




from __future__ import annotations






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     order

        # a_{0} ... a_{k}
        lowerCAmelCase__				:  Dict		       =     [1.0] + [0.0] * order
        # b_{0} ... b_{k}
        lowerCAmelCase__				:  Optional[int]		       =     [1.0] + [0.0] * order

        # x[n-1] ... x[n-k]
        lowerCAmelCase__				:  Dict		       =     [0.0] * self.order
        # y[n-1] ... y[n-k]
        lowerCAmelCase__				:  Optional[int]		       =     [0.0] * self.order

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if len(__UpperCAmelCase		) < self.order:
          lowerCAmelCase__				:  Tuple		       =     [1.0, *a_coeffs]

        if len(__UpperCAmelCase		) != self.order + 1:
          lowerCAmelCase__				:  str		       =     (
              f"""Expected a_coeffs to have {self.order + 1} elements """
              f"""for {self.order}-order filter, got {len(__UpperCAmelCase		)}"""
          )
          raise ValueError(__UpperCAmelCase		)

        if len(__UpperCAmelCase		) != self.order + 1:
          lowerCAmelCase__				:  Optional[int]		       =     (
              f"""Expected b_coeffs to have {self.order + 1} elements """
              f"""for {self.order}-order filter, got {len(__UpperCAmelCase		)}"""
          )
          raise ValueError(__UpperCAmelCase		)

        lowerCAmelCase__				:  int		       =     a_coeffs
        lowerCAmelCase__				:  Union[str, Any]		       =     b_coeffs







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     0.0

        # Start at index 1 and do index 0 at the end.
        for i in range(1      ,				self.order + 1		):
          result += (
              self.b_coeffs[i] * self.input_history[i - 1]
              - self.a_coeffs[i] * self.output_history[i - 1]
          )

        lowerCAmelCase__				:  Optional[int]		       =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

        lowerCAmelCase__				:  Optional[Any]		       =     self.input_history[:-1]
        lowerCAmelCase__				:  Union[str, Any]		       =     self.output_history[:-1]

        lowerCAmelCase__				:  int		       =     sample
        lowerCAmelCase__				:  Dict		       =     result

        return result







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     (  _lowercase	):
      A__		     =		42






class  _lowerCAmelCase     (  _lowercase    ,			_lowercase	):

      @register_to_config
      def __init__(   self      ,				__UpperCAmelCase = 6_5536      ,				__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__()
        lowerCAmelCase__				:  Dict		       =     sample_size

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

        if use_timestep_embedding:
          lowerCAmelCase__				:  str		       =     block_out_channels[0] * 4
          lowerCAmelCase__				:  Dict		       =     TimestepEmbedding(
              in_channels=__UpperCAmelCase      ,				time_embed_dim=__UpperCAmelCase      ,				act_fn=__UpperCAmelCase      ,				out_dim=block_out_channels[0]      ,				)

        lowerCAmelCase__				:  str		       =     nn.ModuleList([]		)
        lowerCAmelCase__				:  Optional[Any]		       =     None
        lowerCAmelCase__				:  Optional[int]		       =     nn.ModuleList([]		)
        lowerCAmelCase__				:  Optional[Any]		       =     None

        # down
        lowerCAmelCase__				:  List[Any]		       =     in_channels
        for i, down_block_type in enumerate(__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     output_channel
          lowerCAmelCase__				:  Any		       =     block_out_channels[i]

          if i == 0:
            input_channel += extra_in_channels

          lowerCAmelCase__				:  Any		       =     i == len(__UpperCAmelCase		) - 1

          lowerCAmelCase__				:  Any		       =     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
        lowerCAmelCase__				:  Optional[int]		       =     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
        lowerCAmelCase__				:  Union[str, Any]		       =     list(reversed(__UpperCAmelCase		)		)
        lowerCAmelCase__				:  List[str]		       =     reversed_block_out_channels[0]
        if out_block_type is None:
          lowerCAmelCase__				:  Optional[int]		       =     out_channels
        else:
          lowerCAmelCase__				:  int		       =     block_out_channels[0]

        for i, up_block_type in enumerate(__UpperCAmelCase		):
          lowerCAmelCase__				:  Optional[int]		       =     output_channel
          lowerCAmelCase__				:  Any		       =     (
              reversed_block_out_channels[i + 1] if i < len(__UpperCAmelCase		) - 1 else final_upsample_channels
          )

          lowerCAmelCase__				:  Optional[int]		       =     i == len(__UpperCAmelCase		) - 1

          lowerCAmelCase__				:  Optional[int]		       =     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		)
          lowerCAmelCase__				:  int		       =     output_channel

        # out
        lowerCAmelCase__				:  Union[str, Any]		       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,				32		)
        lowerCAmelCase__				:  Dict		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = True      ,				):
        lowerCAmelCase__				:  int		       =     timestep
        if not torch.is_tensor(__UpperCAmelCase		):
          lowerCAmelCase__				:  Any		       =     torch.tensor([timesteps]      ,				dtype=torch.long      ,				device=sample.device		)
        elif torch.is_tensor(__UpperCAmelCase		) and len(timesteps.shape		) == 0:
          lowerCAmelCase__				:  Any		       =     timesteps[None].to(sample.device		)

        lowerCAmelCase__				:  int		       =     self.time_proj(__UpperCAmelCase		)
        if self.config.use_timestep_embedding:
          lowerCAmelCase__				:  str		       =     self.time_mlp(__UpperCAmelCase		)
        else:
          lowerCAmelCase__				:  Tuple		       =     timestep_embed[..., None]
          lowerCAmelCase__				:  Union[str, Any]		       =     timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
          lowerCAmelCase__				:  Any		       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

        # 2. down
        lowerCAmelCase__				:  int		       =     ()
        for downsample_block in self.down_blocks:
          lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     downsample_block(hidden_states=__UpperCAmelCase      ,				temb=__UpperCAmelCase		)
          down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
          lowerCAmelCase__				:  Dict		       =     self.mid_block(__UpperCAmelCase      ,				__UpperCAmelCase		)

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

        # 5. post-process
        if self.out_block:
          lowerCAmelCase__				:  Any		       =     self.out_block(__UpperCAmelCase      ,				__UpperCAmelCase		)

        if not return_dict:
          return (sample,)

        return UNetaDOutput(sample=__UpperCAmelCase		)

