




from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase						:							Union[str, Any]			     =						logging.get_logger(__name__)

__lowerCAmelCase						:							Optional[int]			     =						{
    '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        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				int									=   """flaubert"""
				SCREAMING_SNAKE_CASE_			:				str									=   {
				    """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[str]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	List[str]=0.0   , __lowerCamelCase							:	Optional[Any]=3_01_45   , __lowerCamelCase							:	List[str]=20_48   , __lowerCamelCase							:	Optional[Any]=12   , __lowerCamelCase							:	List[Any]=16   , __lowerCamelCase							:	Tuple=0.1   , __lowerCamelCase							:	Tuple=0.1   , __lowerCamelCase							:	Optional[Any]=True   , __lowerCamelCase							:	Union[str, Any]=False   , __lowerCamelCase							:	Optional[Any]=False   , __lowerCamelCase							:	List[str]=False   , __lowerCamelCase							:	Any=1   , __lowerCamelCase							:	Dict=True   , __lowerCamelCase							:	Any=5_12   , __lowerCamelCase							:	Union[str, Any]=20_48**-0.5   , __lowerCamelCase							:	Dict=1e-12   , __lowerCamelCase							:	List[Any]=0.02   , __lowerCamelCase							:	Optional[Any]=0   , __lowerCamelCase							:	Dict=1   , __lowerCamelCase							:	Tuple=2   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	List[Any]=5   , __lowerCamelCase							:	int=True   , __lowerCamelCase							:	str="first"   , __lowerCamelCase							:	int=True   , __lowerCamelCase							:	Optional[int]=None   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[Any]=0.1   , __lowerCamelCase							:	int=5   , __lowerCamelCase							:	Union[str, Any]=5   , __lowerCamelCase							:	Optional[int]=0   , __lowerCamelCase							:	Tuple=0   , __lowerCamelCase							:	Union[str, Any]=2   , __lowerCamelCase							:	Union[str, Any]=0   , **__lowerCamelCase							:	str   , )   ->	Any:
							a       =     pre_norm
							a       =     layerdrop
							a       =     vocab_size
							a       =     emb_dim
							a       =     n_layers
							a       =     n_heads
							a       =     dropout
							a       =     attention_dropout
							a       =     gelu_activation
							a       =     sinusoidal_embeddings
							a       =     causal
							a       =     asm
							a       =     n_langs
							a       =     use_lang_emb
							a       =     layer_norm_eps
							a       =     bos_index
							a       =     eos_index
							a       =     pad_index
							a       =     unk_index
							a       =     mask_index
							a       =     is_encoder
							a       =     max_position_embeddings
							a       =     embed_init_std
							a       =     init_std
							a       =     summary_type
							a       =     summary_use_proj
							a       =     summary_activation
							a       =     summary_proj_to_labels
							a       =     summary_first_dropout
							a       =     start_n_top
							a       =     end_n_top
							a       =     mask_token_id
							a       =     lang_id

							if "n_words" in kwargs:
										a       =     kwargs["n_words"]

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




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				@property
				def 				__UpperCAmelCase				( self							:	int				)   ->	Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										a       =     {0: "batch", 1: "choice", 2: "sequence"}
							else:
										a       =     {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]				)












class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	List[str]   , __lowerCamelCase							:	int				)   ->	None:
							a       =     data
							a       =     42
							a       =     42




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     0
			a       =     x




def      __magic_name__							(					A						:   Node,    A						:   Node  ):






			'''simple docstring'''







			a       ,						a       =     find_set(A  ), find_set(A  )
			if x == y:
						return

			elif x.rank > y.rank:
						a       =     x
			else:
						a       =     y
						if x.rank == y.rank:
									y.rank += 1




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			if x != x.parent:
						a       =     find_set(x.parent  )
			return x.parent




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     ({0, 1, 2}, {3, 4, 5})
			for s in sets:
						if node.data in s:
									return s
			a       =     F"""{node.data} is not in {sets}"""
			raise ValueError(A  )




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     [Node(A  ) for i in range(6  )]
			for v in vertex:
						make_set(A  )

			union_set(vertex[0],    vertex[1]  )
			union_set(vertex[1],    vertex[2]  )
			union_set(vertex[3],    vertex[4]  )
			union_set(vertex[3],    vertex[5]  )

			for nodea in vertex:
						for nodea in vertex:
									if find_python_set(A  ).isdisjoint(find_python_set(A  )  ):
												assert find_set(A  ) != find_set(A  )
									else:
												assert find_set(A  ) == find_set(A  )


if __name__ == "__main__":
		test_disjoint_set()






