



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase    :		Optional[Any]		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Tuple		      =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[int]		=		'pegasus'
						UpperCAmelCase			:		str		=		['past_key_values']
						UpperCAmelCase			:		str		=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







						def __init__(  self    :				Dict       ,	snake_case    :				Tuple=50265       ,	snake_case    :				List[str]=1024       ,	snake_case    :				Optional[Any]=12       ,	snake_case    :				List[str]=4096       ,	snake_case    :				Optional[Any]=16       ,	snake_case    :				Optional[int]=12       ,	snake_case    :				Any=4096       ,	snake_case    :				Any=16       ,	snake_case    :				List[Any]=0.0       ,	snake_case    :				List[Any]=0.0       ,	snake_case    :				Tuple=True       ,	snake_case    :				Any=True       ,	snake_case    :				Optional[int]="gelu"       ,	snake_case    :				str=1024       ,	snake_case    :				List[Any]=0.1       ,	snake_case    :				List[Any]=0.0       ,	snake_case    :				List[str]=0.0       ,	snake_case    :				str=0.02       ,	snake_case    :				str=0       ,	snake_case    :				Dict=False       ,	snake_case    :				Optional[int]=0       ,	snake_case    :				str=1       ,	snake_case    :				Optional[Any]=1       ,	**snake_case    :				Dict       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     str						=				d_model
											SCREAMING_SNAKE_CASE							:     Dict						=				encoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Dict						=				encoder_layers
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				encoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				decoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Tuple						=				decoder_layers
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				decoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				dropout
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				attention_dropout
											SCREAMING_SNAKE_CASE							:     Dict						=				activation_dropout
											SCREAMING_SNAKE_CASE							:     str						=				activation_function
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				init_std
											SCREAMING_SNAKE_CASE							:     Any						=				encoder_layerdrop
											SCREAMING_SNAKE_CASE							:     List[str]						=				decoder_layerdrop
											SCREAMING_SNAKE_CASE							:     str						=				use_cache
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				encoder_layers
											SCREAMING_SNAKE_CASE							:     Dict						=				scale_embedding  # scale factor will be sqrt(d_model) if True
											super().__init__(
											    pad_token_id=snake_case       ,	eos_token_id=snake_case       ,	is_encoder_decoder=snake_case       ,	decoder_start_token_id=snake_case       ,	forced_eos_token_id=snake_case       ,	**snake_case       ,	)







						@property
						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											return self.encoder_attention_heads







						@property
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											return self.d_model




from __future__ import annotations

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = None   )     ->     list[list[str]]:
					SCREAMING_SNAKE_CASE							:     Dict						=				word_bank or []
					# create a table
					SCREAMING_SNAKE_CASE							:     int						=				len(__lowerCAmelCase   ) + 1

					SCREAMING_SNAKE_CASE							:     list[list[list[str]]]						=				[]
					for _ in range(__lowerCAmelCase   ):
										table.append([]   )
					# seed value
					SCREAMING_SNAKE_CASE							:     Tuple						=				[[]]  # 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:
																									SCREAMING_SNAKE_CASE							:     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"""],
			    )
			)