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 					_UpperCAmelCase			(     __SCREAMING_SNAKE_CASE  ):



 '''simple docstring'''




 __A			       =					42




class 					_UpperCAmelCase			(     __SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE  ):



 '''simple docstring'''




 @register_to_config
 def __init__(		self				:   int							, lowercase_				:   int = 65536							, lowercase_				:   Optional[int] = None							, lowercase_				:   int = 2							, lowercase_				:   int = 2							, lowercase_				:   int = 0							, lowercase_				:   str = "fourier"							, lowercase_				:   bool = True							, lowercase_				:   bool = False							, lowercase_				:   float = 0.0							, lowercase_				:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")							, lowercase_				:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")							, lowercase_				:   Tuple[str] = "UNetMidBlock1D"							, lowercase_				:   str = None							, lowercase_				:   Tuple[int] = (32, 32, 64)							, lowercase_				:   str = None							, lowercase_				:   int = 8							, lowercase_				:   int = 1							, lowercase_				:   bool = False							, )						->	int:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    sample_size

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

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

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

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

     if i == 0:
       input_channel += extra_in_channels

     _UpperCamelCase           =    i == len(UpperCamelCase__) - 1

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

   # mid
   _UpperCamelCase           =    get_mid_block(
       UpperCamelCase__							, 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=UpperCamelCase__							, add_downsample=UpperCamelCase__							, )

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

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

     _UpperCamelCase           =    i == len(UpperCamelCase__) - 1

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

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







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   torch.FloatTensor							, lowercase_				:   Union[torch.Tensor, float, int]							, lowercase_				:   bool = True							, )						->	Union[UNetaDOutput, Tuple]:


   """simple docstring"""







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

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

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

   # 3. mid
   if self.mid_block:
     _UpperCamelCase           =    self.mid_block(UpperCamelCase__							, UpperCamelCase__)

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

   # 5. post-process
   if self.out_block:
     _UpperCamelCase           =    self.out_block(UpperCamelCase__							, UpperCamelCase__)

   if not return_dict:
     return (sample,)

   return UNetaDOutput(sample=UpperCamelCase__)






'''simple docstring'''
from __future__ import annotations

from math import ceil, floor, sqrt




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = 200_0000			)  ->							int:


		'''simple docstring'''


		snake_case  :       list[int]     			=						[0]
		snake_case  :       int

		for idx in range(1 ,       ceil(sqrt(target * 2			) * 1.1			)			):
				triangle_numbers.append(triangle_numbers[-1] + idx			)

		# we want this to be as close as possible to target
		snake_case  :       int     			=						0
		# the area corresponding to the grid that gives the product closest to target
		snake_case  :       int     			=						0
		# an estimate of b, using the quadratic formula
		snake_case  :       float
		# the largest integer less than b_estimate
		snake_case  :       int
		# the largest integer less than b_estimate
		snake_case  :       int
		# the triangle number corresponding to b_floor
		snake_case  :       int
		# the triangle number corresponding to b_ceil
		snake_case  :       int

		for idx_a, triangle_a in enumerate(triangle_numbers[1:] ,       1			):
				snake_case  :       int     			=						(-1 + sqrt(1 + 8 * target / triangle_a			)) / 2
				snake_case  :       Optional[int]     			=						floor(SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						ceil(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Optional[Any]     			=						triangle_numbers[b_floor]
				snake_case  :       Union[str, Any]     			=						triangle_numbers[b_ceil]

				if abs(target - triangle_b_first_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       str     			=						triangle_b_first_guess * triangle_a
						snake_case  :       int     			=						idx_a * b_floor

				if abs(target - triangle_b_second_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       List[Any]     			=						triangle_b_second_guess * triangle_a
						snake_case  :       Any     			=						idx_a * b_ceil

		return area


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


