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    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			42
class    A_ (       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      int = 6_55_36     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      int = 2     ,							__SCREAMING_SNAKE_CASE :      int = 2     ,							__SCREAMING_SNAKE_CASE :      int = 0     ,							__SCREAMING_SNAKE_CASE :      str = "fourier"     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      float = 0.0     ,							__SCREAMING_SNAKE_CASE :      Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,							__SCREAMING_SNAKE_CASE :      Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,							__SCREAMING_SNAKE_CASE :      Tuple[str] = "UNetMidBlock1D"     ,							__SCREAMING_SNAKE_CASE :      str = None     ,							__SCREAMING_SNAKE_CASE :      Tuple[int] = (32, 32, 64)     ,							__SCREAMING_SNAKE_CASE :      str = None     ,							__SCREAMING_SNAKE_CASE :      int = 8     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()
   __a       =  sample_size

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

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

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

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

    if i == 0:
     input_channel += extra_in_channels

    __a       =  i == len(__SCREAMING_SNAKE_CASE       ) - 1

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

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

    __a       =  i == len(__SCREAMING_SNAKE_CASE       ) - 1

    __a       =  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       )
    __a       =  output_channel

   # out
   __a       =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,							32       )
   __a       =  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 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      Union[torch.Tensor, float, int]     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							):
   __a       =  timestep
   if not torch.is_tensor(__SCREAMING_SNAKE_CASE       ):
    __a       =  torch.tensor([timesteps]     ,							dtype=torch.long     ,							device=sample.device       )
   elif torch.is_tensor(__SCREAMING_SNAKE_CASE       ) and len(timesteps.shape       ) == 0:
    __a       =  timesteps[None].to(sample.device       )

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

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

   # 3. mid
   if self.mid_block:
    __a       =  self.mid_block(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

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

   # 5. post-process
   if self.out_block:
    __a       =  self.out_block(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE       )



def   __A		(			_A					):



 """simple docstring"""


 __a       =  []
 for data in source_data:
  for i, el in enumerate(_A					):
   if len(_A					) < i + 1:
    data_lists.append([]					)
   data_lists[i].append(float(_A					)					)
 return data_lists



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 for dlist, weight in zip(_A							,				_A					):
  __a       =  min(_A					)
  __a       =  max(_A					)

  __a       =  []
  # for weight 0 score is 1 - actual score
  if weight == 0:
   for item in dlist:
    try:
     score.append(1 - ((item - mind) / (maxd - mind))					)
    except ZeroDivisionError:
     score.append(1					)

  elif weight == 1:
   for item in dlist:
    try:
     score.append((item - mind) / (maxd - mind)					)
    except ZeroDivisionError:
     score.append(0					)

        # weight not 0 or 1
  else:
   __a       =  f"""Invalid weight of {weight:f} provided"""
   raise ValueError(_A					)

  score_lists.append(_A					)

 return score_lists



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [0 for i in range(len(score_lists[0]					)					)]

 for slist in score_lists:
  for j, ele in enumerate(_A					):
   __a       =  final_scores[j] + ele

 return final_scores






def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  get_data(_A					)
 __a       =  calculate_each_score(_A							,				_A					)
 __a       =  generate_final_scores(_A					)

 # append scores to source data
 for i, ele in enumerate(_A					):
  source_data[i].append(_A					)

 return source_data


