



from __future__ import annotations

snake_case__					:     Any    		=   """#"""

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			{}



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			self._trie
							for char in text:
								if char not in trie:
									__lowercase		       =			{}
								__lowercase		       =			trie[char]
							__lowercase		       =			True



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			self._trie
							for char in prefix:
								if char in trie:
									__lowercase		       =			trie[char]
								else:
									return []
							return self._elements(lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							dict	):






							'''simple docstring'''



							__lowercase		       =			[]
							for c, v in d.items():
								__lowercase		       =			[" "] if c == END else [(c + s) for s in self._elements(lowerCamelCase	)]
								result.extend(lowerCamelCase	)
							return tuple(lowerCamelCase	)

snake_case__					:     str    		=   Trie()
snake_case__					:     Any    		=   ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
	trie.insert_word(word)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			trie.find_word(_SCREAMING_SNAKE_CASE   )
	return tuple(string + word for word in suffixes   )



def    snake_case_				(    ):
	print(autocomplete_using_trie("de"   )   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

							# parameters for encoder hidden states
							__lowercase		       =			clip_extra_context_tokens
							__lowercase		       =			nn.Linear(
							    lowerCamelCase							,      self.clip_extra_context_tokens * cross_attention_dim	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.LayerNorm(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowercase		       =			image_embeddings.shape[0]
								__lowercase		       =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
								__lowercase		       =			classifier_free_guidance_embeddings.expand(
								    lowerCamelCase							,      -1	)
								__lowercase		       =			torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,      dim=0	)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase		       =			prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

							__lowercase		       =			self.encoder_hidden_states_proj(lowerCamelCase	)
							__lowercase		       =			self.text_encoder_hidden_states_norm(lowerCamelCase	)
							__lowercase		       =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,      dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings



