text: import unittest
  
  from numpy import ndarray
  
  from transformers import TensorType, is_flax_available, is_torch_available
  from transformers.testing_utils import require_flax, require_torch
  from transformers.tokenization_bert_fast import BertTokenizerFast
  
  
  if is_flax_available():
      from transformers.modeling_flax_bert import FlaxBertModel
  
  if is_torch_available():
      import torch
  
      from transformers.modeling_bert import BertModel
  
  
  @require_flax
  @require_torch
  class FlaxBertModelTest(unittest.TestCase):
      def test_from_pytorch(self):
          with torch.no_grad():
              with self.subTest("bert-base-cased"):
                  tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
                  fx_model = FlaxBertModel.from_pretrained("bert-base-cased")
                  pt_model = BertModel.from_pretrained("bert-base-cased")
  
                  # Check for simple input
                  pt_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.PYTORCH)
                  fx_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.JAX)
                  pt_outputs = pt_model(**pt_inputs)
                  fx_outputs = fx_model(**fx_inputs)
  
                  self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
  
                  for fx_output, pt_output in zip(fx_outputs, pt_outputs):
                      self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-4)
  
      def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):
          diff = (a - b).sum()
          self.assertLessEqual(diff, tol, "Difference between torch and flax is {} (>= {})".format(diff, tol))
  