




from __future__ import annotations


def 		lowerCamelCase__       (						_A	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	word_bank or []
	# create a table
	snake_case_						       =	len(_A				) + 1

	snake_case_						       =	[]
	for _ in range(_A				):
		table.append([]				)
	# seed value
	snake_case_						       =	[[]]  # because empty string has empty combination

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

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

	return table[len(_A				)]


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"],
		    )
		)






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__     ):







						'''simple docstring'''





						@register_to_config
						def __init__(		self				:	Tuple			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	float			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	str			,      __lowercase				:	bool = False			,      ):






							"""simple docstring"""






							super().__init__()

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)
							snake_case_						       =	False

							snake_case_						       =	nn.Dropout(p=__lowercase		)

							snake_case_						       =	TaConfig(
							    vocab_size=__lowercase			,      d_model=__lowercase			,      num_heads=__lowercase			,      d_kv=__lowercase			,      d_ff=__lowercase			,      dropout_rate=__lowercase			,      feed_forward_proj=__lowercase			,      is_decoder=__lowercase			,      is_encoder_decoder=__lowercase			,      )

							snake_case_						       =	nn.ModuleList()
							for lyr_num in range(__lowercase		):
								snake_case_						       =	TaBlock(__lowercase		)
								self.encoders.append(__lowercase		)

							snake_case_						       =	TaLayerNorm(__lowercase		)
							snake_case_						       =	nn.Dropout(p=__lowercase		)






						def 			snake_case__		(		self				:	str			,      __lowercase				:	Dict			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.token_embedder(__lowercase		)

							snake_case_						       =	encoder_input_tokens.shape[1]
							snake_case_						       =	torch.arange(__lowercase			,      device=encoder_input_tokens.device		)
							x += self.position_encoding(__lowercase		)

							snake_case_						       =	self.dropout_pre(__lowercase		)

							# inverted the attention mask
							snake_case_						       =	encoder_input_tokens.size()
							snake_case_						       =	self.get_extended_attention_mask(__lowercase			,      __lowercase		)

							for lyr in self.encoders:
								snake_case_						       =	lyr(__lowercase			,      __lowercase		)[0]
							snake_case_						       =	self.layer_norm(__lowercase		)

							return self.dropout_post(__lowercase		), encoder_inputs_mask
