



"""simple docstring"""






from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_lowercase			:			Dict										=						{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'ernie_m'
					_a             =							{'dropout': 'classifier_dropout', 'num_classes': 'num_labels'}



					def __init__(	self							:  Tuple,  lowerCamelCase							:  int = 25_0002,  lowerCamelCase							:  int = 768,  lowerCamelCase							:  int = 12,  lowerCamelCase							:  int = 12,  lowerCamelCase							:  int = 3072,  lowerCamelCase							:  str = "gelu",  lowerCamelCase							:  float = 0.1,  lowerCamelCase							:  float = 0.1,  lowerCamelCase							:  int = 514,  lowerCamelCase							:  float = 0.02,  lowerCamelCase							:  int = 1,  lowerCamelCase							:  float = 1E-05,  lowerCamelCase							:  Optional[int]=None,  lowerCamelCase							:  Any=False,  lowerCamelCase							:  Dict=0.0,  **lowerCamelCase							:  Dict,  )-> Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase,  **lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =vocab_size
								lowerCamelCase__		:		Optional[int]		     =hidden_size
								lowerCamelCase__		:		Optional[Any]		     =num_hidden_layers
								lowerCamelCase__		:		Dict		     =num_attention_heads
								lowerCamelCase__		:		Optional[Any]		     =intermediate_size
								lowerCamelCase__		:		Tuple		     =hidden_act
								lowerCamelCase__		:		Optional[Any]		     =hidden_dropout_prob
								lowerCamelCase__		:		int		     =attention_probs_dropout_prob
								lowerCamelCase__		:		Tuple		     =max_position_embeddings
								lowerCamelCase__		:		str		     =initializer_range
								lowerCamelCase__		:		List[Any]		     =layer_norm_eps
								lowerCamelCase__		:		Tuple		     =classifier_dropout
								lowerCamelCase__		:		Any		     =is_decoder
								lowerCamelCase__		:		str		     =act_dropout









"""simple docstring"""






from __future__ import annotations






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   list[str] | None = None     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =word_bank or []
			# create a table
			lowerCamelCase__		:		int		     =len(__lowerCamelCase     ) + 1

			lowerCamelCase__		:		list[list[list[str]]]		     =[]
			for _ in range(__lowerCamelCase     ):
						table.append([]     )
			# seed value
			lowerCamelCase__		:		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:
															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(__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"],
		    )
		)




