




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A   :  Optional[Any]					=      {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A   :  Optional[int]					=      ['DebertaTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A   :  int					=      [
						    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DebertaForMaskedLM',
						    'DebertaForQuestionAnswering',
						    'DebertaForSequenceClassification',
						    'DebertaForTokenClassification',
						    'DebertaModel',
						    'DebertaPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A   :  Dict					=      [
						    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDebertaForMaskedLM',
						    'TFDebertaForQuestionAnswering',
						    'TFDebertaForSequenceClassification',
						    'TFDebertaForTokenClassification',
						    'TFDebertaModel',
						    'TFDebertaPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
						from .tokenization_deberta import DebertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_fast import DebertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta import (
												    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaForMaskedLM,
												    DebertaForQuestionAnswering,
												    DebertaForSequenceClassification,
												    DebertaForTokenClassification,
												    DebertaModel,
												    DebertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta import (
												    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaForMaskedLM,
												    TFDebertaForQuestionAnswering,
												    TFDebertaForSequenceClassification,
												    TFDebertaForTokenClassification,
												    TFDebertaModel,
												    TFDebertaPreTrainedModel,
												)


else:
						import sys

						__A   :  str					=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""







from __future__ import annotations

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->list[list[str]]:






		"""simple docstring"""


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

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

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

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

		return table[len(_lowerCamelCase				)]


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'],
						    )
						)
