text: # coding=utf-8
  # Copyright 2019 HuggingFace Inc.
  #
  # 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 copy
  import gc
  import inspect
  import os.path
  import random
  import tempfile
  import unittest
  from typing import List, Tuple
  
  from transformers import is_torch_available
  from transformers.file_utils import WEIGHTS_NAME
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
  
  
  if is_torch_available():
      import numpy as np
      import torch
  
      from transformers import (
          BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MODEL_FOR_CAUSAL_LM_MAPPING,
          MODEL_FOR_MASKED_LM_MAPPING,
          MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          AdaptiveEmbedding,
          BertConfig,
          BertModel,
          PretrainedConfig,
          PreTrainedModel,
          T5ForConditionalGeneration,
      )
  
  
  def _config_zero_init(config):
      configs_no_init = copy.deepcopy(config)
      for key in configs_no_init.__dict__.keys():
          if "_range" in key or "_std" in key or "initializer_factor" in key:
              setattr(configs_no_init, key, 1e-10)
      return configs_no_init
  
  
  TINY_T5 = "patrickvonplaten/t5-tiny-random"
  
  
  @require_torch
  class ModelTesterMixin:
  
      model_tester = None
      all_model_classes = ()
      all_generative_model_classes = ()
      test_torchscript = True
      test_pruning = True
      test_resize_embeddings = True
      test_head_masking = True
      test_missing_keys = True
      test_model_parallel = False
      is_encoder_decoder = False
  
      def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
          inputs_dict = copy.deepcopy(inputs_dict)
          if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
              inputs_dict = {
                  k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
                  if isinstance(v, torch.Tensor) and v.ndim > 1
                  else v
                  for k, v in inputs_dict.items()
              }
  
          if return_labels:
              if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
                  inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
              elif model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
                  inputs_dict["start_positions"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
                  inputs_dict["end_positions"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
              elif model_class in [
                  *MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values(),
                  *MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.values(),
              ]:
                  inputs_dict["labels"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
              elif model_class in [
                  *MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
                  *MODEL_FOR_CAUSAL_LM_MAPPING.values(),
                  *MODEL_FOR_MASKED_LM_MAPPING.values(),
                  *MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
              ]:
                  inputs_dict["labels"] = torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                  )
          return inputs_dict
  
      def test_save_load(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
              out_2 = outputs[0].cpu().numpy()
              out_2[np.isnan(out_2)] = 0
  
              with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(tmpdirname)
                  model = model_class.from_pretrained(tmpdirname)
                  model.to(torch_device)
                  with torch.no_grad():
                      after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
                  # Make sure we don't have nans
                  out_1 = after_outputs[0].cpu().numpy()
                  out_1[np.isnan(out_1)] = 0
                  max_diff = np.amax(np.abs(out_1 - out_2))
                  self.assertLessEqual(max_diff, 1e-5)
  
      def test_save_load__keys_to_ignore_on_save(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              _keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None)
              if _keys_to_ignore_on_save is None:
                  continue
  
              # check the keys are in the original state_dict
              for k in _keys_to_ignore_on_save:
                  self.assertIn(k, model.state_dict())
  
              # check that certain keys didn't get saved with the model
              with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(tmpdirname)
                  output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
                  state_dict_saved = torch.load(output_model_file)
                  for k in _keys_to_ignore_on_save:
                      self.assertNotIn(k, state_dict_saved)
  
      def test_initialization(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          configs_no_init = _config_zero_init(config)
          for model_class in self.all_model_classes:
              model = model_class(config=configs_no_init)
              for name, param in model.named_parameters():
                  if param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1e9).round() / 1e9).item(),
                          [0.0, 1.0],
                          msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                      )
  
      def test_determinism(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
                  second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
  
              out_1 = first.cpu().numpy()
              out_2 = second.cpu().numpy()
              out_1 = out_1[~np.isnan(out_1)]
              out_2 = out_2[~np.isnan(out_2)]
              max_diff = np.amax(np.abs(out_1 - out_2))
              self.assertLessEqual(max_diff, 1e-5)
  
      def test_forward_signature(self):
          config, _ = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              signature = inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              arg_names = [*signature.parameters.keys()]
  
              if model.config.is_encoder_decoder:
                  expected_arg_names = [
                      "input_ids",
                      "attention_mask",
                      "decoder_input_ids",
                      "decoder_attention_mask",
                  ]
                  expected_arg_names.extend(
                      ["head_mask", "decoder_head_mask", "encoder_outputs"]
                      if "head_mask" and "decoder_head_mask" in arg_names
                      else ["encoder_outputs"]
                  )
                  self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
              else:
                  expected_arg_names = ["input_ids"]
                  self.assertListEqual(arg_names[:1], expected_arg_names)
  
      def test_training(self):
          if not self.model_tester.is_training:
              return
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.return_dict = True
  
          for model_class in self.all_model_classes:
              if model_class in MODEL_MAPPING.values():
                  continue
              model = model_class(config)
              model.to(torch_device)
              model.train()
              inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              loss = model(**inputs).loss
              loss.backward()
  
      def test_training_gradient_checkpointing(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training or not hasattr(config, "gradient_checkpointing"):
              return
  
          config.gradient_checkpointing = True
          config.use_cache = False
          config.return_dict = True
  
          for model_class in self.all_model_classes:
              if model_class in MODEL_MAPPING.values():
                  continue
              model = model_class(config)
              model.to(torch_device)
              model.train()
              inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              loss = model(**inputs).loss
              loss.backward()
  
      def test_attention_outputs(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.return_dict = True
  
          seq_len = getattr(self.model_tester, "seq_length", None)
          decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
          encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
          decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
          encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
          chunk_length = getattr(self.model_tester, "chunk_length", None)
          if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
              encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
  
          for model_class in self.all_model_classes:
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = False
              config.return_dict = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              config.output_attentions = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              if chunk_length is not None:
                  self.assertListEqual(
                      list(attentions[0].shape[-4:]),
                      [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
                  )
              else:
                  self.assertListEqual(
                      list(attentions[0].shape[-3:]),
                      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                  )
              out_len = len(outputs)
  
              if self.is_encoder_decoder:
                  correct_outlen = 5
  
                  # loss is at first position
                  if "labels" in inputs_dict:
                      correct_outlen += 1  # loss is added to beginning
                  # Question Answering model returns start_logits and end_logits
                  if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
                      correct_outlen += 1  # start_logits and end_logits instead of only 1 output
                  if "past_key_values" in outputs:
                      correct_outlen += 1  # past_key_values have been returned
  
                  self.assertEqual(out_len, correct_outlen)
  
                  # decoder attentions
                  decoder_attentions = outputs.decoder_attentions
                  self.assertIsInstance(decoder_attentions, (list, tuple))
                  self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
                  self.assertListEqual(
                      list(decoder_attentions[0].shape[-3:]),
                      [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
                  )
  
                  # cross attentions
                  cross_attentions = outputs.cross_attentions
                  self.assertIsInstance(cross_attentions, (list, tuple))
                  self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
                  self.assertListEqual(
                      list(cross_attentions[0].shape[-3:]),
                      [
                          self.model_tester.num_attention_heads,
                          decoder_seq_length,
                          encoder_key_length,
                      ],
                  )
  
              # Check attention is always last and order is fine
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
              if hasattr(self.model_tester, "num_hidden_states_types"):
                  added_hidden_states = self.model_tester.num_hidden_states_types
              elif self.is_encoder_decoder:
                  added_hidden_states = 2
              else:
                  added_hidden_states = 1
              self.assertEqual(out_len + added_hidden_states, len(outputs))
  
              self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  
              self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
              if chunk_length is not None:
                  self.assertListEqual(
                      list(self_attentions[0].shape[-4:]),
                      [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
                  )
              else:
                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]),
                      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                  )
  
      def test_torchscript(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          self._create_and_check_torchscript(config, inputs_dict)
  
      def test_torchscript_output_attentions(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.output_attentions = True
          self._create_and_check_torchscript(config, inputs_dict)
  
      def test_torchscript_output_hidden_state(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.output_hidden_states = True
          self._create_and_check_torchscript(config, inputs_dict)
  
      def _create_and_check_torchscript(self, config, inputs_dict):
          if not self.test_torchscript:
              return
  
          configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
          configs_no_init.torchscript = True
          for model_class in self.all_model_classes:
              model = model_class(config=configs_no_init)
              model.to(torch_device)
              model.eval()
              inputs = self._prepare_for_class(inputs_dict, model_class)
  
              try:
                  if model.config.is_encoder_decoder:
                      model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
                      input_ids = inputs["input_ids"]
                      attention_mask = inputs["attention_mask"]
                      decoder_input_ids = inputs["decoder_input_ids"]
                      decoder_attention_mask = inputs["decoder_attention_mask"]
                      traced_model = torch.jit.trace(
                          model, (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
                      )
                  else:
                      input_ids = inputs["input_ids"]
                      traced_model = torch.jit.trace(model, input_ids)
              except RuntimeError:
                  self.fail("Couldn't trace module.")
  
              with tempfile.TemporaryDirectory() as tmp_dir_name:
                  pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
  
                  try:
                      torch.jit.save(traced_model, pt_file_name)
                  except Exception:
                      self.fail("Couldn't save module.")
  
                  try:
                      loaded_model = torch.jit.load(pt_file_name)
                  except Exception:
                      self.fail("Couldn't load module.")
  
              model.to(torch_device)
              model.eval()
  
              loaded_model.to(torch_device)
              loaded_model.eval()
  
              model_state_dict = model.state_dict()
              loaded_model_state_dict = loaded_model.state_dict()
  
              self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
  
              models_equal = True
              for layer_name, p1 in model_state_dict.items():
                  p2 = loaded_model_state_dict[layer_name]
                  if p1.data.ne(p2.data).sum() > 0:
                      models_equal = False
  
              self.assertTrue(models_equal)
  
      def test_headmasking(self):
          if not self.test_head_masking:
              return
  
          global_rng.seed(42)
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          global_rng.seed()
  
          inputs_dict["output_attentions"] = True
          config.output_hidden_states = True
          configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
          for model_class in self.all_model_classes:
              model = model_class(config=configs_no_init)
              model.to(torch_device)
              model.eval()
  
              # Prepare head_mask
              # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
              head_mask = torch.ones(
                  self.model_tester.num_hidden_layers,
                  self.model_tester.num_attention_heads,
                  device=torch_device,
              )
              head_mask[0, 0] = 0
              head_mask[-1, :-1] = 0
              head_mask.requires_grad_(requires_grad=True)
              inputs = self._prepare_for_class(inputs_dict, model_class).copy()
              inputs["head_mask"] = head_mask
              if model.config.is_encoder_decoder:
                  signature = inspect.signature(model.forward)
                  arg_names = [*signature.parameters.keys()]
                  if "decoder_head_mask" in arg_names:  # necessary diferentiation because of T5 model
                      inputs["decoder_head_mask"] = head_mask
              outputs = model(**inputs, return_dict=True)
  
              # Test that we can get a gradient back for importance score computation
              output = sum(t.sum() for t in outputs[0])
              output = output.sum()
              output.backward()
              multihead_outputs = head_mask.grad
  
              self.assertIsNotNone(multihead_outputs)
              self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
  
              def check_attentions_validity(attentions):
                  # Remove Nan
                  for t in attentions:
                      self.assertLess(
                          torch.sum(torch.isnan(t)), t.numel() / 4
                      )  # Check we don't have more than 25% nans (arbitrary)
                  attentions = [
                      t.masked_fill(torch.isnan(t), 0.0) for t in attentions
                  ]  # remove them (the test is less complete)
  
                  self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
                  self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
                  if len(attentions) > 2:  # encoder-decoder models have only 2 layers in each module
                      self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
                  self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
                  self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
  
              if model.config.is_encoder_decoder:
                  check_attentions_validity(outputs.encoder_attentions)
                  check_attentions_validity(outputs.decoder_attentions)
              else:
                  check_attentions_validity(outputs.attentions)
  
      def test_head_pruning(self):
          if not self.test_pruning:
              return
  
          for model_class in self.all_model_classes:
              (
                  config,
                  inputs_dict,
              ) = self.model_tester.prepare_config_and_inputs_for_common()
  
              if "head_mask" in inputs_dict:
                  del inputs_dict["head_mask"]
  
              inputs_dict["output_attentions"] = True
              config.output_hidden_states = False
              model = model_class(config=config)
              model.to(torch_device)
              model.eval()
              heads_to_prune = {
                  0: list(range(1, self.model_tester.num_attention_heads)),
                  -1: [0],
              }
              model.prune_heads(heads_to_prune)
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
              attentions = outputs[-1]
  
              self.assertEqual(attentions[0].shape[-3], 1)
              self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
              self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
  
      def test_head_pruning_save_load_from_pretrained(self):
          if not self.test_pruning:
              return
  
          for model_class in self.all_model_classes:
              (
                  config,
                  inputs_dict,
              ) = self.model_tester.prepare_config_and_inputs_for_common()
  
              if "head_mask" in inputs_dict:
                  del inputs_dict["head_mask"]
  
              inputs_dict["output_attentions"] = True
              config.output_hidden_states = False
              model = model_class(config=config)
              model.to(torch_device)
              model.eval()
              heads_to_prune = {
                  0: list(range(1, self.model_tester.num_attention_heads)),
                  -1: [0],
              }
              model.prune_heads(heads_to_prune)
  
              with tempfile.TemporaryDirectory() as temp_dir_name:
                  model.save_pretrained(temp_dir_name)
                  model = model_class.from_pretrained(temp_dir_name)
                  model.to(torch_device)
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs[-1]
              self.assertEqual(attentions[0].shape[-3], 1)
              self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
              self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
  
      def test_head_pruning_save_load_from_config_init(self):
          if not self.test_pruning:
              return
  
          for model_class in self.all_model_classes:
              (
                  config,
                  inputs_dict,
              ) = self.model_tester.prepare_config_and_inputs_for_common()
  
              if "head_mask" in inputs_dict:
                  del inputs_dict["head_mask"]
  
              inputs_dict["output_attentions"] = True
              config.output_hidden_states = False
  
              heads_to_prune = {
                  0: list(range(1, self.model_tester.num_attention_heads)),
                  -1: [0],
              }
              config.pruned_heads = heads_to_prune
  
              model = model_class(config=config)
              model.to(torch_device)
              model.eval()
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs[-1]
  
              self.assertEqual(attentions[0].shape[-3], 1)
              self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
              self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
  
      def test_head_pruning_integration(self):
          if not self.test_pruning:
              return
  
          for model_class in self.all_model_classes:
              (
                  config,
                  inputs_dict,
              ) = self.model_tester.prepare_config_and_inputs_for_common()
  
              if "head_mask" in inputs_dict:
                  del inputs_dict["head_mask"]
  
              inputs_dict["output_attentions"] = True
              config.output_hidden_states = False
  
              heads_to_prune = {0: [0], 1: [1, 2]}
              config.pruned_heads = heads_to_prune
  
              model = model_class(config=config)
              model.to(torch_device)
              model.eval()
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs[-1]
  
              self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
              self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
              self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
              self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
  
              with tempfile.TemporaryDirectory() as temp_dir_name:
                  model.save_pretrained(temp_dir_name)
                  model = model_class.from_pretrained(temp_dir_name)
                  model.to(torch_device)
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs[-1]
  
              self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
              self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
              self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
              self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
  
              heads_to_prune = {0: [0], 2: [1, 2]}
              model.prune_heads(heads_to_prune)
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs[-1]
  
              self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
              self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
              self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
              self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
  
              self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
  
      def test_hidden_states_output(self):
          def check_hidden_states_output(inputs_dict, config, model_class):
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
              hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
              expected_num_layers = getattr(
                  self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
              )
              self.assertEqual(len(hidden_states), expected_num_layers)
  
              if hasattr(self.model_tester, "encoder_seq_length"):
                  seq_length = self.model_tester.encoder_seq_length
                  if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
                      seq_length = seq_length * self.model_tester.chunk_length
              else:
                  seq_length = self.model_tester.seq_length
  
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]),
                  [seq_length, self.model_tester.hidden_size],
              )
  
              if config.is_encoder_decoder:
                  hidden_states = outputs.decoder_hidden_states
  
                  self.assertIsInstance(hidden_states, (list, tuple))
                  self.assertEqual(len(hidden_states), expected_num_layers)
                  seq_len = getattr(self.model_tester, "seq_length", None)
                  decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
  
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:]),
                      [decoder_seq_length, self.model_tester.hidden_size],
                  )
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              inputs_dict["output_hidden_states"] = True
              check_hidden_states_output(inputs_dict, config, model_class)
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              config.output_hidden_states = True
  
              check_hidden_states_output(inputs_dict, config, model_class)
  
      def test_retain_grad_hidden_states_attentions(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.output_hidden_states = True
          config.output_attentions = True
  
          # no need to test all models as different heads yield the same functionality
          model_class = self.all_model_classes[0]
          model = model_class(config)
          model.to(torch_device)
  
          inputs = self._prepare_for_class(inputs_dict, model_class)
  
          outputs = model(**inputs)
  
          print(outputs)
          output = outputs[0]
  
          if config.is_encoder_decoder:
              # Seq2Seq models
              encoder_hidden_states = outputs.encoder_hidden_states[0]
              encoder_attentions = outputs.encoder_attentions[0]
              encoder_hidden_states.retain_grad()
              encoder_attentions.retain_grad()
  
              decoder_hidden_states = outputs.decoder_hidden_states[0]
              decoder_attentions = outputs.decoder_attentions[0]
              decoder_hidden_states.retain_grad()
              decoder_attentions.retain_grad()
  
              cross_attentions = outputs.cross_attentions[0]
              cross_attentions.retain_grad()
  
              output.flatten()[0].backward(retain_graph=True)
  
              self.assertIsNotNone(encoder_hidden_states.grad)
              self.assertIsNotNone(encoder_attentions.grad)
              self.assertIsNotNone(decoder_hidden_states.grad)
              self.assertIsNotNone(decoder_attentions.grad)
              self.assertIsNotNone(cross_attentions.grad)
          else:
              # Encoder-/Decoder-only models
              hidden_states = outputs.hidden_states[0]
              attentions = outputs.attentions[0]
  
              hidden_states.retain_grad()
              attentions.retain_grad()
  
              output.flatten()[0].backward(retain_graph=True)
  
              self.assertIsNotNone(hidden_states.grad)
              self.assertIsNotNone(attentions.grad)
  
      def test_feed_forward_chunking(self):
          (
              original_config,
              inputs_dict,
          ) = self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              torch.manual_seed(0)
              config = copy.deepcopy(original_config)
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
  
              torch.manual_seed(0)
              config.chunk_size_feed_forward = 1
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
              self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
  
      def test_resize_tokens_embeddings(self):
          (
              original_config,
              inputs_dict,
          ) = self.model_tester.prepare_config_and_inputs_for_common()
          if not self.test_resize_embeddings:
              return
  
          for model_class in self.all_model_classes:
              config = copy.deepcopy(original_config)
              model = model_class(config)
              model.to(torch_device)
  
              if self.model_tester.is_training is False:
                  model.eval()
  
              model_vocab_size = config.vocab_size
              # Retrieve the embeddings and clone theme
              model_embed = model.resize_token_embeddings(model_vocab_size)
              cloned_embeddings = model_embed.weight.clone()
  
              # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
              model_embed = model.resize_token_embeddings(model_vocab_size + 10)
              self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
              # Check that it actually resizes the embeddings matrix
              self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
              # Check that the model can still do a forward pass successfully (every parameter should be resized)
              model(**self._prepare_for_class(inputs_dict, model_class))
  
              # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
              model_embed = model.resize_token_embeddings(model_vocab_size - 15)
              self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
              # Check that it actually resizes the embeddings matrix
              self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
  
              # Check that the model can still do a forward pass successfully (every parameter should be resized)
              # Input ids should be clamped to the maximum size of the vocabulary
              inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
  
              # make sure that decoder_input_ids are resized as well
              if "decoder_input_ids" in inputs_dict:
                  inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
              model(**self._prepare_for_class(inputs_dict, model_class))
  
              # Check that adding and removing tokens has not modified the first part of the embedding matrix.
              models_equal = True
              for p1, p2 in zip(cloned_embeddings, model_embed.weight):
                  if p1.data.ne(p2.data).sum() > 0:
                      models_equal = False
  
              self.assertTrue(models_equal)
  
      def test_resize_embeddings_untied(self):
          (
              original_config,
              inputs_dict,
          ) = self.model_tester.prepare_config_and_inputs_for_common()
          if not self.test_resize_embeddings:
              return
  
          original_config.tie_word_embeddings = False
  
          # if model cannot untied embeddings -> leave test
          if original_config.tie_word_embeddings:
              return
  
          for model_class in self.all_model_classes:
              config = copy.deepcopy(original_config)
              model = model_class(config).to(torch_device)
  
              # if no output embeddings -> leave test
              if model.get_output_embeddings() is None:
                  continue
  
              # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
              model_vocab_size = config.vocab_size
              model.resize_token_embeddings(model_vocab_size + 10)
              self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
              output_embeds = model.get_output_embeddings()
              self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
              # Check bias if present
              if output_embeds.bias is not None:
                  self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
              # Check that the model can still do a forward pass successfully (every parameter should be resized)
              model(**self._prepare_for_class(inputs_dict, model_class))
  
              # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
              model.resize_token_embeddings(model_vocab_size - 15)
              self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
              # Check that it actually resizes the embeddings matrix
              output_embeds = model.get_output_embeddings()
              self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
              # Check bias if present
              if output_embeds.bias is not None:
                  self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
              # Check that the model can still do a forward pass successfully (every parameter should be resized)
              # Input ids should be clamped to the maximum size of the vocabulary
              inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
              if "decoder_input_ids" in inputs_dict:
                  inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
              # Check that the model can still do a forward pass successfully (every parameter should be resized)
              model(**self._prepare_for_class(inputs_dict, model_class))
  
      def test_model_common_attributes(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding, AdaptiveEmbedding))
              model.set_input_embeddings(torch.nn.Embedding(10, 10))
              x = model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
  
      def test_correct_missing_keys(self):
          if not self.test_missing_keys:
              return
          config, _ = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              base_model_prefix = model.base_model_prefix
  
              if hasattr(model, base_model_prefix):
                  with tempfile.TemporaryDirectory() as temp_dir_name:
                      model.base_model.save_pretrained(temp_dir_name)
                      model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)
  
                      with self.subTest(msg=f"Missing keys for {model.__class__.__name__}"):
                          self.assertGreater(len(loading_info["missing_keys"]), 0)
  
      def test_tie_model_weights(self):
          if not self.test_torchscript:
              return
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          def check_same_values(layer_1, layer_2):
              equal = True
              for p1, p2 in zip(layer_1.weight, layer_2.weight):
                  if p1.data.ne(p2.data).sum() > 0:
                      equal = False
              return equal
  
          for model_class in self.all_model_classes:
              config.torchscript = True
              model_not_tied = model_class(config)
              if model_not_tied.get_output_embeddings() is None:
                  continue
  
              config_tied = copy.deepcopy(config)
              config_tied.torchscript = False
              model_tied = model_class(config_tied)
              params_tied = list(model_tied.parameters())
              # Check that the embedding layer and decoding layer are the same in size and in value
              # self.assertTrue(check_same_values(embeddings, decoding))
  
              # # Check that after modification, they remain the same.
              # embeddings.weight.data.div_(2)
              # # Check that the embedding layer and decoding layer are the same in size and in value
              # self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
              # self.assertTrue(check_same_values(embeddings, decoding))
  
              # # Check that after modification, they remain the same.
              # decoding.weight.data.div_(4)
              # # Check that the embedding layer and decoding layer are the same in size and in value
              # self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
              # self.assertTrue(check_same_values(embeddings, decoding))
  
              # Check that after resize they remain tied.
              model_tied.resize_token_embeddings(config.vocab_size + 10)
              params_tied_2 = list(model_tied.parameters())
              self.assertEqual(len(params_tied_2), len(params_tied))
  
              # decoding.weight.data.mul_(20)
              # # Check that the embedding layer and decoding layer are the same in size and in value
              # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
              # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
  
      def test_model_outputs_equivalence(self):
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          def set_nan_tensor_to_zero(t):
              t[t != t] = 0
              return t
  
          def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
              with torch.no_grad():
                  tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
                  dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
  
                  def recursive_check(tuple_object, dict_object):
                      if isinstance(tuple_object, (List, Tuple)):
                          for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                              recursive_check(tuple_iterable_value, dict_iterable_value)
                      elif tuple_object is None:
                          return
                      else:
                          self.assertTrue(
                              torch.allclose(
                                  set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                              ),
                              msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
                          )
  
                  recursive_check(tuple_output, dict_output)
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class)
              check_equivalence(model, tuple_inputs, dict_inputs)
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              check_equivalence(model, tuple_inputs, dict_inputs)
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class)
              check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class)
              check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              check_equivalence(
                  model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
              )
  
      def test_inputs_embeds(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
  
              if not self.is_encoder_decoder:
                  input_ids = inputs["input_ids"]
                  del inputs["input_ids"]
              else:
                  encoder_input_ids = inputs["input_ids"]
                  decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                  del inputs["input_ids"]
                  inputs.pop("decoder_input_ids", None)
  
              wte = model.get_input_embeddings()
              if not self.is_encoder_decoder:
                  inputs["inputs_embeds"] = wte(input_ids)
              else:
                  inputs["inputs_embeds"] = wte(encoder_input_ids)
                  inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
  
              with torch.no_grad():
                  model(**inputs)[0]
  
      @require_torch_multi_gpu
      def test_multi_gpu_data_parallel_forward(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          # some params shouldn't be scattered by nn.DataParallel
          # so just remove them if they are present.
          blacklist_non_batched_params = ["head_mask", "decoder_head_mask"]
          for k in blacklist_non_batched_params:
              inputs_dict.pop(k, None)
  
          # move input tensors to cuda:O
          for k, v in inputs_dict.items():
              if torch.is_tensor(v):
                  inputs_dict[k] = v.to(0)
  
          for model_class in self.all_model_classes:
              model = model_class(config=config)
              model.to(0)
              model.eval()
  
              # Wrap model in nn.DataParallel
              model = torch.nn.DataParallel(model)
              with torch.no_grad():
                  _ = model(**self._prepare_for_class(inputs_dict, model_class))
  
      @require_torch_multi_gpu
      def test_model_parallelization(self):
          if not self.test_model_parallel:
              return
  
          # a candidate for testing_utils
          def get_current_gpu_memory_use():
              """ returns a list of cuda memory allocations per GPU in MBs"""
  
              per_device_memory = []
              for id in range(torch.cuda.device_count()):
                  with torch.cuda.device(id):
                      per_device_memory.append(torch.cuda.memory_allocated() >> 20)
  
              return per_device_memory
  
          # Needs a large model to see the difference.
          config = self.model_tester.get_large_model_config()
  
          for model_class in self.all_parallelizable_model_classes:
              torch.cuda.empty_cache()
  
              # 1. single gpu memory load + unload + memory measurements
              # Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests)
              memory_at_start = get_current_gpu_memory_use()
  
              # Put model on device 0 and take a memory snapshot
              model = model_class(config)
              model.to("cuda:0")
              memory_after_model_load = get_current_gpu_memory_use()
  
              # The memory use on device 0 should be higher than it was initially.
              self.assertGreater(memory_after_model_load[0], memory_at_start[0])
  
              del model
              gc.collect()
              torch.cuda.empty_cache()
  
              # 2. MP test
              # it's essential to re-calibrate the usage before the next stage
              memory_at_start = get_current_gpu_memory_use()
  
              # Spread model layers over multiple devices
              model = model_class(config)
              model.parallelize()
              memory_after_parallelization = get_current_gpu_memory_use()
  
              # Assert that the memory use on all devices is higher than it was when loaded only on CPU
              for n in range(torch.cuda.device_count()):
                  self.assertGreater(memory_after_parallelization[n], memory_at_start[n])
  
              # Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it
              self.assertLess(memory_after_parallelization[0], memory_after_model_load[0])
  
              # Assert that the memory use of device 1 is higher than it was when the entire model was loaded
              # on device 0 and device 1 wasn't used at all
              self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1])
  
              del model
              gc.collect()
              torch.cuda.empty_cache()
  
      @require_torch_multi_gpu
      def test_model_parallel_equal_results(self):
          if not self.test_model_parallel:
              return
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_parallelizable_model_classes:
              inputs_dict = self._prepare_for_class(inputs_dict, model_class)
  
              def cast_to_device(dictionary, device):
                  output = {}
                  for k, v in dictionary.items():
                      if isinstance(v, torch.Tensor):
                          output[k] = v.to(device)
                      else:
                          output[k] = v
  
                  return output
  
              model = model_class(config)
              output = model(**cast_to_device(inputs_dict, "cpu"))
  
              model.parallelize()
  
              parallel_output = model(**cast_to_device(inputs_dict, "cuda:0"))
  
              for value, parallel_value in zip(output, parallel_output):
                  if isinstance(value, torch.Tensor):
                      self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7))
                  elif isinstance(value, (Tuple, List)):
                      for value_, parallel_value_ in zip(value, parallel_value):
                          self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7))
  
      @require_torch_multi_gpu
      def test_model_parallel_beam_search(self):
          if not self.test_model_parallel:
              return
  
          all_generative_and_parallelizable_model_classes = tuple(
              set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes)
          )
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in all_generative_and_parallelizable_model_classes:
              inputs_dict = self._prepare_for_class(inputs_dict, model_class)
              model = model_class(config)
  
              def cast_to_device(dictionary, device):
                  output = {}
                  for k, v in dictionary.items():
                      if isinstance(v, torch.Tensor):
                          output[k] = v.to(device)
                      else:
                          output[k] = v
  
                  return output
  
              model.parallelize()
              model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
  
  
  global_rng = random.Random()
  
  
  def ids_tensor(shape, vocab_size, rng=None, name=None):
      #  Creates a random int32 tensor of the shape within the vocab size
      if rng is None:
          rng = global_rng
  
      total_dims = 1
      for dim in shape:
          total_dims *= dim
  
      values = []
      for _ in range(total_dims):
          values.append(rng.randint(0, vocab_size - 1))
  
      return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()
  
  
  def random_attention_mask(shape, rng=None, name=None):
      attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None)
      # make sure that at least one token is attended to for each batch
      attn_mask[:, -1] = 1
      return attn_mask
  
  
  def floats_tensor(shape, scale=1.0, rng=None, name=None):
      """Creates a random float32 tensor"""
      if rng is None:
          rng = global_rng
  
      total_dims = 1
      for dim in shape:
          total_dims *= dim
  
      values = []
      for _ in range(total_dims):
          values.append(rng.random() * scale)
  
      return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
  
  
  @require_torch
  class ModelUtilsTest(unittest.TestCase):
      @slow
      def test_model_from_pretrained(self):
          for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              config = BertConfig.from_pretrained(model_name)
              self.assertIsNotNone(config)
              self.assertIsInstance(config, PretrainedConfig)
  
              model = BertModel.from_pretrained(model_name)
              model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
              self.assertIsNotNone(model)
              self.assertIsInstance(model, PreTrainedModel)
              for value in loading_info.values():
                  self.assertEqual(len(value), 0)
  
              config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
  
              # Not sure this is the intended behavior. TODO fix Lysandre & Thom
              config.name_or_path = model_name
  
              model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
              self.assertEqual(model.config.output_hidden_states, True)
              self.assertEqual(model.config, config)
  
      def test_model_from_pretrained_with_different_pretrained_model_name(self):
          model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
          self.assertIsNotNone(model)
  
          with self.assertRaises(Exception) as context:
              BertModel.from_pretrained(TINY_T5)
          self.assertTrue("You tried to initiate a model of type" in str(context.exception))
  