




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_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						4_2







class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__ ):

 '''simple docstring'''

 @register_to_config
 def __init__(			self	,   _UpperCAmelCase = 6_55_36	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = "fourier"	,   _UpperCAmelCase = True	,   _UpperCAmelCase = False	,   _UpperCAmelCase = 0.0	,   _UpperCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,   _UpperCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,   _UpperCAmelCase = "UNetMidBlock1D"	,   _UpperCAmelCase = None	,   _UpperCAmelCase = (32, 32, 64)	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 8	,   _UpperCAmelCase = 1	,   _UpperCAmelCase = False	,   ):
       super().__init__()
       snake_case_ =   sample_size

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

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

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

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

             if i == 0:
                   input_channel += extra_in_channels

             snake_case_ =   i == len(_UpperCAmelCase      ) - 1

             snake_case_ =   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
       snake_case_ =   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
       snake_case_ =   list(reversed(_UpperCAmelCase      )      )
       snake_case_ =   reversed_block_out_channels[0]
       if out_block_type is None:
             snake_case_ =   out_channels
       else:
             snake_case_ =   block_out_channels[0]

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

             snake_case_ =   i == len(_UpperCAmelCase      ) - 1

             snake_case_ =   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      )
             snake_case_ =   output_channel

       # out
       snake_case_ =   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,   32      )
       snake_case_ =   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	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = True	,   ):
       snake_case_ =   timestep
       if not torch.is_tensor(_UpperCAmelCase      ):
             snake_case_ =   torch.tensor([timesteps]	,   dtype=torch.long	,   device=sample.device      )
       elif torch.is_tensor(_UpperCAmelCase      ) and len(timesteps.shape      ) == 0:
             snake_case_ =   timesteps[None].to(sample.device      )

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

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

       # 3. mid
       if self.mid_block:
             snake_case_ =   self.mid_block(_UpperCAmelCase	,   _UpperCAmelCase      )

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

       # 5. post-process
       if self.out_block:
             snake_case_ =   self.out_block(_UpperCAmelCase	,   _UpperCAmelCase      )

       if not return_dict:
             return (sample,)

       return UNetaDOutput(sample=_UpperCAmelCase      )



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      return int((input_a, input_a).count(0      ) == 0      )






def  __lowerCAmelCase					()->						None:




      """simple docstring"""

      assert and_gate(0      ,      0      ) == 0
      assert and_gate(0      ,      1      ) == 0
      assert and_gate(1      ,      0      ) == 0
      assert and_gate(1      ,      1      ) == 1


if __name__ == "__main__":
   test_and_gate()
   print(and_gate(1, 0))
   print(and_gate(0, 0))
   print(and_gate(0, 1))
   print(and_gate(1, 1))