



"""simple docstring"""






class     UpperCAmelCase    :  # Public class to implement a graph



   def __init__(       self    :	int							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	list[list[bool]]				):



     """simple docstring"""
     _snake_case   	=      row
     _snake_case   	=      col
     _snake_case   	=      graph



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	list[list[bool]]				):



     """simple docstring"""
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	list[list[bool]]				):



     """simple docstring"""
     # Checking all 8 elements surrounding nth element
     _snake_case   	=      [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     _snake_case   	=      [-1, 0, 1, -1, 1, -1, 0, 1]
     _snake_case   	=      True  # Make those cells visited
     for k in range(8				):
       if self.is_safe(i + row_nbr[k]							,       j + col_nbr[k]							,       __lowerCamelCase				):
         self.diffs(i + row_nbr[k]							,       j + col_nbr[k]							,       __lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	str				):  # And finally, count all islands.



     """simple docstring"""
     _snake_case   	=      [[False for j in range(self.COL				)] for i in range(self.ROW				)]
     _snake_case   	=      0
     for i in range(self.ROW				):
       for j in range(self.COL				):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
           count += 1
     return count








"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

