





'''simple docstring'''


import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import torch






class 	lowerCAmelCase__			(			TensorFormatter[Mapping, "torch.Tensor", Mapping]	):




			def __init__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     Dict=None	,  **lowerCamelCase__  :     int )   ->List[Any]:



					'''simple docstring'''







					super().__init__(features=lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      torch_tensor_kwargs
					import torch  # noqa import torch at initialization




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     List[str] )   ->Dict:



					'''simple docstring'''







					import torch

					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) and column:
							if all(
							    isinstance(lowerCamelCase__	,  torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
							    for x in column ):
									return torch.stack(lowerCamelCase__ )
					return column




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Dict )   ->Dict:



					'''simple docstring'''







					import torch

					if isinstance(lowerCamelCase__	,  (str, bytes, type(lowerCamelCase__ )) ):
							return value
					elif isinstance(lowerCamelCase__	,  (np.character, np.ndarray) ) and np.issubdtype(value.dtype	,  np.character ):
							return value.tolist()

					_UpperCAmelCase		:       Any								=      {}

					if isinstance(lowerCamelCase__	,  (np.number, np.ndarray) ) and np.issubdtype(value.dtype	,  np.integer ):
							_UpperCAmelCase		:       Dict								=      {"dtype": torch.intaa}
					elif isinstance(lowerCamelCase__	,  (np.number, np.ndarray) ) and np.issubdtype(value.dtype	,  np.floating ):
							_UpperCAmelCase		:       str								=      {"dtype": torch.floataa}
					elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(lowerCamelCase__	,  PIL.Image.Image ):
									_UpperCAmelCase		:       str								=      np.asarray(lowerCamelCase__ )
					return torch.tensor(lowerCamelCase__	,  **{**default_dtype, **self.torch_tensor_kwargs} )




			def 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     int )   ->Union[str, Any]:



					'''simple docstring'''







					import torch

					# support for torch, tf, jax etc.
					if hasattr(lowerCamelCase__	,  "__array__" ) and not isinstance(lowerCamelCase__	,  torch.Tensor ):
							_UpperCAmelCase		:       List[Any]								=      data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(lowerCamelCase__	,  np.ndarray ):
							if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
									return self._consolidate([self.recursive_tensorize(lowerCamelCase__ ) for substruct in data_struct] )
					elif isinstance(lowerCamelCase__	,  (list, tuple) ):
							return self._consolidate([self.recursive_tensorize(lowerCamelCase__ ) for substruct in data_struct] )
					return self._tensorize(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     dict )   ->Tuple:



					'''simple docstring'''







					return map_nested(self._recursive_tensorize	,  lowerCamelCase__	,  map_list=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     pa.Table )   ->Mapping:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      self.numpy_arrow_extractor().extract_row(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      self.python_features_decoder.decode_row(lowerCamelCase__ )
					return self.recursive_tensorize(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     pa.Table )   ->"torch.Tensor":



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      self.numpy_arrow_extractor().extract_column(lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      self.python_features_decoder.decode_column(lowerCamelCase__	,  pa_table.column_names[0] )
					_UpperCAmelCase		:       Tuple								=      self.recursive_tensorize(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      self._consolidate(lowerCamelCase__ )
					return column






			def 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     pa.Table )   ->Mapping:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      self.numpy_arrow_extractor().extract_batch(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      self.python_features_decoder.decode_batch(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      self.recursive_tensorize(lowerCamelCase__ )
					for column_name in batch:
							_UpperCAmelCase		:       List[Any]								=      self._consolidate(batch[column_name] )
					return batch







'''simple docstring'''


from __future__ import annotations

lowerCamelCase__    	=     {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     dict[str, list[str]]	,  lowerCamelCase__  :     str )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      graph
					# mapping node to its parent in resulting breadth first tree
					_UpperCAmelCase		:       dict[str, str | None]								=      {}
					_UpperCAmelCase		:       List[Any]								=      source_vertex




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      {self.source_vertex}
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       List[str]								=      [self.source_vertex]  # first in first out queue

					while queue:
							_UpperCAmelCase		:       int								=      queue.pop(0 )
							for adjacent_vertex in self.graph[vertex]:
									if adjacent_vertex not in visited:
											visited.add(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      vertex
											queue.append(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







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

					_UpperCAmelCase		:       int								=      self.parent.get(lowerCamelCase__ )
					if target_vertex_parent is None:
							_UpperCAmelCase		:       Tuple								=      (
							    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
							)
							raise ValueError(lowerCamelCase__ )

					return self.shortest_path(lowerCamelCase__ ) + F"""->{target_vertex}"""


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



