"""simple docstring"""







from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   torch.FloatTensor







class 	UpperCamelCase    (					lowercase   ,			lowercase		):



				@register_to_config
				def __init__(self       :   str			,						_A       :   int = 16			,						_A       :   int = 88			,						_A       :   Optional[int] = None			,						_A       :   Optional[int] = None			,						_A       :   int = 1			,						_A       :   float = 0.0			,						_A       :   int = 32			,						_A       :   Optional[int] = None			,						_A       :   bool = False			,						_A       :   Optional[int] = None			,						_A       :   str = "geglu"			,						_A       :   bool = True			,						_A       :   bool = True			,						)					->			Union[str, Any]:
					super().__init__()
					__snake_case    :							Dict   	=				num_attention_heads
					__snake_case    :							List[str]   	=				attention_head_dim
					__snake_case    :							int   	=				num_attention_heads * attention_head_dim

					__snake_case    :							str   	=				in_channels

					__snake_case    :							str   	=				torch.nn.GroupNorm(num_groups=_A			,						num_channels=_A			,						eps=1E-6			,						affine=_A)
					__snake_case    :							Optional[int]   	=				nn.Linear(_A			,						_A)

					# 3. Define transformers blocks
					__snake_case    :							Union[str, Any]   	=				nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            _A			,						_A			,						_A			,						dropout=_A			,						cross_attention_dim=_A			,						activation_fn=_A			,						attention_bias=_A			,						double_self_attention=_A			,						norm_elementwise_affine=_A			,						)
					        for d in range(_A)
					    ])

					__snake_case    :							str   	=				nn.Linear(_A			,						_A)






				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   Tuple=None			,						_A       :   Tuple=None			,						_A       :   Tuple=None			,						_A       :   Union[str, Any]=1			,						_A       :   Tuple=None			,						_A       :   bool = True			,						)					->			List[Any]:
					__snake_case    :							List[str]   	=				hidden_states.shape
					__snake_case    :							str   	=				batch_frames // num_frames

					__snake_case    :							List[Any]   	=				hidden_states

					__snake_case    :							Optional[Any]   	=				hidden_states[None, :].reshape(_A			,						_A			,						_A			,						_A			,						_A)
					__snake_case    :							Optional[Any]   	=				hidden_states.permute(0			,						2			,						1			,						3			,						4)

					__snake_case    :							Union[str, Any]   	=				self.norm(_A)
					__snake_case    :							Tuple   	=				hidden_states.permute(0			,						3			,						4			,						2			,						1).reshape(batch_size * height * width			,						_A			,						_A)

					__snake_case    :							Dict   	=				self.proj_in(_A)

					# 2. Blocks
					for block in self.transformer_blocks:
						__snake_case    :							List[str]   	=				block(
						    _A			,						encoder_hidden_states=_A			,						timestep=_A			,						cross_attention_kwargs=_A			,						class_labels=_A			,						)

					# 3. Output
					__snake_case    :							List[Any]   	=				self.proj_out(_A)
					__snake_case    :							Union[str, Any]   	=				(
					    hidden_states[None, None, :]
					    .reshape(_A			,						_A			,						_A			,						_A			,						_A)
					    .permute(0			,						3			,						4			,						1			,						2)
					    .contiguous()
					)
					__snake_case    :							Any   	=				hidden_states.reshape(_A			,						_A			,						_A			,						_A)

					__snake_case    :							Tuple   	=				hidden_states + residual

					if not return_dict:
						return (output,)

					return TransformerTemporalModelOutput(sample=_A)






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



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



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





