





'''simple docstring'''




from __future__ import annotations






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]      )					->					None:
     create_state_space_tree(__snake_case			,							[]			,							0			,							[0 for i in range(len(__snake_case      )      )]      )






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]			,							__snake_case  :			list[int | str]			,							__snake_case  :			int			,							__snake_case  :			list[int]			,							)					->					None:

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

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

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







generate_all_permutations(sequence_a)






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


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

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

          __A						:			Union[str, Any]								=	vocab_size
          __A						:			List[str]								=	hidden_size
          __A						:			Tuple								=	num_hidden_layers
          __A						:			List[str]								=	num_attention_heads
          __A						:			Tuple								=	hidden_act
          __A						:			Optional[Any]								=	intermediate_size
          __A						:			List[Any]								=	hidden_dropout_prob
          __A						:			Union[str, Any]								=	attention_probs_dropout_prob
          __A						:			Dict								=	max_position_embeddings
          __A						:			Any								=	type_vocab_size
          __A						:			Optional[int]								=	initializer_range
          __A						:			List[str]								=	layer_norm_eps
          __A						:			Optional[int]								=	position_embedding_type
          __A						:			Dict								=	use_cache
          __A						:			List[str]								=	classifier_dropout




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						:			List[str]								=	{0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						:			Tuple								=	{0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])