
from __future__ import annotations

_lowerCamelCase	:    Optional[int]          =      {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   Union[str, Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      graph
				# mapping node to its parent in resulting breadth first tree
				_snake_case          =      {}
				_snake_case          =      source_vertex



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      {self.source_vertex}
				_snake_case          =      None
				_snake_case          =      [self.source_vertex]  # first in first out queue

				while queue:
							_snake_case          =      queue.pop(0     )
							for adjacent_vertex in self.graph[vertex]:
										if adjacent_vertex not in visited:
													visited.add(_a     )
													_snake_case          =      vertex
													queue.append(_a     )





	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				if target_vertex == self.source_vertex:
							return self.source_vertex

				_snake_case          =      self.parent.get(_a     )
				if target_vertex_parent is None:
							_snake_case          =      (
							    f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
							)
							raise ValueError(_a     )

				return self.shortest_path(_a     ) + f'''->{target_vertex}'''


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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, Any]          =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "canine"



	def __init__(  self     :   int      ,  lowercase     :   Optional[int]=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Optional[int]=3_072      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   int=16_384      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-12      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   Dict=0xE000      ,  lowercase     :   Optional[Any]=0xE001      ,  lowercase     :   Union[str, Any]=4      ,  lowercase     :   str=4      ,  lowercase     :   Optional[int]=8      ,  lowercase     :   List[str]=16_384      ,  lowercase     :   Union[str, Any]=128      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


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

				_snake_case          =      max_position_embeddings
				_snake_case          =      hidden_size
				_snake_case          =      num_hidden_layers
				_snake_case          =      num_attention_heads
				_snake_case          =      intermediate_size
				_snake_case          =      hidden_act
				_snake_case          =      hidden_dropout_prob
				_snake_case          =      attention_probs_dropout_prob
				_snake_case          =      initializer_range
				_snake_case          =      type_vocab_size
				_snake_case          =      layer_norm_eps

				# Character config:
				_snake_case          =      downsampling_rate
				_snake_case          =      upsampling_kernel_size
				_snake_case          =      num_hash_functions
				_snake_case          =      num_hash_buckets
				_snake_case          =      local_transformer_stride