

'''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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  torch.FloatTensor






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  @register_to_config
  def __init__(      self   :     List[str]					,lowercase__   :     int = 6_5_5_3_6					,lowercase__   :     Optional[int] = None					,lowercase__   :     int = 2					,lowercase__   :     int = 2					,lowercase__   :     int = 0					,lowercase__   :     str = "fourier"					,lowercase__   :     bool = True					,lowercase__   :     bool = False					,lowercase__   :     float = 0.0					,lowercase__   :     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,lowercase__   :     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,lowercase__   :     Tuple[str] = "UNetMidBlock1D"					,lowercase__   :     str = None					,lowercase__   :     Tuple[int] = (3_2, 3_2, 6_4)					,lowercase__   :     str = None					,lowercase__   :     int = 8					,lowercase__   :     int = 1					,lowercase__   :     bool = False					,):
   super().__init__()
   __lowercase							  = sample_size

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

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

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

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

    if i == 0:
     input_channel += extra_in_channels

    __lowercase							  = i == len(lowercase__			) - 1

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

   # mid
   __lowercase							  = get_mid_block(
       lowercase__					,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=lowercase__					,add_downsample=lowercase__					,)

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

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

    __lowercase							  = i == len(lowercase__			) - 1

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

   # out
   __lowercase							  = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,3_2			)
   __lowercase							  = get_out_block(
       out_block_type=lowercase__					,num_groups_out=lowercase__					,embed_dim=block_out_channels[0]					,out_channels=lowercase__					,act_fn=lowercase__					,fc_dim=block_out_channels[-1] // 4					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     torch.FloatTensor					,lowercase__   :     Union[torch.Tensor, float, int]					,lowercase__   :     bool = True					,):
   __lowercase							  = timestep
   if not torch.is_tensor(lowercase__			):
    __lowercase							  = torch.tensor([timesteps]					,dtype=torch.long					,device=sample.device			)
   elif torch.is_tensor(lowercase__			) and len(timesteps.shape			) == 0:
    __lowercase							  = timesteps[None].to(sample.device			)

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

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

   # 3. mid
   if self.mid_block:
    __lowercase							  = self.mid_block(lowercase__					,lowercase__			)

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

   # 5. post-process
   if self.out_block:
    __lowercase							  = self.out_block(lowercase__					,lowercase__			)

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=lowercase__			)



from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



