'''simple docstring'''






class lowercase_       :  # Public class to implement a graph







   """simple docstring"""

   def __init__(      self      :			Any, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			list[list[bool]]     )  ->			None:
        _A    		=  row
        _A    		=  col
        _A    		=  graph
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			list[list[bool]]     )  ->			bool:
        return (
            0 <= i < self.ROW
            and 0 <= j < self.COL
            and not visited[i][j]
            and self.graph[i][j]
        )
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			list[list[bool]]     )  ->			None:
        # Checking all 8 elements surrounding nth element
        _A    		=  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
        _A    		=  [-1, 0, 1, -1, 1, -1, 0, 1]
        _A    		=  True  # Make those cells visited
        for k in range(8     ):
             if self.is_safe(i + row_nbr[k], j + col_nbr[k], UpperCamelCase__     ):
                  self.diffs(i + row_nbr[k], j + col_nbr[k], UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			int:  # And finally, count all islands.
        _A    		=  [[False for j in range(self.COL     )] for i in range(self.ROW     )]
        _A    		=  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(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )
                       count += 1
        return count




'''simple docstring'''






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 numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







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







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



