





"""simple docstring"""


from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				"DiagonalGaussianDistribution"






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  True







			@register_to_config
			def __init__(      self						,   _A = 3						,   _A = 3						,   _A = ("DownEncoderBlock2D",)						,   _A = ("UpDecoderBlock2D",)						,   _A = (6_4,)						,   _A = 1						,   _A = "silu"						,   _A = 4						,   _A = 3_2						,   _A = 3_2						,   _A = 0.1_82_15						,   ):






									'''simple docstring'''







									super().__init__()

									# pass init params to Encoder
									UpperCamelCase				:		str				   =			Encoder(
									    in_channels=_A						,   out_channels=_A						,   down_block_types=_A						,   block_out_channels=_A						,   layers_per_block=_A						,   act_fn=_A						,   norm_num_groups=_A						,   double_z=_A						,   )

									# pass init params to Decoder
									UpperCamelCase				:		Any				   =			Decoder(
									    in_channels=_A						,   out_channels=_A						,   up_block_types=_A						,   block_out_channels=_A						,   layers_per_block=_A						,   norm_num_groups=_A						,   act_fn=_A						,   )

									UpperCamelCase				:		Union[str, Any]				   =			nn.Convad(2 * latent_channels						,   2 * latent_channels						,   1    )
									UpperCamelCase				:		Any				   =			nn.Convad(_A						,   _A						,   1    )

									UpperCamelCase				:		Optional[Any]				   =			False
									UpperCamelCase				:		Optional[int]				   =			False

									# only relevant if vae tiling is enabled
									UpperCamelCase				:		Union[str, Any]				   =			self.config.sample_size
									UpperCamelCase				:		Dict				   =			(
									    self.config.sample_size[0]
									    if isinstance(self.config.sample_size						,   (list, tuple)    )
									    else self.config.sample_size
									)
									UpperCamelCase				:		Optional[Any]				   =			int(sample_size / (2 ** (len(self.config.block_out_channels    ) - 1))    )
									UpperCamelCase				:		Optional[int]				   =			0.25







			def   _a      (      self						,   _A						,   _A=False    ):






									'''simple docstring'''







									if isinstance(_A						,   (Encoder, Decoder)    ):
															UpperCamelCase				:		List[Any]				   =			value







			def   _a      (      self						,   _A = True    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			use_tiling







			def   _a      (      self    ):






									'''simple docstring'''







									self.enable_tiling(_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			True







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			False







			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			{}

									def fn_recursive_add_processors(_A						,   _A						,   _A    ):
															if hasattr(_A						,   """set_processor"""    ):
																					UpperCamelCase				:		List[Any]				   =			module.processor

															for sub_name, child in module.named_children():
																					fn_recursive_add_processors(f"""{name}.{sub_name}"""						,   _A						,   _A    )

															return processors

									for name, module in self.named_children():
															fn_recursive_add_processors(_A						,   _A						,   _A    )

									return processors







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			len(self.attn_processors.keys()    )

									if isinstance(_A						,   _A    ) and len(_A    ) != count:
															raise ValueError(
															    f"""A dict of processors was passed, but the number of processors {len(_A    )} does not match the"""
															    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""    )

									def fn_recursive_attn_processor(_A						,   _A						,   _A    ):
															if hasattr(_A						,   """set_processor"""    ):
																					if not isinstance(_A						,   _A    ):
																											module.set_processor(_A    )
																					else:
																											module.set_processor(processor.pop(f"""{name}.processor"""    )    )

															for sub_name, child in module.named_children():
																					fn_recursive_attn_processor(f"""{name}.{sub_name}"""						,   _A						,   _A    )

									for name, module in self.named_children():
															fn_recursive_attn_processor(_A						,   _A						,   _A    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.set_attn_processor(AttnProcessor()    )







			@apply_forward_hook
			def   _a      (      self						,   _A						,   _A = True    ):






									'''simple docstring'''







									if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
															return self.tiled_encode(_A						,   return_dict=_A    )

									if self.use_slicing and x.shape[0] > 1:
															UpperCamelCase				:		str				   =			[self.encoder(_A    ) for x_slice in x.split(1    )]
															UpperCamelCase				:		List[str]				   =			torch.cat(_A    )
									else:
															UpperCamelCase				:		str				   =			self.encoder(_A    )

									UpperCamelCase				:		str				   =			self.quant_conv(_A    )
									UpperCamelCase				:		Optional[int]				   =			DiagonalGaussianDistribution(_A    )

									if not return_dict:
															return (posterior,)

									return AutoencoderKLOutput(latent_dist=_A    )







			def   _a      (      self						,   _A						,   _A = True    ):






									'''simple docstring'''







									if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
															return self.tiled_decode(_A						,   return_dict=_A    )

									UpperCamelCase				:		Dict				   =			self.post_quant_conv(_A    )
									UpperCamelCase				:		List[Any]				   =			self.decoder(_A    )

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=_A    )







			@apply_forward_hook
			def   _a      (      self						,   _A						,   _A = True    ):






									'''simple docstring'''







									if self.use_slicing and z.shape[0] > 1:
															UpperCamelCase				:		Optional[int]				   =			[self._decode(_A    ).sample for z_slice in z.split(1    )]
															UpperCamelCase				:		Union[str, Any]				   =			torch.cat(_A    )
									else:
															UpperCamelCase				:		str				   =			self._decode(_A    ).sample

									if not return_dict:
															return (decoded,)

									return DecoderOutput(sample=_A    )







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			min(a.shape[2]						,   b.shape[2]						,   _A    )
									for y in range(_A    ):
															UpperCamelCase				:		Optional[int]				   =			a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			min(a.shape[3]						,   b.shape[3]						,   _A    )
									for x in range(_A    ):
															UpperCamelCase				:		Optional[int]				   =			a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
									return b







			def   _a      (      self						,   _A						,   _A = True    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)    )
									UpperCamelCase				:		Optional[Any]				   =			int(self.tile_latent_min_size * self.tile_overlap_factor    )
									UpperCamelCase				:		int				   =			self.tile_latent_min_size - blend_extent

									# Split the image into 512x512 tiles and encode them separately.
									UpperCamelCase				:		Any				   =			[]
									for i in range(0						,   x.shape[2]						,   _A    ):
															UpperCamelCase				:		Optional[Any]				   =			[]
															for j in range(0						,   x.shape[3]						,   _A    ):
																					UpperCamelCase				:		Optional[Any]				   =			x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																					UpperCamelCase				:		Optional[int]				   =			self.encoder(_A    )
																					UpperCamelCase				:		List[Any]				   =			self.quant_conv(_A    )
																					row.append(_A    )
															rows.append(_A    )
									UpperCamelCase				:		Union[str, Any]				   =			[]
									for i, row in enumerate(_A    ):
															UpperCamelCase				:		Any				   =			[]
															for j, tile in enumerate(_A    ):
																					# blend the above tile and the left tile
																					# to the current tile and add the current tile to the result row
																					if i > 0:
																											UpperCamelCase				:		Optional[int]				   =			self.blend_v(rows[i - 1][j]						,   _A						,   _A    )
																					if j > 0:
																											UpperCamelCase				:		Any				   =			self.blend_h(row[j - 1]						,   _A						,   _A    )
																					result_row.append(tile[:, :, :row_limit, :row_limit]    )
															result_rows.append(torch.cat(_A						,   dim=3    )    )

									UpperCamelCase				:		Union[str, Any]				   =			torch.cat(_A						,   dim=2    )
									UpperCamelCase				:		str				   =			DiagonalGaussianDistribution(_A    )

									if not return_dict:
															return (posterior,)

									return AutoencoderKLOutput(latent_dist=_A    )







			def   _a      (      self						,   _A						,   _A = True    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)    )
									UpperCamelCase				:		Optional[Any]				   =			int(self.tile_sample_min_size * self.tile_overlap_factor    )
									UpperCamelCase				:		Any				   =			self.tile_sample_min_size - blend_extent

									# Split z into overlapping 64x64 tiles and decode them separately.
									# The tiles have an overlap to avoid seams between tiles.
									UpperCamelCase				:		Tuple				   =			[]
									for i in range(0						,   z.shape[2]						,   _A    ):
															UpperCamelCase				:		Optional[int]				   =			[]
															for j in range(0						,   z.shape[3]						,   _A    ):
																					UpperCamelCase				:		List[str]				   =			z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																					UpperCamelCase				:		Optional[Any]				   =			self.post_quant_conv(_A    )
																					UpperCamelCase				:		List[str]				   =			self.decoder(_A    )
																					row.append(_A    )
															rows.append(_A    )
									UpperCamelCase				:		List[str]				   =			[]
									for i, row in enumerate(_A    ):
															UpperCamelCase				:		Tuple				   =			[]
															for j, tile in enumerate(_A    ):
																					# blend the above tile and the left tile
																					# to the current tile and add the current tile to the result row
																					if i > 0:
																											UpperCamelCase				:		List[Any]				   =			self.blend_v(rows[i - 1][j]						,   _A						,   _A    )
																					if j > 0:
																											UpperCamelCase				:		Any				   =			self.blend_h(row[j - 1]						,   _A						,   _A    )
																					result_row.append(tile[:, :, :row_limit, :row_limit]    )
															result_rows.append(torch.cat(_A						,   dim=3    )    )

									UpperCamelCase				:		Tuple				   =			torch.cat(_A						,   dim=2    )
									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=_A    )







			def   _a      (      self						,   _A						,   _A = False						,   _A = True						,   _A = None						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			sample
									UpperCamelCase				:		Union[str, Any]				   =			self.encode(_A    ).latent_dist
									if sample_posterior:
															UpperCamelCase				:		Optional[Any]				   =			posterior.sample(generator=_A    )
									else:
															UpperCamelCase				:		Dict				   =			posterior.mode()
									UpperCamelCase				:		Any				   =			self.decode(_A    ).sample

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=_A    )








"""simple docstring"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str		)							->					None:
									snake_case   :			Optional[int]						    = self._trie
									for char in text:
															if char not in trie:
																					snake_case   :			str						    = {}
															snake_case   :			Optional[Any]						    = trie[char]
									snake_case   :			str						    = True



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



