


from collections import OrderedDict
from typing import Mapping

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


_A		:				Dict									=       logging.get_logger(__name__)

_A		:				Union[str, Any]									=       {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[str]						      =	"roberta"





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=50265    ,	SCREAMING_SNAKE_CASE_					:				Dict=768    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=12    ,	SCREAMING_SNAKE_CASE_					:				Any=12    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=3072    ,	SCREAMING_SNAKE_CASE_					:				Any="gelu"    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				Any=0.1    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=512    ,	SCREAMING_SNAKE_CASE_					:				Dict=2    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0.02    ,	SCREAMING_SNAKE_CASE_					:				List[str]=1E-1_2    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				Any=0    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=2    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]="absolute"    ,	SCREAMING_SNAKE_CASE_					:				Tuple=True    ,	SCREAMING_SNAKE_CASE_					:				Tuple=None    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,	bos_token_id=SCREAMING_SNAKE_CASE_    ,	eos_token_id=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

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



class        a					(			_UpperCAmelCase   ):





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






from __future__ import annotations

_A		:				List[str]									=       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				dict[str, list[str]]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Dict     =	graph
        # mapping node to its parent in resulting breadth first tree
        __lowerCamelCase:				dict[str, str | None]     =	{}
        __lowerCamelCase:				str     =	source_vertex





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	{self.source_vertex}
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[int]     =	[self.source_vertex]  # first in first out queue

        while queue:
           __lowerCamelCase:				int     =	queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	vertex
                 queue.append(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        if target_vertex == self.source_vertex:
           return self.source_vertex

        __lowerCamelCase:				List[str]     =	self.parent.get(SCREAMING_SNAKE_CASE_							)
        if target_vertex_parent is None:
           __lowerCamelCase:				Optional[int]     =	(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(SCREAMING_SNAKE_CASE_							)

        return self.shortest_path(SCREAMING_SNAKE_CASE_							) + F'->{target_vertex}'


if __name__ == "__main__":
   _A		:				int									=       Graph(graph, '''G''')
   g.breath_first_search()
   print(g.shortest_path('''D'''))
   print(g.shortest_path('''G'''))
   print(g.shortest_path('''Foo'''))


