snake_case__				= {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    list[str]:



      '''simple docstring'''

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

            # mark node as explored
                  explored.add(__UpperCAmelCase		)

    # in case there's no path between the 2 nodes
      return []
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      if not graph or start not in graph or target not in graph:
            return -1
      if start == target:
            return 0
      _lowerCamelCase			    =				[start]
      _lowerCamelCase			    =				set(__UpperCAmelCase		)
      # Keep tab on distances from `start` node.
      _lowerCamelCase			    =				{start: 0, target: -1}
      while queue:
            _lowerCamelCase			    =				queue.pop(0		)
            if node == target:
                  _lowerCamelCase			    =				(
                      dist[node] if dist[target] == -1 else min(dist[target] ,						dist[node]		)
                  )
            for adjacent in graph[node]:
                  if adjacent not in visited:
                        visited.add(__UpperCAmelCase		)
                        queue.append(__UpperCAmelCase		)
                        _lowerCamelCase			    =				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
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip