





'''simple docstring'''






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  UpperCAmelCase     (			_UpperCamelCase   ):
      _lowercase:			torch.FloatTensor




class  UpperCAmelCase     (			_UpperCamelCase							,		_UpperCamelCase   ):




      @register_to_config
      def __init__(			self			: int			,			__snake_case			: int = 6_55_36			,			__snake_case			: Optional[int] = None			,			__snake_case			: int = 2			,			__snake_case			: int = 2			,			__snake_case			: int = 0			,			__snake_case			: str = "fourier"			,			__snake_case			: bool = True			,			__snake_case			: bool = False			,			__snake_case			: float = 0.0			,			__snake_case			: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,			__snake_case			: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,			__snake_case			: Tuple[str] = "UNetMidBlock1D"			,			__snake_case			: str = None			,			__snake_case			: Tuple[int] = (32, 32, 64)			,			__snake_case			: str = None			,			__snake_case			: int = 8			,			__snake_case			: int = 1			,			__snake_case			: bool = False			,			)				->      Tuple:
            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 				lowercase__    (			self			: Dict			,			__snake_case			: torch.FloatTensor			,			__snake_case			: Union[torch.Tensor, float, int]			,			__snake_case			: bool = True			,			)				->      Union[UNetaDOutput, Tuple]:
            _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     ,							_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  )












import numpy as np




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return 1 / (1 + np.exp(-vector   ))




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return vector * sigmoid(1.7_02 * vector   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






