


'''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_  (__SCREAMING_SNAKE_CASE  ):





    """simple docstring"""






    lowerCamelCase  :					List[str]							     =			4_2







class    UpperCAmelCase_  (__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE  ):





    """simple docstring"""






    @register_to_config
    def __init__(							self			,     SCREAMING_SNAKE_CASE_ = 6_55_36			,     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			,     )				->     int:
         super().__init__()
         __lowerCamelCase   :	Any								=		sample_size

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

         if use_timestep_embedding:
              __lowerCamelCase   :	str								=		block_out_channels[0] * 4
              __lowerCamelCase   :	Union[str, Any]								=		TimestepEmbedding(
                  in_channels=_SCREAMING_SNAKE_CASE			,     time_embed_dim=_SCREAMING_SNAKE_CASE			,     act_fn=_SCREAMING_SNAKE_CASE			,     out_dim=block_out_channels[0]			,     )

         __lowerCamelCase   :	int								=		nn.ModuleList([]	)
         __lowerCamelCase   :	Any								=		None
         __lowerCamelCase   :	Any								=		nn.ModuleList([]	)
         __lowerCamelCase   :	Tuple								=		None

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

              if i == 0:
                   input_channel += extra_in_channels

              __lowerCamelCase   :	Optional[Any]								=		i == len(_SCREAMING_SNAKE_CASE	) - 1

              __lowerCamelCase   :	Any								=		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
         __lowerCamelCase   :	Optional[int]								=		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
         __lowerCamelCase   :	Optional[int]								=		list(reversed(_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase   :	str								=		reversed_block_out_channels[0]
         if out_block_type is None:
              __lowerCamelCase   :	List[Any]								=		out_channels
         else:
              __lowerCamelCase   :	Dict								=		block_out_channels[0]

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

              __lowerCamelCase   :	str								=		i == len(_SCREAMING_SNAKE_CASE	) - 1

              __lowerCamelCase   :	List[Any]								=		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	)
              __lowerCamelCase   :	Optional[Any]								=		output_channel

         # out
         __lowerCamelCase   :	Any								=		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,     32	)
         __lowerCamelCase   :	List[str]								=		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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = True			,     )				->     Union[UNetaDOutput, Tuple]:
         __lowerCamelCase   :	Any								=		timestep
         if not torch.is_tensor(_SCREAMING_SNAKE_CASE	):
              __lowerCamelCase   :	Dict								=		torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device	)
         elif torch.is_tensor(_SCREAMING_SNAKE_CASE	) and len(timesteps.shape	) == 0:
              __lowerCamelCase   :	Any								=		timesteps[None].to(sample.device	)

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

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

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

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

         # 5. post-process
         if self.out_block:
              __lowerCamelCase   :	Optional[int]								=		self.out_block(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE	)

         if not return_dict:
              return (sample,)

         return UNetaDOutput(sample=_SCREAMING_SNAKE_CASE	)











"""simple docstring"""






def 			_lowercase				(  __snake_case					,__snake_case	)						->   float:
			if digit_amount > 0:
						return round(number - int(__snake_case	)					,__snake_case	)
			return number - int(__snake_case	)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))