
'''simple docstring'''







from collections import deque



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= deque()
						_SCREAMING_SNAKE_CASE				= [False for _ in range(_SCREAMING_SNAKE_CASE  )]
						_SCREAMING_SNAKE_CASE				= [-1 for _ in range(_SCREAMING_SNAKE_CASE  )]
						_SCREAMING_SNAKE_CASE				= index_of[:]

						def strong_connect(_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str  ):
												_SCREAMING_SNAKE_CASE				= index  # the number when this node is seen
												_SCREAMING_SNAKE_CASE				= index  # lowest rank node reachable from here
												index += 1
												stack.append(_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= True

												for w in g[v]:
																		if index_of[w] == -1:
																								_SCREAMING_SNAKE_CASE				= strong_connect(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
																								_SCREAMING_SNAKE_CASE				= (
																								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																								)
																		elif on_stack[w]:
																								_SCREAMING_SNAKE_CASE				= (
																								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																								)

												if lowlink_of[v] == index_of[v]:
																		_SCREAMING_SNAKE_CASE				= []
																		_SCREAMING_SNAKE_CASE				= stack.pop()
																		_SCREAMING_SNAKE_CASE				= False
																		component.append(_SCREAMING_SNAKE_CASE  )
																		while w != v:
																								_SCREAMING_SNAKE_CASE				= stack.pop()
																								_SCREAMING_SNAKE_CASE				= False
																								component.append(_SCREAMING_SNAKE_CASE  )
																		components.append(_SCREAMING_SNAKE_CASE  )
												return index

						_SCREAMING_SNAKE_CASE				= []
						for v in range(_SCREAMING_SNAKE_CASE  ):
												if index_of[v] == -1:
																		strong_connect(_SCREAMING_SNAKE_CASE	,			0	,			_SCREAMING_SNAKE_CASE  )

						return components



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						_SCREAMING_SNAKE_CASE				= [[] for _ in range(_SCREAMING_SNAKE_CASE  )]
						for u, v in edges:
												g[u].append(_SCREAMING_SNAKE_CASE  )
						return g


if __name__ == "__main__":
						# Test
						_snake_case				:   Dict     =				7
						_snake_case				:   Any     =				[0, 0, 1, 2, 3, 3, 4, 4, 6]
						_snake_case				:   Any     =				[1, 3, 2, 0, 1, 4, 5, 6, 5]
						_snake_case				:   Union[str, Any]     =				[(u, v) for u, v in zip(source, target)]
						_snake_case				:   str     =				create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)

'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


_snake_case				:   List[Any]     =				logging.get_logger(__name__)

_snake_case				:   int     =				{
    """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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Tuple       =					"""convbert"""




		def __init__(				self					,						UpperCamelCase=30_522					,						UpperCamelCase=768					,						UpperCamelCase=12					,						UpperCamelCase=12					,						UpperCamelCase=3_072					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=512					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-12					,						UpperCamelCase=1					,						UpperCamelCase=0					,						UpperCamelCase=2					,						UpperCamelCase=768					,						UpperCamelCase=2					,						UpperCamelCase=9					,						UpperCamelCase=1					,						UpperCamelCase=None					,						**UpperCamelCase					,						):
								super().__init__(
								    pad_token_id=UpperCamelCase					,						bos_token_id=UpperCamelCase					,						eos_token_id=UpperCamelCase					,						**UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= embedding_size
								_SCREAMING_SNAKE_CASE				= head_ratio
								_SCREAMING_SNAKE_CASE				= conv_kernel_size
								_SCREAMING_SNAKE_CASE				= num_groups
								_SCREAMING_SNAKE_CASE				= classifier_dropout


class 			lowerCAmelCase (				__UpperCAmelCase			):




		@property
		def     lowercase       (				self      ):
								if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "choice", 2: "sequence"}
								else:
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]      )