
'''simple docstring'''


def 			__snake_case  (     _UpperCAmelCase       :							Dict):
   UpperCamelCase	  =	[0] * len(__snake_case)
   UpperCamelCase	  =	[]
   UpperCamelCase	  =	[1] * len(__snake_case)

   for values in graph.values():
      for i in values:
         indegree[i] += 1

   for i in range(len(__snake_case)):
      if indegree[i] == 0:
         queue.append(__snake_case)

   while queue:
      UpperCamelCase	  =	queue.pop(0)
      for x in graph[vertex]:
         indegree[x] -= 1

         if long_dist[vertex] + 1 > long_dist[x]:
            UpperCamelCase	  =	long_dist[vertex] + 1

         if indegree[x] == 0:
            queue.append(__snake_case)

   print(max(__snake_case))


# Adjacency list of Graph
snake_case_			:	Union[str, Any] 	=       {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)












'''simple docstring'''




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


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d