




'''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 get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class   _SCREAMING_SNAKE_CASE   (   snake_case_     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					42






class   _SCREAMING_SNAKE_CASE   (   snake_case_		,snake_case_     ):



 @register_to_config
 def __init__(   self		:   str			,				a__		:   int = 6_5536			,				a__		:   Optional[int] = None			,				a__		:   int = 2			,				a__		:   int = 2			,				a__		:   int = 0			,				a__		:   str = "fourier"			,				a__		:   bool = True			,				a__		:   bool = False			,				a__		:   float = 0.0			,				a__		:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,				a__		:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,				a__		:   Tuple[str] = "UNetMidBlock1D"			,				a__		:   str = None			,				a__		:   Tuple[int] = (32, 32, 64)			,				a__		:   str = None			,				a__		:   int = 8			,				a__		:   int = 1			,				a__		:   bool = False			,				):
      super().__init__()
      __magic_name__						       =    sample_size

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

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

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

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

           if i == 0:
                input_channel += extra_in_channels

           __magic_name__						       =    i == len(a__      ) - 1

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

      # mid
      __magic_name__						       =    get_mid_block(
          a__			,				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=a__			,				add_downsample=a__			,				)

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

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

           __magic_name__						       =    i == len(a__      ) - 1

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

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



 def 			snake_case__  (   self		:   Dict			,				a__		:   torch.FloatTensor			,				a__		:   Union[torch.Tensor, float, int]			,				a__		:   bool = True			,				):
      __magic_name__						       =    timestep
      if not torch.is_tensor(a__      ):
           __magic_name__						       =    torch.tensor([timesteps]			,				dtype=torch.long			,				device=sample.device      )
      elif torch.is_tensor(a__      ) and len(timesteps.shape      ) == 0:
           __magic_name__						       =    timesteps[None].to(sample.device      )

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

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

      # 3. mid
      if self.mid_block:
           __magic_name__						       =    self.mid_block(a__			,				a__      )

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

      # 5. post-process
      if self.out_block:
           __magic_name__						       =    self.out_block(a__			,				a__      )

      if not return_dict:
           return (sample,)

      return UNetaDOutput(sample=a__      )









'''simple docstring'''




from __future__ import annotations

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






