



from typing import TYPE_CHECKING

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


lowercase__     							=						{"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__     							=						["""FNetTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__     							=						["""FNetTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__     							=						[
				    """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """FNetForMaskedLM""",
				    """FNetForMultipleChoice""",
				    """FNetForNextSentencePrediction""",
				    """FNetForPreTraining""",
				    """FNetForQuestionAnswering""",
				    """FNetForSequenceClassification""",
				    """FNetForTokenClassification""",
				    """FNetLayer""",
				    """FNetModel""",
				    """FNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet import FNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet_fast import FNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fnet import (
								    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FNetForMaskedLM,
								    FNetForMultipleChoice,
								    FNetForNextSentencePrediction,
								    FNetForPreTraining,
								    FNetForQuestionAnswering,
								    FNetForSequenceClassification,
								    FNetForTokenClassification,
								    FNetLayer,
								    FNetModel,
								    FNetPreTrainedModel,
								)


else:
				import sys

				lowercase__     							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from __future__ import annotations






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] | None = None )  ->list[list[str]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		word_bank or []
			# create a table
			lowerCamelCase__ :      int								=		len(UpperCamelCase ) + 1

			lowerCamelCase__ :      list[list[list[str]]]								=		[]
			for _ in range(UpperCamelCase ):
						table.append([] )
			# seed value
			lowerCamelCase__ :      Tuple								=		[[]]  # because empty string has empty combination

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

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

			return table[len(UpperCamelCase )]


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'''],
				    )
				)



