code: # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import random
  import tempfile
  
  import numpy as np
  
  import transformers
  from transformers import is_flax_available, is_torch_available
  from transformers.testing_utils import require_flax, require_torch
  
  
  if is_flax_available():
      import os
  
      import jax
      import jax.numpy as jnp
      from transformers.modeling_flax_utils import convert_state_dict_from_pt
  
      os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12"  # assumed parallelism: 8
  
  if is_torch_available():
      import torch
  
  
  def ids_tensor(shape, vocab_size, rng=None):
      """Creates a random int32 tensor of the shape within the vocab size."""
      if rng is None:
          rng = random.Random()
  
      total_dims = 1
      for dim in shape:
          total_dims *= dim
  
      values = []
      for _ in range(total_dims):
          values.append(rng.randint(0, vocab_size - 1))
  
      output = np.array(values, dtype=jnp.int32).reshape(shape)
  
      return output
  
  
  def random_attention_mask(shape, rng=None):
      attn_mask = ids_tensor(shape, vocab_size=2, rng=rng)
      # make sure that at least one token is attended to for each batch
      attn_mask[:, -1] = 1
      return attn_mask
  
  
  @require_flax
  class FlaxModelTesterMixin:
      model_tester = None
      all_model_classes = ()
  
      def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
          diff = np.abs((a - b)).max()
          self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
  
      @require_torch
      def test_equivalence_flax_pytorch(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  pt_model_class_name = model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  pt_model_class = getattr(transformers, pt_model_class_name)
                  pt_model = pt_model_class(config).eval()
  
                  fx_state = convert_state_dict_from_pt(model_class, pt_model.state_dict(), config)
                  fx_model = model_class(config, dtype=jnp.float32)
                  fx_model.params = fx_state
  
                  pt_inputs = {k: torch.tensor(v.tolist()) for k, v in inputs_dict.items()}
  
                  with torch.no_grad():
                      pt_outputs = pt_model(**pt_inputs).to_tuple()
  
                  fx_outputs = fx_model(**inputs_dict)
                  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(), 1e-3)
  
                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(tmpdirname)
                      fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
  
                  fx_outputs_loaded = fx_model_loaded(**inputs_dict)
                  self.assertEqual(
                      len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
                  )
                  for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
                      self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 5e-3)
  
      def test_from_pretrained_save_pretrained(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  model = model_class(config)
  
                  outputs = model(**inputs_dict)
  
                  with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(tmpdirname)
                      model_loaded = model_class.from_pretrained(tmpdirname)
  
                  outputs_loaded = model_loaded(**inputs_dict)
                  for output_loaded, output in zip(outputs_loaded, outputs):
                      self.assert_almost_equals(output_loaded, output, 5e-3)
  
      def test_jit_compilation(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  model = model_class(config)
  
                  @jax.jit
                  def model_jitted(input_ids, attention_mask=None, token_type_ids=None):
                      return model(input_ids, attention_mask, token_type_ids)
  
                  with self.subTest("JIT Disabled"):
                      with jax.disable_jit():
                          outputs = model_jitted(**inputs_dict)
  
                  with self.subTest("JIT Enabled"):
                      jitted_outputs = model_jitted(**inputs_dict)
  
                  self.assertEqual(len(outputs), len(jitted_outputs))
                  for jitted_output, output in zip(jitted_outputs, outputs):
                      self.assertEqual(jitted_output.shape, output.shape)
  
      def test_naming_convention(self):
          for model_class in self.all_model_classes:
              model_class_name = model_class.__name__
              module_class_name = (
                  model_class_name[:-5] + "Module" if model_class_name[-5:] == "Model" else model_class_name + "Module"
              )
              bert_modeling_flax_module = __import__(model_class.__module__, fromlist=[module_class_name])
              module_cls = getattr(bert_modeling_flax_module, module_class_name)
  
              self.assertIsNotNone(module_cls)
  