'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_						 =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						 =      {
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class  a			(							snake_case_					):



  """simple docstring"""
  __lowerCAmelCase					   =       """yolos"""







  def __init__(				self ,     snake_case_=768 ,     snake_case_=12 ,     snake_case_=12 ,     snake_case_=3072 ,     snake_case_="gelu" ,     snake_case_=0.0 ,     snake_case_=0.0 ,     snake_case_=0.0_2 ,     snake_case_=1e-1_2 ,     snake_case_=[512, 864] ,     snake_case_=16 ,     snake_case_=3 ,     snake_case_=True ,     snake_case_=100 ,     snake_case_=True ,     snake_case_=False ,     snake_case_=1 ,     snake_case_=5 ,     snake_case_=2 ,     snake_case_=5 ,     snake_case_=2 ,     snake_case_=0.1 ,     **snake_case_ ,     ):

    '''simple docstring'''







    super().__init__(**A_       )

    __UpperCAmelCase:       List[str]		=			hidden_size
    __UpperCAmelCase:       Tuple		=			num_hidden_layers
    __UpperCAmelCase:       List[Any]		=			num_attention_heads
    __UpperCAmelCase:       Optional[int]		=			intermediate_size
    __UpperCAmelCase:       List[Any]		=			hidden_act
    __UpperCAmelCase:       str		=			hidden_dropout_prob
    __UpperCAmelCase:       List[Any]		=			attention_probs_dropout_prob
    __UpperCAmelCase:       int		=			initializer_range
    __UpperCAmelCase:       Optional[Any]		=			layer_norm_eps
    __UpperCAmelCase:       int		=			image_size
    __UpperCAmelCase:       str		=			patch_size
    __UpperCAmelCase:       str		=			num_channels
    __UpperCAmelCase:       List[str]		=			qkv_bias
    __UpperCAmelCase:       Dict		=			num_detection_tokens
    __UpperCAmelCase:       List[str]		=			use_mid_position_embeddings
    __UpperCAmelCase:       Optional[Any]		=			auxiliary_loss
    # Hungarian matcher
    __UpperCAmelCase:       str		=			class_cost
    __UpperCAmelCase:       List[str]		=			bbox_cost
    __UpperCAmelCase:       Tuple		=			giou_cost
    # Loss coefficients
    __UpperCAmelCase:       Any		=			bbox_loss_coefficient
    __UpperCAmelCase:       List[Any]		=			giou_loss_coefficient
    __UpperCAmelCase:       Any		=			eos_coefficient




class  a			(							snake_case_					):



  """simple docstring"""
  __lowerCAmelCase					   =       version.parse("""1.11"""					)







  @property
  def  lowercase_	(				self       ):

    '''simple docstring'''







    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]       )







  @property
  def  lowercase_	(				self       ):

    '''simple docstring'''







    return 1e-4







  @property
  def  lowercase_	(				self       ):

    '''simple docstring'''







    return 12

'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[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
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

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







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




