




_A   =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}



def 						lowercase_      (       A__			,		A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case								= set()
    # keep track of all the paths to be checked
    snake_case								= [[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
        snake_case								= queue.pop(0		)
        # get the last node from the path
        snake_case								= path[-1]
        if node not in explored:
            snake_case								= graph[node]
            # go through all neighbour nodes, construct a new path and
            # push it into the queue
            for neighbour in neighbours:
                snake_case								= 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 						lowercase_      (       A__			,		A__			,		A__		)							->							Any:
    """simple docstring"""

    if not graph or start not in graph or target not in graph:
        return -1
    if start == target:
        return 0
    snake_case								= [start]
    snake_case								= set(A__		)
    # Keep tab on distances from `start` node.
    snake_case								= {start: 0, target: -1}
    while queue:
        snake_case								= queue.pop(0		)
        if node == target:
            snake_case								= (
                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__		)
                snake_case								= 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



import warnings
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


_A   =			logging.get_logger(__name__)

_A   =			{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"segformer"





     def __init__(self				:   List[Any]    ,   _A				:   int=3    ,   _A				:   List[Any]=4    ,   _A				:   Any=[2, 2, 2, 2]    ,   _A				:   Dict=[8, 4, 2, 1]    ,   _A				:   List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,   _A				:   Tuple=[7, 3, 3, 3]    ,   _A				:   Optional[int]=[4, 2, 2, 2]    ,   _A				:   Dict=[1, 2, 5, 8]    ,   _A				:   int=[4, 4, 4, 4]    ,   _A				:   Dict="gelu"    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.1    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Dict=0.1    ,   _A				:   List[Any]=1E-6    ,   _A				:   List[str]=2_5_6    ,   _A				:   Optional[Any]=2_5_5    ,   **_A				:   str    ,   )						->						Tuple:
         super().__init__(**_A       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
             warnings.warn(
                 "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                 " removed, as the behaviour will default to that of reshape_last_stage = True."    ,   _A    ,   )

         snake_case								= num_channels
         snake_case								= num_encoder_blocks
         snake_case								= depths
         snake_case								= sr_ratios
         snake_case								= hidden_sizes
         snake_case								= patch_sizes
         snake_case								= strides
         snake_case								= mlp_ratios
         snake_case								= num_attention_heads
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= classifier_dropout_prob
         snake_case								= initializer_range
         snake_case								= drop_path_rate
         snake_case								= layer_norm_eps
         snake_case								= decoder_hidden_size
         snake_case								= kwargs.get("reshape_last_stage"    ,   _A       )
         snake_case								= semantic_loss_ignore_index







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		version.parse("1.11"  )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						float:
         return 1E-4





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         return 1_2

