
from collections import OrderedDict
from typing import Mapping

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


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """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     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''convbert'''



     def __init__(				self							:							List[Any]     ,						_A							:							Union[str, Any]=3_0522     ,						_A							:							Dict=768     ,						_A							:							int=12     ,						_A							:							Union[str, Any]=12     ,						_A							:							int=3072     ,						_A							:							Optional[int]="gelu"     ,						_A							:							Optional[Any]=0.1     ,						_A							:							Optional[Any]=0.1     ,						_A							:							Union[str, Any]=512     ,						_A							:							List[str]=2     ,						_A							:							List[Any]=0.02     ,						_A							:							Union[str, Any]=1e-12     ,						_A							:							Union[str, Any]=1     ,						_A							:							List[str]=0     ,						_A							:							Optional[Any]=2     ,						_A							:							int=768     ,						_A							:							List[str]=2     ,						_A							:							str=9     ,						_A							:							List[Any]=1     ,						_A							:							Optional[Any]=None     ,						**_A							:							List[str]     ,						):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Dict					=		vocab_size
       __SCREAMING_SNAKE_CASE      :	int					=		hidden_size
       __SCREAMING_SNAKE_CASE      :	List[str]					=		num_hidden_layers
       __SCREAMING_SNAKE_CASE      :	Any					=		num_attention_heads
       __SCREAMING_SNAKE_CASE      :	Tuple					=		intermediate_size
       __SCREAMING_SNAKE_CASE      :	Tuple					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		hidden_dropout_prob
       __SCREAMING_SNAKE_CASE      :	str					=		attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		max_position_embeddings
       __SCREAMING_SNAKE_CASE      :	Any					=		type_vocab_size
       __SCREAMING_SNAKE_CASE      :	Tuple					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		layer_norm_eps
       __SCREAMING_SNAKE_CASE      :	Dict					=		embedding_size
       __SCREAMING_SNAKE_CASE      :	str					=		head_ratio
       __SCREAMING_SNAKE_CASE      :	Tuple					=		conv_kernel_size
       __SCREAMING_SNAKE_CASE      :	int					=		num_groups
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		classifier_dropout






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




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





SCREAMING_SNAKE_CASE__   			=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   list[str]:
			A__	  =     set()
			# keep track of all the paths to be checked
			A__	  =     [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						A__	  =     queue.pop(0       )
						# get the last node from the path
						A__	  =     path[-1]
						if node not in explored:
									A__	  =     graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												A__	  =     list(__UpperCamelCase       )
												new_path.append(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(__UpperCamelCase       )

    # in case there's no path between the 2 nodes
			return []


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			A__	  =     [start]
			A__	  =     set(__UpperCamelCase       )
			# Keep tab on distances from `start` node.
			A__	  =     {start: 0, target: -1}
			while queue:
						A__	  =     queue.pop(0       )
						if node == target:
									A__	  =     (
									    dist[node] if dist[target] == -1 else min(dist[target]			,						dist[node]       )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												A__	  =     dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




