



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     (	UpperCAmelCase__   ):
 lowerCAmelCase__       					=  42



class  __lowerCAmelCase     (	UpperCAmelCase__  ,	UpperCAmelCase__   ):




 @register_to_config
 def __init__(						self	,				__UpperCAmelCase = 65536	,				__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	,				):



    '''simple docstring'''
    super().__init__()
    __lowerCamelCase													=    sample_size

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

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

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

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

       if i == 0:
          input_channel += extra_in_channels

       __lowerCamelCase													=    i == len(lowerCamelCase__				) - 1

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

    # mid
    __lowerCamelCase													=    get_mid_block(
        lowerCamelCase__	,				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=lowerCamelCase__	,				add_downsample=lowerCamelCase__	,				)

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

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

       __lowerCamelCase													=    i == len(lowerCamelCase__				) - 1

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

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = True	,				):



    '''simple docstring'''
    __lowerCamelCase													=    timestep
    if not torch.is_tensor(lowerCamelCase__				):
       __lowerCamelCase													=    torch.tensor([timesteps]	,				dtype=torch.long	,				device=sample.device				)
    elif torch.is_tensor(lowerCamelCase__				) and len(timesteps.shape				) == 0:
       __lowerCamelCase													=    timesteps[None].to(sample.device				)

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

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

    # 3. mid
    if self.mid_block:
       __lowerCamelCase													=    self.mid_block(lowerCamelCase__	,				lowerCamelCase__				)

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

    # 5. post-process
    if self.out_block:
       __lowerCamelCase													=    self.out_block(lowerCamelCase__	,				lowerCamelCase__				)

    if not return_dict:
       return (sample,)

    return UNetaDOutput(sample=lowerCamelCase__				)









def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str = " "							):
   __lowerCamelCase													=    []

   __lowerCamelCase													=    0
   for index, char in enumerate(_UpperCamelCase							):
      if char == separator:
         split_words.append(string[last_index:index]							)
         __lowerCamelCase													=    index + 1
      elif index + 1 == len(_UpperCamelCase							):
         split_words.append(string[last_index : index + 1]							)
   return split_words


if __name__ == "__main__":
  from doctest import testmod

  testmod()






