


from collections import OrderedDict
from typing import Mapping

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


_A		:				List[str]									=       logging.get_logger(__name__)

_A		:				List[Any]									=       {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  int						      =	"distilbert"
     UpperCAmelCase__ :  Union[str, Any]						      =	{
         "hidden_size": "dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
     }





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any=30522    ,	SCREAMING_SNAKE_CASE_					:				Dict=512    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=False    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=6    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=12    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=768    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=4 * 768    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=0.1    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple="gelu"    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=0.02    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0.1    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0.2    ,	SCREAMING_SNAKE_CASE_					:				Dict=0    ,	**SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	):
        __lowerCamelCase:				int     =	vocab_size
        __lowerCamelCase:				Tuple     =	max_position_embeddings
        __lowerCamelCase:				Optional[Any]     =	sinusoidal_pos_embds
        __lowerCamelCase:				List[str]     =	n_layers
        __lowerCamelCase:				int     =	n_heads
        __lowerCamelCase:				Tuple     =	dim
        __lowerCamelCase:				Optional[Any]     =	hidden_dim
        __lowerCamelCase:				List[Any]     =	dropout
        __lowerCamelCase:				Dict     =	attention_dropout
        __lowerCamelCase:				List[Any]     =	activation
        __lowerCamelCase:				List[str]     =	initializer_range
        __lowerCamelCase:				List[Any]     =	qa_dropout
        __lowerCamelCase:				List[Any]     =	seq_classif_dropout
        super().__init__(**SCREAMING_SNAKE_CASE_    ,	pad_token_id=SCREAMING_SNAKE_CASE_							)



class        a					(			_UpperCAmelCase   ):





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        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'''))


