
"""simple docstring"""






from __future__ import annotations






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  list[str] | None = None		)  ->				list[list[str]]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  word_bank or []
			# create a table
			lowerCAmelCase_    :int			     =  len(lowercase__		) + 1

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

			# iterate through the indices
			for i in range(lowercase__		):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(lowercase__		)] == word:
															lowerCAmelCase_    :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(lowercase__		)] += new_combinations

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

			return table[len(lowercase__		)]


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'],
				    )
				)













'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

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




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= 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]

								_a								= 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, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= 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"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

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

								return text_encoder_hidden_states, additive_clip_time_embeddings
