if time_embedding_type == "fourier":
       _A      =       GaussianFourierProjection(
           embedding_size=8			,						set_W_to_weight=__lowerCAmelCase			,						log=__lowerCAmelCase			,						flip_sin_to_cos=__lowerCAmelCase      )
       _A      =       2 * block_out_channels[0]
    elif time_embedding_type == "positional":
       _A      =       Timesteps(
           block_out_channels[0]			,						flip_sin_to_cos=__lowerCAmelCase			,						downscale_freq_shift=__lowerCAmelCase      )
       _A      =       block_out_channels[0]

    if use_timestep_embedding:
       _A      =       block_out_channels[0] * 4
       _A      =       TimestepEmbedding(
           in_channels=__lowerCAmelCase			,						time_embed_dim=__lowerCAmelCase			,						act_fn=__lowerCAmelCase			,						out_dim=block_out_channels[0]			,						)

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

    # down
    _A      =       in_channels
    for i, down_block_type in enumerate(__lowerCAmelCase      ):
       _A      =       output_channel
       _A      =       block_out_channels[i]

       if i == 0:
          input_channel += extra_in_channels

       _A      =       i == len(__lowerCAmelCase      ) - 1

       _A      =       get_down_block(
           __lowerCAmelCase			,						num_layers=__lowerCAmelCase			,						in_channels=__lowerCAmelCase			,						out_channels=__lowerCAmelCase			,						temb_channels=block_out_channels[0]			,						add_downsample=not is_final_block or downsample_each_block			,						)
       self.down_blocks.append(__lowerCAmelCase      )

    # mid
    _A      =       get_mid_block(
        __lowerCAmelCase			,						in_channels=block_out_channels[-1]			,						mid_channels=block_out_channels[-1]			,						out_channels=block_out_channels[-1]			,						embed_dim=block_out_channels[0]			,						num_layers=__lowerCAmelCase			,						add_downsample=__lowerCAmelCase			,						)

    # up
    _A      =       list(reversed(__lowerCAmelCase      )      )
    _A      =       reversed_block_out_channels[0]
    if out_block_type is None:
       _A      =       out_channels
    else:
       _A      =       block_out_channels[0]

    for i, up_block_type in enumerate(__lowerCAmelCase      ):
       _A      =       output_channel
       _A      =       (
           reversed_block_out_channels[i + 1] if i < len(__lowerCAmelCase      ) - 1 else final_upsample_channels
       )

       _A      =       i == len(__lowerCAmelCase      ) - 1

       _A      =       get_up_block(
           __lowerCAmelCase			,						num_layers=__lowerCAmelCase			,						in_channels=__lowerCAmelCase			,						out_channels=__lowerCAmelCase			,						temb_channels=block_out_channels[0]			,						add_upsample=not is_final_block			,						)
       self.up_blocks.append(__lowerCAmelCase      )
       _A      =       output_channel

    # out
    _A      =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,						32      )
    _A      =       get_out_block(
        out_block_type=__lowerCAmelCase			,						num_groups_out=__lowerCAmelCase			,						embed_dim=block_out_channels[0]			,						out_channels=__lowerCAmelCase			,						act_fn=__lowerCAmelCase			,						fc_dim=block_out_channels[-1] // 4			,						)

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					Union[torch.Tensor, float, int]			,						__lowerCAmelCase					:					bool = True			,						)   ->	Union[UNetaDOutput, Tuple]:
    _A      =       timestep
    if not torch.is_tensor(__lowerCAmelCase      ):
       _A      =       torch.tensor([timesteps]			,						dtype=torch.long			,						device=sample.device      )
    elif torch.is_tensor(__lowerCAmelCase      ) and len(timesteps.shape      ) == 0:
       _A      =       timesteps[None].to(sample.device      )

    _A      =       self.time_proj(__lowerCAmelCase      )
    if self.config.use_timestep_embedding:
       _A      =       self.time_mlp(__lowerCAmelCase      )
    else:
       _A      =       timestep_embed[..., None]
       _A      =       timestep_embed.repeat([1, 1, sample.shape[2]]      ).to(sample.dtype      )
       _A      =       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])      )

    # 2. down
    _A      =       ()
    for downsample_block in self.down_blocks:
       _A  ,    _A      =       downsample_block(hidden_states=__lowerCAmelCase			,						temb=__lowerCAmelCase      )
       down_block_res_samples += res_samples

    # 3. mid
    if self.mid_block:
       _A      =       self.mid_block(__lowerCAmelCase			,						__lowerCAmelCase      )

    # 4. up
    for i, upsample_block in enumerate(self.up_blocks      ):
       _A      =       down_block_res_samples[-1:]
       _A      =       down_block_res_samples[:-1]
       _A      =       upsample_block(__lowerCAmelCase			,						res_hidden_states_tuple=__lowerCAmelCase			,						temb=__lowerCAmelCase      )

    # 5. post-process
    if self.out_block:
       _A      =       self.out_block(__lowerCAmelCase			,						__lowerCAmelCase      )

    if not return_dict:
       return (sample,)

    return UNetaDOutput(sample=__lowerCAmelCase      )






'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int = 1000    ):
  UpperCAmelCase    				=						-1
  UpperCAmelCase    				=						0
  for a in range(1							, n // 3    ):
    # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
    UpperCAmelCase    				=						(n * n - 2 * a * n) // (2 * n - 2 * a)
    UpperCAmelCase    				=						n - a - b
    if c * c == (a * a + b * b):
      UpperCAmelCase    				=						a * b * c
      if candidate >= product:
        UpperCAmelCase    				=						candidate
  return product


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


