



import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase__       =	logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__						)
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
			            '''to the `max_length` value of the model configuration.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
			            '''to the `num_beams` value of the model configuration.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
			    }    ,						)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       super().to_dict()
						for k, v in d.items():
									if isinstance(A			,		A):
												_UpperCAmelCase			=       v.to_dict()
						return d





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





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_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(_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 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# 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(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_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
