



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase		       =      logging.get_logger(__name__)

lowerCamelCase		       =      {
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}


class 				_a						(       SCREAMING_SNAKE_CASE_			):





							'''simple docstring'''

							A				:List[str] =       'convbert'



							def __init__(							self ,					__UpperCAmelCase=3_0522 ,					__UpperCAmelCase=768 ,					__UpperCAmelCase=12 ,					__UpperCAmelCase=12 ,					__UpperCAmelCase=3072 ,					__UpperCAmelCase="gelu" ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=1E-12 ,					__UpperCAmelCase=1 ,					__UpperCAmelCase=0 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=768 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=9 ,					__UpperCAmelCase=1 ,					__UpperCAmelCase=None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__(
											    pad_token_id=__UpperCAmelCase ,					bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											a__	:							List[str]      						= vocab_size
											a__	:							Union[str, Any]      						= hidden_size
											a__	:							Dict      						= num_hidden_layers
											a__	:							Tuple      						= num_attention_heads
											a__	:							List[Any]      						= intermediate_size
											a__	:							Tuple      						= hidden_act
											a__	:							str      						= hidden_dropout_prob
											a__	:							int      						= attention_probs_dropout_prob
											a__	:							Union[str, Any]      						= max_position_embeddings
											a__	:							Union[str, Any]      						= type_vocab_size
											a__	:							List[str]      						= initializer_range
											a__	:							Optional[int]      						= layer_norm_eps
											a__	:							Optional[int]      						= embedding_size
											a__	:							Tuple      						= head_ratio
											a__	:							List[str]      						= conv_kernel_size
											a__	:							Optional[Any]      						= num_groups
											a__	:							Optional[Any]      						= classifier_dropout





class 				_a						(       SCREAMING_SNAKE_CASE_			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											if self.task == "multiple-choice":
															a__	:							int      						= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															a__	:							Optional[Any]      						= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											        ("token_type_ids", dynamic_axis),
											    ]				)







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"""],
			    )
			)