from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import tensorflow as tf

   from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM







@require_tf
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""tf"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""tf"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     labels=_lowercase				).loss
     SCREAMING_SNAKE_CASE__      							=				-tf.math.reduce_mean(_lowercase				).numpy()

     SCREAMING_SNAKE_CASE__      							=				-21.22_81_68
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 2E-4				)

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[0] * len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[1] * len(__UpperCamelCase     )

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

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

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

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

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

  print(max(__UpperCamelCase     )     )


# Adjacency list of Graph
__lowerCamelCase      :			int							       =		{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)
