def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			bool:

 # 1. Validate that path exists between current and next vertices
 if graph[path[curr_ind - 1]][next_ver] == 0:
  return False

 # 2. Validate that next vertex is not already in path
 return not any(vertex == next_ver for vertex in path					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			bool:

 # Base Case
 if curr_ind == len(_lowerCAmelCase					):
  # return whether path exists between current and starting vertices
  return graph[path[curr_ind - 1]][path[0]] == 1

 # Recursive Step
 for next_ver in range(0							,       len(_lowerCAmelCase					)					):
  if valid_connection(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					):
   # Insert current vertex  into path as next transition
   UpperCamelCase     : Optional[Any]	     =       next_ver
   # Validate created path
   if util_hamilton_cycle(_lowerCAmelCase							,       _lowerCAmelCase							,       curr_ind + 1					):
    return True
   # Backtrack
   UpperCamelCase     : List[Any]	     =       -1
 return False



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase = 0					)						->			list[int]:
 UpperCamelCase     : List[str]	     =       [-1] * (len(_lowerCAmelCase					) + 1)
 # initialize start and end of path with starting index
 UpperCamelCase     : Dict	     =       start_index
 # evaluate and if we find answer return path either return empty array
 return path if util_hamilton_cycle(_lowerCAmelCase							,       _lowerCAmelCase							,       1					) else []







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  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
