
'''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 ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42







class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 6_55_36							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 2							,		lowerCAmelCase_ = 2							,		lowerCAmelCase_ = 0							,		lowerCAmelCase_ = "fourier"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = False							,		lowerCAmelCase_ = 0.0							,		lowerCAmelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")							,		lowerCAmelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")							,		lowerCAmelCase_ = "UNetMidBlock1D"							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = (32, 32, 64)							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 8							,		lowerCAmelCase_ = 1							,		lowerCAmelCase_ = False							,		):

       '''simple docstring'''

       super().__init__()
       a_      :	str      =							sample_size

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

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

       a_      :	Union[str, Any]      =							nn.ModuleList([] )
       a_      :	Tuple      =							None
       a_      :	Optional[int]      =							nn.ModuleList([] )
       a_      :	str      =							None

       # down
       a_      :	Any      =							in_channels
       for i, down_block_type in enumerate(lowerCAmelCase_ ):
         a_      :	int      =							output_channel
         a_      :	Tuple      =							block_out_channels[i]

         if i == 0:
           input_channel += extra_in_channels

         a_      :	int      =							i == len(lowerCAmelCase_ ) - 1

         a_      :	int      =							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
       a_      :	int      =							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
       a_      :	Any      =							list(reversed(lowerCAmelCase_ ) )
       a_      :	Any      =							reversed_block_out_channels[0]
       if out_block_type is None:
         a_      :	Any      =							out_channels
       else:
         a_      :	str      =							block_out_channels[0]

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

         a_      :	Optional[Any]      =							i == len(lowerCAmelCase_ ) - 1

         a_      :	List[Any]      =							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_ )
         a_      :	List[Any]      =							output_channel

       # out
       a_      :	Union[str, Any]      =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							,		32 )
       a_      :	Any      =							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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	int      =							timestep
       if not torch.is_tensor(lowerCAmelCase_ ):
         a_      :	str      =							torch.tensor([timesteps]							,		dtype=torch.long							,		device=sample.device )
       elif torch.is_tensor(lowerCAmelCase_ ) and len(timesteps.shape ) == 0:
         a_      :	Union[str, Any]      =							timesteps[None].to(sample.device )

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

       # 2. down
       a_      :	Optional[Any]      =							()
       for downsample_block in self.down_blocks:
         a_      , a_      :	List[str]      =							downsample_block(hidden_states=lowerCAmelCase_							,		temb=lowerCAmelCase_ )
         down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
         a_      :	Tuple      =							self.mid_block(lowerCAmelCase_							,		lowerCAmelCase_ )

       # 4. up
       for i, upsample_block in enumerate(self.up_blocks ):
         a_      :	str      =							down_block_res_samples[-1:]
         a_      :	int      =							down_block_res_samples[:-1]
         a_      :	Any      =							upsample_block(lowerCAmelCase_							,		res_hidden_states_tuple=lowerCAmelCase_							,		temb=lowerCAmelCase_ )

       # 5. post-process
       if self.out_block:
         a_      :	int      =							self.out_block(lowerCAmelCase_							,		lowerCAmelCase_ )

       if not return_dict:
         return (sample,)

       return UNetaDOutput(sample=lowerCAmelCase_ )




'''simple docstring'''

def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







  if not head:
    return True
  # split the list to two parts
  a_      , a_      :	Union[str, Any]      =							head.next, head
  while fast and fast.next:
    a_      :	Dict      =							fast.next.next
    a_      :	List[str]      =							slow.next
  a_      :	str      =							slow.next
  a_      :	Optional[int]      =							None  # Don't forget here! But forget still works!
  # reverse the second part
  a_      :	Dict      =							None
  while second:
    a_      :	Any      =							second.next
    a_      :	Any      =							node
    a_      :	Optional[Any]      =							second
    a_      :	Optional[Any]      =							nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    a_      :	List[str]      =							node.next
    a_      :	Dict      =							head.next
  return True






def 	_snake_case						(      A_       :    List[str]   ):
  """simple docstring"""







  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  a_      :	Optional[Any]      =							head
  while fast and fast.next:
    a_      , a_      :	Optional[int]      =							fast.next.next, slow.next

  # 2. Push the second half into the stack
  a_      :	List[str]      =							[slow.val]
  while slow.next:
    a_      :	List[str]      =							slow.next
    stack.append(slow.val   )

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    a_      :	Union[str, Any]      =							cur.next

  return True






def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







  if not head or not head.next:
    return True
  a_      :	List[Any]      =							{}
  a_      :	int      =							0
  while head:
    if head.val in d:
      d[head.val].append(A_   )
    else:
      a_      :	int      =							[pos]
    a_      :	List[str]      =							head.next
    pos += 1
  a_      :	str      =							pos - 1
  a_      :	List[str]      =							0
  for v in d.values():
    if len(A_   ) % 2 != 0:
      middle += 1
    else:
      a_      :	Optional[int]      =							0
      for i in range(0     ,		len(A_   )   ):
        if v[i] + v[len(A_   ) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True


