




"""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 			_snake_case					(				__snake_case							):

				'''simple docstring'''




				UpperCamelCase__		  =42



class 			_snake_case					(				__snake_case				,    __snake_case							):

				'''simple docstring'''




				@register_to_config
				def __init__(				self   :	List[Any]       ,				snake_case   :	int = 65_536       ,				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       ,				):
								super().__init__()
								UpperCAmelCase_       :Tuple         =   sample_size

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

								if use_timestep_embedding:
												UpperCAmelCase_       :List[Any]         =   block_out_channels[0] * 4
												UpperCAmelCase_       :List[Any]         =   TimestepEmbedding(
												    in_channels=snake_case       ,				time_embed_dim=snake_case       ,				act_fn=snake_case       ,				out_dim=block_out_channels[0]       ,				)

								UpperCAmelCase_       :List[Any]         =   nn.ModuleList([]  )
								UpperCAmelCase_       :List[str]         =   None
								UpperCAmelCase_       :str         =   nn.ModuleList([]  )
								UpperCAmelCase_       :str         =   None

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

												if i == 0:
																input_channel += extra_in_channels

												UpperCAmelCase_       :List[str]         =   i == len(snake_case  ) - 1

												UpperCAmelCase_       :Dict         =   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
								UpperCAmelCase_       :Optional[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
								UpperCAmelCase_       :Optional[int]         =   list(reversed(snake_case  )  )
								UpperCAmelCase_       :Any         =   reversed_block_out_channels[0]
								if out_block_type is None:
												UpperCAmelCase_       :Optional[Any]         =   out_channels
								else:
												UpperCAmelCase_       :Any         =   block_out_channels[0]

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

												UpperCAmelCase_       :int         =   i == len(snake_case  ) - 1

												UpperCAmelCase_       :Optional[int]         =   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  )
												UpperCAmelCase_       :List[str]         =   output_channel

								# out
								UpperCAmelCase_       :str         =   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,				32  )
								UpperCAmelCase_       :Optional[Any]         =   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 					snake_case_			(				self   :	Any       ,				snake_case   :	torch.FloatTensor       ,				snake_case   :	Union[torch.Tensor, float, int]       ,				snake_case   :	bool = True       ,				):
								UpperCAmelCase_       :Union[str, Any]         =   timestep
								if not torch.is_tensor(snake_case  ):
												UpperCAmelCase_       :Optional[Any]         =   torch.tensor([timesteps]       ,				dtype=torch.long       ,				device=sample.device  )
								elif torch.is_tensor(snake_case  ) and len(timesteps.shape  ) == 0:
												UpperCAmelCase_       :List[Any]         =   timesteps[None].to(sample.device  )

								UpperCAmelCase_       :Dict         =   self.time_proj(snake_case  )
								if self.config.use_timestep_embedding:
												UpperCAmelCase_       :List[Any]         =   self.time_mlp(snake_case  )
								else:
												UpperCAmelCase_       :Tuple         =   timestep_embed[..., None]
												UpperCAmelCase_       :int         =   timestep_embed.repeat([1, 1, sample.shape[2]]  ).to(sample.dtype  )
												UpperCAmelCase_       :Union[str, Any]         =   timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])  )

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

								# 3. mid
								if self.mid_block:
												UpperCAmelCase_       :List[str]         =   self.mid_block(snake_case       ,				snake_case  )

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks  ):
												UpperCAmelCase_       :Dict         =   down_block_res_samples[-1:]
												UpperCAmelCase_       :List[str]         =   down_block_res_samples[:-1]
												UpperCAmelCase_       :List[str]         =   upsample_block(snake_case       ,				res_hidden_states_tuple=snake_case       ,				temb=snake_case  )

								# 5. post-process
								if self.out_block:
												UpperCAmelCase_       :Any         =   self.out_block(snake_case       ,				snake_case  )

								if not return_dict:
												return (sample,)

								return UNetaDOutput(sample=snake_case  )




from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




