





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 		_lowerCAmelCase(		_a):

  """simple docstring"""







  lowerCamelCase__     	=       42







class 		_lowerCAmelCase(		_a						,       _a):

  """simple docstring"""







  @register_to_config
  def __init__(   self    ,							UpperCAmelCase = None    ,							UpperCAmelCase = 3    ,							UpperCAmelCase = 3    ,							UpperCAmelCase = False    ,							UpperCAmelCase = "positional"    ,							UpperCAmelCase = 0    ,							UpperCAmelCase = True    ,							UpperCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")    ,							UpperCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")    ,							UpperCAmelCase = (2_24, 4_48, 6_72, 8_96)    ,							UpperCAmelCase = 2    ,							UpperCAmelCase = 1    ,							UpperCAmelCase = 1    ,							UpperCAmelCase = "conv"    ,							UpperCAmelCase = "conv"    ,							UpperCAmelCase = "silu"    ,							UpperCAmelCase = 8    ,							UpperCAmelCase = 32    ,							UpperCAmelCase = 1e-5    ,							UpperCAmelCase = "default"    ,							UpperCAmelCase = True    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							)->   Any:
     super().__init__()

     __A    =  sample_size
     __A    =  block_out_channels[0] * 4

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

     # time
     if time_embedding_type == "fourier":
        __A    =  GaussianFourierProjection(embedding_size=block_out_channels[0]    ,							scale=16      )
        __A    =  2 * block_out_channels[0]
     elif time_embedding_type == "positional":
        __A    =  Timesteps(block_out_channels[0]    ,							UpperCAmelCase    ,							UpperCAmelCase      )
        __A    =  block_out_channels[0]

     __A    =  TimestepEmbedding(UpperCAmelCase    ,							UpperCAmelCase      )

     # class embedding
     if class_embed_type is None and num_class_embeds is not None:
        __A    =  nn.Embedding(UpperCAmelCase    ,							UpperCAmelCase      )
     elif class_embed_type == "timestep":
        __A    =  TimestepEmbedding(UpperCAmelCase    ,							UpperCAmelCase      )
     elif class_embed_type == "identity":
        __A    =  nn.Identity(UpperCAmelCase    ,							UpperCAmelCase      )
     else:
        __A    =  None

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

     # down
     __A    =  block_out_channels[0]
     for i, down_block_type in enumerate(UpperCAmelCase      ):
        __A    =  output_channel
        __A    =  block_out_channels[i]
        __A    =  i == len(UpperCAmelCase      ) - 1

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

     # mid
     __A    =  UNetMidBlockaD(
         in_channels=block_out_channels[-1]    ,							temb_channels=UpperCAmelCase    ,							resnet_eps=UpperCAmelCase    ,							resnet_act_fn=UpperCAmelCase    ,							output_scale_factor=UpperCAmelCase    ,							resnet_time_scale_shift=UpperCAmelCase    ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]    ,							resnet_groups=UpperCAmelCase    ,							add_attention=UpperCAmelCase    ,							)

     # up
     __A    =  list(reversed(UpperCAmelCase      )      )
     __A    =  reversed_block_out_channels[0]
     for i, up_block_type in enumerate(UpperCAmelCase      ):
        __A    =  output_channel
        __A    =  reversed_block_out_channels[i]
        __A    =  reversed_block_out_channels[min(i + 1    ,							len(UpperCAmelCase      ) - 1      )]

        __A    =  i == len(UpperCAmelCase      ) - 1

        __A    =  get_up_block(
            UpperCAmelCase    ,							num_layers=layers_per_block + 1    ,							in_channels=UpperCAmelCase    ,							out_channels=UpperCAmelCase    ,							prev_output_channel=UpperCAmelCase    ,							temb_channels=UpperCAmelCase    ,							add_upsample=not is_final_block    ,							resnet_eps=UpperCAmelCase    ,							resnet_act_fn=UpperCAmelCase    ,							resnet_groups=UpperCAmelCase    ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,							resnet_time_scale_shift=UpperCAmelCase    ,							upsample_type=UpperCAmelCase    ,							)
        self.up_blocks.append(UpperCAmelCase      )
        __A    =  output_channel

     # out
     __A    =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,							32      )
     __A    =  nn.GroupNorm(num_channels=block_out_channels[0]    ,							num_groups=UpperCAmelCase    ,							eps=UpperCAmelCase      )
     __A    =  nn.SiLU()
     __A    =  nn.Convad(block_out_channels[0]    ,							UpperCAmelCase    ,							kernel_size=3    ,							padding=1      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase = None    ,							UpperCAmelCase = True    ,							)->   Union[UNetaDOutput, Tuple]:
     # 0. center input if necessary
     if self.config.center_input_sample:
        __A    =  2 * sample - 1.0

     # 1. time
     __A    =  timestep
     if not torch.is_tensor(UpperCAmelCase      ):
        __A    =  torch.tensor([timesteps]    ,							dtype=torch.long    ,							device=sample.device      )
     elif torch.is_tensor(UpperCAmelCase      ) and len(timesteps.shape      ) == 0:
        __A    =  timesteps[None].to(sample.device      )

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

     __A    =  self.time_proj(UpperCAmelCase      )

     # 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.
     __A    =  t_emb.to(dtype=self.dtype      )
     __A    =  self.time_embedding(UpperCAmelCase      )

     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":
           __A    =  self.time_proj(UpperCAmelCase      )

        __A    =  self.class_embedding(UpperCAmelCase      ).to(dtype=self.dtype      )
        __A    =  emb + class_emb

     # 2. pre-process
     __A    =  sample
     __A    =  self.conv_in(UpperCAmelCase      )

     # 3. down
     __A    =  (sample,)
     for downsample_block in self.down_blocks:
        if hasattr(UpperCAmelCase    ,							'''skip_conv'''      ):
           __A   ,		__A   ,		__A    =  downsample_block(
               hidden_states=UpperCAmelCase    ,							temb=UpperCAmelCase    ,							skip_sample=UpperCAmelCase      )
        else:
           __A   ,		__A    =  downsample_block(hidden_states=UpperCAmelCase    ,							temb=UpperCAmelCase      )

        down_block_res_samples += res_samples

     # 4. mid
     __A    =  self.mid_block(UpperCAmelCase    ,							UpperCAmelCase      )

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

        if hasattr(UpperCAmelCase    ,							'''skip_conv'''      ):
           __A   ,		__A    =  upsample_block(UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )
        else:
           __A    =  upsample_block(UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )

        # 6. post-process
     __A    =  self.conv_norm_out(UpperCAmelCase      )
     __A    =  self.conv_act(UpperCAmelCase      )
     __A    =  self.conv_out(UpperCAmelCase      )

     if skip_sample is not None:
        sample += skip_sample

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

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=UpperCAmelCase      )








from __future__ import annotations
def  __UpperCamelCase    (						snake_case      )    ->		bool:


   '''simple docstring'''







   __A    =  42
   __A    =  list(snake_case      )
   __A   ,		__A    =  _vector[:-1], int(_vector[-1]      )
   __A    =  [int(snake_case      ) for digit in __vector]

   vector.reverse()
   for i, digit in enumerate(snake_case      ):
      if i & 1 == 0:
         __A    =  digit * 2
         if doubled > 9:
            doubled -= 9
         check_digit += doubled
      else:
         check_digit += digit

   return check_digit % 1_0 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    assert is_luhn("""79927398713""")
    assert not is_luhn("""79927398714""")

