



'''simple docstring'''

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		    =     {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}

class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					Dict            =      'flaubert'
							lowerCAmelCase :					Dict            =      {
							    'hidden_size': 'emb_dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							    'n_words': 'vocab_size',  # For backward compatibility
							}







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : Tuple=False			,_UpperCAmelCase      : Union[str, Any]=0.0			,_UpperCAmelCase      : int=30145			,_UpperCAmelCase      : Union[str, Any]=2048			,_UpperCAmelCase      : Dict=12			,_UpperCAmelCase      : Any=16			,_UpperCAmelCase      : Any=0.1			,_UpperCAmelCase      : Dict=0.1			,_UpperCAmelCase      : Optional[int]=True			,_UpperCAmelCase      : Dict=False			,_UpperCAmelCase      : List[Any]=False			,_UpperCAmelCase      : Optional[int]=False			,_UpperCAmelCase      : str=1			,_UpperCAmelCase      : List[Any]=True			,_UpperCAmelCase      : Tuple=512			,_UpperCAmelCase      : Union[str, Any]=2048**-0.5			,_UpperCAmelCase      : List[Any]=1E-12			,_UpperCAmelCase      : Optional[int]=0.02			,_UpperCAmelCase      : str=0			,_UpperCAmelCase      : int=1			,_UpperCAmelCase      : Optional[int]=2			,_UpperCAmelCase      : Optional[int]=3			,_UpperCAmelCase      : Optional[int]=5			,_UpperCAmelCase      : str=True			,_UpperCAmelCase      : Tuple="first"			,_UpperCAmelCase      : List[Any]=True			,_UpperCAmelCase      : Any=None			,_UpperCAmelCase      : str=True			,_UpperCAmelCase      : List[str]=0.1			,_UpperCAmelCase      : Any=5			,_UpperCAmelCase      : int=5			,_UpperCAmelCase      : Optional[int]=0			,_UpperCAmelCase      : List[Any]=0			,_UpperCAmelCase      : Optional[Any]=2			,_UpperCAmelCase      : Dict=0			,**_UpperCAmelCase      : Tuple			,):
									_a  :   int				 =							pre_norm
									_a  :   Optional[int]				 =							layerdrop
									_a  :   List[str]				 =							vocab_size
									_a  :   str				 =							emb_dim
									_a  :   Tuple				 =							n_layers
									_a  :   Union[str, Any]				 =							n_heads
									_a  :   Any				 =							dropout
									_a  :   Dict				 =							attention_dropout
									_a  :   Dict				 =							gelu_activation
									_a  :   Dict				 =							sinusoidal_embeddings
									_a  :   Tuple				 =							causal
									_a  :   Union[str, Any]				 =							asm
									_a  :   Optional[int]				 =							n_langs
									_a  :   str				 =							use_lang_emb
									_a  :   Optional[int]				 =							layer_norm_eps
									_a  :   int				 =							bos_index
									_a  :   Optional[int]				 =							eos_index
									_a  :   List[Any]				 =							pad_index
									_a  :   List[str]				 =							unk_index
									_a  :   Tuple				 =							mask_index
									_a  :   Optional[int]				 =							is_encoder
									_a  :   Optional[Any]				 =							max_position_embeddings
									_a  :   List[str]				 =							embed_init_std
									_a  :   Union[str, Any]				 =							init_std
									_a  :   Any				 =							summary_type
									_a  :   Dict				 =							summary_use_proj
									_a  :   Dict				 =							summary_activation
									_a  :   int				 =							summary_proj_to_labels
									_a  :   Union[str, Any]				 =							summary_first_dropout
									_a  :   Optional[int]				 =							start_n_top
									_a  :   Optional[Any]				 =							end_n_top
									_a  :   Optional[int]				 =							mask_token_id
									_a  :   Any				 =							lang_id

									if "n_words" in kwargs:
											_a  :   Union[str, Any]				 =							kwargs['n_words']

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

class 			__magic_name__		(					_UpperCamelCase     ):







							@property
							def 							__lowercase    (							self      : Dict	):
									if self.task == "multiple-choice":
											_a  :   int				 =							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a  :   Optional[int]				 =							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]	)









'''simple docstring'''

from __future__ import annotations

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : int	):
									_a  :   Tuple				 =							value
									_a  :   Node | None				 =							None
									_a  :   Node | None				 =							None

class 			__magic_name__		:
							lowerCAmelCase :					int







							def __init__(							self      : Union[str, Any]	):
									_a  :   Tuple				 =							0







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int	):
									if node is None:
											return

									if path_sum == self.target:
											self.paths += 1

									if node.left:
											self.depth_first_search(node.left			,path_sum + node.left.value	)
									if node.right:
											self.depth_first_search(node.right			,path_sum + node.right.value	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int | None = None	):
									if node is None:
											return 0
									if target is not None:
											_a  :   Any				 =							target

									self.depth_first_search(_UpperCAmelCase			,node.value	)
									self.path_sum(node.left	)
									self.path_sum(node.right	)

									return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()




