
'''simple docstring'''

from __future__ import annotations







def 	A  (   UpperCamelCase_       :  list[int | str]   )       ->							None:







			'''simple docstring'''



			create_state_space_tree(UpperCamelCase_    ,						[]    ,						0    ,						[0 for i in range(len(UpperCamelCase_   )   )]   )







def 	A  (   UpperCamelCase_       :  list[int | str]    ,						UpperCamelCase_       :  list[int | str]    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  list[int]    ,						)       ->							None:







			'''simple docstring'''



			if index == len(UpperCamelCase_   ):
						print(UpperCamelCase_   )
						return

			for i in range(len(UpperCamelCase_   )   ):
						if not index_used[i]:
									current_sequence.append(sequence[i]   )
									lowerCAmelCase__										=				True
									create_state_space_tree(UpperCamelCase_    ,						UpperCamelCase_    ,						index + 1    ,						UpperCamelCase_   )
									current_sequence.pop()
									lowerCAmelCase__										=				False
UpperCAmelCase__ :			list[int | str]											=		[3, 1, 2, 4]
generate_all_permutations(sequence)

UpperCAmelCase__ :			list[int | str]											=		["A", "B", "C"]



generate_all_permutations(sequence_a)












'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

