
"""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  ):
     A__      =			42
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     @register_to_config
     def __init__(     self  ,     snake_case__ = 65536  ,     snake_case__ = None  ,     snake_case__ = 2  ,     snake_case__ = 2  ,     snake_case__ = 0  ,     snake_case__ = "fourier"  ,     snake_case__ = True  ,     snake_case__ = False  ,     snake_case__ = 0.0  ,     snake_case__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")  ,     snake_case__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")  ,     snake_case__ = "UNetMidBlock1D"  ,     snake_case__ = None  ,     snake_case__ = (32, 32, 64)  ,     snake_case__ = None  ,     snake_case__ = 8  ,     snake_case__ = 1  ,     snake_case__ = False  ,     ):




       """simple docstring"""


       super().__init__()
       _SCREAMING_SNAKE_CASE   :				Tuple							    = sample_size

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

       if use_timestep_embedding:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = block_out_channels[0] * 4
         _SCREAMING_SNAKE_CASE   :				List[str]							    = TimestepEmbedding(
             in_channels=snake_case__  ,     time_embed_dim=snake_case__  ,     act_fn=snake_case__  ,     out_dim=block_out_channels[0]  ,     )

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

       # down
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = in_channels
       for i, down_block_type in enumerate(snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Dict							    = output_channel
         _SCREAMING_SNAKE_CASE   :				int							    = block_out_channels[i]

         if i == 0:
           input_channel += extra_in_channels

         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = i == len(snake_case__				) - 1

         _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_down_block(
             snake_case__  ,     num_layers=snake_case__  ,     in_channels=snake_case__  ,     out_channels=snake_case__  ,     temb_channels=block_out_channels[0]  ,     add_downsample=not is_final_block or downsample_each_block  ,     )
         self.down_blocks.append(snake_case__				)

       # mid
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_mid_block(
           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=snake_case__  ,     add_downsample=snake_case__  ,     )

       # up
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = reversed_block_out_channels[0]
       if out_block_type is None:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = out_channels
       else:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = block_out_channels[0]

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

         _SCREAMING_SNAKE_CASE   :				int							    = i == len(snake_case__				) - 1

         _SCREAMING_SNAKE_CASE   :				Dict							    = get_up_block(
             snake_case__  ,     num_layers=snake_case__  ,     in_channels=snake_case__  ,     out_channels=snake_case__  ,     temb_channels=block_out_channels[0]  ,     add_upsample=not is_final_block  ,     )
         self.up_blocks.append(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				int							    = output_channel

       # out
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,     32				)
       _SCREAMING_SNAKE_CASE   :				int							    = get_out_block(
           out_block_type=snake_case__  ,     num_groups_out=snake_case__  ,     embed_dim=block_out_channels[0]  ,     out_channels=snake_case__  ,     act_fn=snake_case__  ,     fc_dim=block_out_channels[-1] // 4  ,     )




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = True  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = timestep
       if not torch.is_tensor(snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = torch.tensor([timesteps]  ,     dtype=torch.long  ,     device=sample.device				)
       elif torch.is_tensor(snake_case__				) and len(timesteps.shape				) == 0:
         _SCREAMING_SNAKE_CASE   :				Dict							    = timesteps[None].to(sample.device				)

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

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

       # 3. mid
       if self.mid_block:
         _SCREAMING_SNAKE_CASE   :				Any							    = self.mid_block(snake_case__  ,     snake_case__				)

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

       # 5. post-process
       if self.out_block:
         _SCREAMING_SNAKE_CASE   :				Dict							    = self.out_block(snake_case__  ,     snake_case__				)

       if not return_dict:
         return (sample,)

       return UNetaDOutput(sample=snake_case__				)





"""simple docstring"""
from __future__ import annotations

lowercase_       :						List[str]     					=	'''#'''
class 			UpperCamelCase      :
     def __init__(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				dict							    = {}
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self._trie
       for char in text:
         if char not in trie:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = {}
         _SCREAMING_SNAKE_CASE   :				List[str]							    = trie[char]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = True
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self._trie
       for char in prefix:
         if char in trie:
           _SCREAMING_SNAKE_CASE   :				str							    = trie[char]
         else:
           return []
       return self._elements(snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = []
       for c, v in d.items():
         _SCREAMING_SNAKE_CASE   :				int							    = [" "] if c == END else [(c + s) for s in self._elements(snake_case__				)]
         result.extend(snake_case__				)
       return tuple(snake_case__				)
lowercase_       :						Union[str, Any]     					=	Trie()
lowercase_       :						Optional[Any]     					=	('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
      trie.insert_word(word)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = trie.find_word(lowerCamelCase__				)
  return tuple(string + word for word in suffixes				)





def    _lowerCAmelCase    (     )  ->    None:
  print(autocomplete_using_trie("de"				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



