



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__				):
       UpperCAmelCase__							:   torch.FloatTensor






class 							lowerCAmelCase_						(    a__			,       a__				):
       @register_to_config
       def __init__(     self,     SCREAMING_SNAKE_CASE_ = 6_5536,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = 2,     SCREAMING_SNAKE_CASE_ = 2,     SCREAMING_SNAKE_CASE_ = 0,     SCREAMING_SNAKE_CASE_ = "fourier",     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = 0.0,     SCREAMING_SNAKE_CASE_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),     SCREAMING_SNAKE_CASE_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),     SCREAMING_SNAKE_CASE_ = "UNetMidBlock1D",     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = (32, 32, 64),     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = 8,     SCREAMING_SNAKE_CASE_ = 1,     SCREAMING_SNAKE_CASE_ = False,     )	->						Any:
          super().__init__()
          UpperCamelCase       :    Optional[Any]		      =      sample_size

          # time
          if time_embedding_type == "fourier":
             UpperCamelCase       :    Any		      =      GaussianFourierProjection(
                 embedding_size=8,     set_W_to_weight=SCREAMING_SNAKE_CASE_,     log=SCREAMING_SNAKE_CASE_,     flip_sin_to_cos=SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    str		      =      2 * block_out_channels[0]
          elif time_embedding_type == "positional":
             UpperCamelCase       :    Optional[Any]		      =      Timesteps(
                 block_out_channels[0],     flip_sin_to_cos=SCREAMING_SNAKE_CASE_,     downscale_freq_shift=SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    int		      =      block_out_channels[0]

          if use_timestep_embedding:
             UpperCamelCase       :    List[Any]		      =      block_out_channels[0] * 4
             UpperCamelCase       :    Any		      =      TimestepEmbedding(
                 in_channels=SCREAMING_SNAKE_CASE_,     time_embed_dim=SCREAMING_SNAKE_CASE_,     act_fn=SCREAMING_SNAKE_CASE_,     out_dim=block_out_channels[0],     )

          UpperCamelCase       :    Tuple		      =      nn.ModuleList([]			)
          UpperCamelCase       :    Dict		      =      None
          UpperCamelCase       :    Tuple		      =      nn.ModuleList([]			)
          UpperCamelCase       :    Optional[Any]		      =      None

          # down
          UpperCamelCase       :    Any		      =      in_channels
          for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    int		      =      output_channel
             UpperCamelCase       :    Union[str, Any]		      =      block_out_channels[i]

             if i == 0:
                input_channel += extra_in_channels

             UpperCamelCase       :    str		      =      i == len(SCREAMING_SNAKE_CASE_			) - 1

             UpperCamelCase       :    Tuple		      =      get_down_block(
                 SCREAMING_SNAKE_CASE_,     num_layers=SCREAMING_SNAKE_CASE_,     in_channels=SCREAMING_SNAKE_CASE_,     out_channels=SCREAMING_SNAKE_CASE_,     temb_channels=block_out_channels[0],     add_downsample=not is_final_block or downsample_each_block,     )
             self.down_blocks.append(SCREAMING_SNAKE_CASE_			)

          # mid
          UpperCamelCase       :    Dict		      =      get_mid_block(
              SCREAMING_SNAKE_CASE_,     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=SCREAMING_SNAKE_CASE_,     add_downsample=SCREAMING_SNAKE_CASE_,     )

          # up
          UpperCamelCase       :    Optional[Any]		      =      list(reversed(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase       :    Tuple		      =      reversed_block_out_channels[0]
          if out_block_type is None:
             UpperCamelCase       :    List[Any]		      =      out_channels
          else:
             UpperCamelCase       :    Tuple		      =      block_out_channels[0]

          for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Tuple		      =      output_channel
             UpperCamelCase       :    Union[str, Any]		      =      (
                 reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_			) - 1 else final_upsample_channels
             )

             UpperCamelCase       :    Dict		      =      i == len(SCREAMING_SNAKE_CASE_			) - 1

             UpperCamelCase       :    List[str]		      =      get_up_block(
                 SCREAMING_SNAKE_CASE_,     num_layers=SCREAMING_SNAKE_CASE_,     in_channels=SCREAMING_SNAKE_CASE_,     out_channels=SCREAMING_SNAKE_CASE_,     temb_channels=block_out_channels[0],     add_upsample=not is_final_block,     )
             self.up_blocks.append(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    List[str]		      =      output_channel

          # out
          UpperCamelCase       :    List[str]		      =      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4,     32			)
          UpperCamelCase       :    Tuple		      =      get_out_block(
              out_block_type=SCREAMING_SNAKE_CASE_,     num_groups_out=SCREAMING_SNAKE_CASE_,     embed_dim=block_out_channels[0],     out_channels=SCREAMING_SNAKE_CASE_,     act_fn=SCREAMING_SNAKE_CASE_,     fc_dim=block_out_channels[-1] // 4,     )






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = True,     )	->						Union[UNetaDOutput, Tuple]:
          UpperCamelCase       :    Optional[Any]		      =      timestep
          if not torch.is_tensor(SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Tuple		      =      torch.tensor([timesteps],     dtype=torch.long,     device=sample.device			)
          elif torch.is_tensor(SCREAMING_SNAKE_CASE_			) and len(timesteps.shape			) == 0:
             UpperCamelCase       :    List[str]		      =      timesteps[None].to(sample.device			)

          UpperCamelCase       :    Tuple		      =      self.time_proj(SCREAMING_SNAKE_CASE_			)
          if self.config.use_timestep_embedding:
             UpperCamelCase       :    str		      =      self.time_mlp(SCREAMING_SNAKE_CASE_			)
          else:
             UpperCamelCase       :    Dict		      =      timestep_embed[..., None]
             UpperCamelCase       :    int		      =      timestep_embed.repeat([1, 1, sample.shape[2]]			).to(sample.dtype			)
             UpperCamelCase       :    Optional[int]		      =      timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])			)

          # 2. down
          UpperCamelCase       :    str		      =      ()
          for downsample_block in self.down_blocks:
             UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      downsample_block(hidden_states=SCREAMING_SNAKE_CASE_,     temb=SCREAMING_SNAKE_CASE_			)
             down_block_res_samples += res_samples

          # 3. mid
          if self.mid_block:
             UpperCamelCase       :    Tuple		      =      self.mid_block(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          # 4. up
          for i, upsample_block in enumerate(self.up_blocks			):
             UpperCamelCase       :    Optional[Any]		      =      down_block_res_samples[-1:]
             UpperCamelCase       :    Optional[int]		      =      down_block_res_samples[:-1]
             UpperCamelCase       :    Any		      =      upsample_block(SCREAMING_SNAKE_CASE_,     res_hidden_states_tuple=SCREAMING_SNAKE_CASE_,     temb=SCREAMING_SNAKE_CASE_			)

          # 5. post-process
          if self.out_block:
             UpperCamelCase       :    Any		      =      self.out_block(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          if not return_dict:
             return (sample,)

          return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_			)










"""simple docstring"""


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



