code: # 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 logging
  import os.path
  import random
  import tempfile
  import unittest
  
  from transformers import is_torch_available
  
  from .utils import require_torch, slow, torch_device
  
  
  if is_torch_available():
      import torch
      import numpy as np
  
      from transformers import (
          AdaptiveEmbedding,
          PretrainedConfig,
          PreTrainedModel,
          BertModel,
          BertConfig,
          BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
          top_k_top_p_filtering,
      )
  
  
  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, 0.0)
      return configs_no_init
  
  
  @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
      is_encoder_decoder = False
  
      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(**inputs_dict)
              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(**inputs_dict)
  
                  # 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_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().item(),
                          [0.0, 1.0],
                          msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
                      )
  
      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(**inputs_dict)[0]
                  second = model(**inputs_dict)[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_attention_outputs(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          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, "key_length", decoder_seq_length)
          encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
  
          for model_class in self.all_model_classes:
              config.output_attentions = True
              config.output_hidden_states = False
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**inputs_dict)
              attentions = outputs[-1]
              self.assertEqual(model.config.output_attentions, True)
              self.assertEqual(model.config.output_hidden_states, False)
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
              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 = (
                      4  # decoder_features_or_logits, decoder_attentions, encoder_features, encoder_attentions
                  )
                  decoder_attention_idx = 1
                  if "lm_labels" in inputs_dict:  # loss will come first
                      correct_outlen += 1  # compute loss
                      decoder_attention_idx += 1
                  self.assertEqual(out_len, correct_outlen)
  
                  decoder_attentions = outputs[decoder_attention_idx]
                  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],
                  )
  
              # Check attention is always last and order is fine
              config.output_attentions = True
              config.output_hidden_states = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**inputs_dict)
              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
              self.assertEqual(model.config.output_attentions, True)
              self.assertEqual(model.config.output_hidden_states, True)
  
              self_attentions = outputs[-1]
              self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
              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 = inputs_dict["input_ids"]  # Let's keep only input_ids
  
              try:
                  traced_gpt2 = torch.jit.trace(model, inputs)
              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_gpt2, 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()
  
          config.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 = inputs_dict.copy()
              inputs["head_mask"] = head_mask
  
              outputs = model(**inputs)
  
              # 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
  
              attentions = outputs[-1]
  
              # 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.assertIsNotNone(multihead_outputs)
              self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
              self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
              self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
              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)
  
      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"]
  
              config.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(**inputs_dict)
  
              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"]
  
              config.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(**inputs_dict)
              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"]
  
              config.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(**inputs_dict)
              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"]
  
              config.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(**inputs_dict)
              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(**inputs_dict)
              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(**inputs_dict)
              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):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              config.output_hidden_states = True
              config.output_attentions = False
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**inputs_dict)
              hidden_states = outputs[-1]
              self.assertEqual(model.config.output_attentions, False)
              self.assertEqual(model.config.output_hidden_states, True)
              self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]),
                  [
                      self.model_tester.encoder_seq_length
                      if hasattr(self.model_tester, "encoder_seq_length")
                      else self.model_tester.seq_length,
                      self.model_tester.hidden_size,
                  ],
              )
  
      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)
  
              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(**inputs_dict)
  
              # 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)
              model(**inputs_dict)
  
              # 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_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="Missing keys for {}".format(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
  
              params_not_tied = list(model_not_tied.parameters())
  
              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.assertGreater(len(params_not_tied), len(params_tied))
              # 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.assertGreater(len(params_not_tied), len(params_tied))
              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_inputs_embeds(self):
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          if not self.is_encoder_decoder:
              input_ids = inputs_dict["input_ids"]
              del inputs_dict["input_ids"]
          else:
              encoder_input_ids = inputs_dict["input_ids"]
              decoder_input_ids = inputs_dict.get("decoder_input_ids", encoder_input_ids)
              del inputs_dict["input_ids"]
              inputs_dict.pop("decoder_input_ids", None)
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              wte = model.get_input_embeddings()
              if not self.is_encoder_decoder:
                  inputs_dict["inputs_embeds"] = wte(input_ids)
              else:
                  inputs_dict["inputs_embeds"] = wte(encoder_input_ids)
                  inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids)
  
              with torch.no_grad():
                  model(**inputs_dict)
  
      def test_lm_head_model_random_generate(self):
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          input_ids = inputs_dict.get("input_ids")
  
          if self.is_encoder_decoder:
              config.output_past = True  # needed for Bart TODO: might have to update for other encoder-decoder models
  
          for model_class in self.all_generative_model_classes:
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              if config.bos_token_id is None:
                  with self.assertRaises(AssertionError):
                      model.generate(do_sample=True, max_length=5)
                  # batch_size = 1
                  self._check_generated_tokens(model.generate(input_ids, do_sample=True))
                  # batch_size = 1, num_beams > 1
                  self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_beams=3))
              else:
                  # batch_size = 1
                  self._check_generated_tokens(model.generate(do_sample=True, max_length=5))
                  # batch_size = 1, num_beams > 1
                  self._check_generated_tokens(model.generate(do_sample=True, max_length=5, num_beams=3))
  
              with self.assertRaises(AssertionError):
                  # generating multiple sequences when greedy no beam generation
                  # is not allowed as it would always generate the same sequences
                  model.generate(input_ids, do_sample=False, num_return_sequences=2)
  
              with self.assertRaises(AssertionError):
                  # generating more sequences than having beams leads is not possible
                  model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
  
              # batch_size > 1, sample
              self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_return_sequences=3))
              # batch_size > 1, greedy
              self._check_generated_tokens(model.generate(input_ids, do_sample=False))
  
              # batch_size > 1, num_beams > 1, sample
              self._check_generated_tokens(
                  model.generate(input_ids, do_sample=True, num_beams=3, num_return_sequences=3,)
              )
              # batch_size > 1, num_beams > 1, greedy
              self._check_generated_tokens(
                  model.generate(input_ids, do_sample=False, num_beams=3, num_return_sequences=3)
              )
  
      def _check_generated_tokens(self, output_ids):
          for token_id in output_ids[0].tolist():
              self.assertGreaterEqual(token_id, 0)
              self.assertLess(token_id, self.model_tester.vocab_size)
  
  
  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 floats_tensor(shape, scale=1.0, rng=None, name=None):
      """Creates a random float32 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.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):
          logging.basicConfig(level=logging.INFO)
          for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[: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)
              model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
              self.assertEqual(model.config.output_attentions, True)
              self.assertEqual(model.config.output_hidden_states, True)
              self.assertEqual(model.config, config)
  
  
  @require_torch
  class UtilsFunctionsTest(unittest.TestCase):
  
      # tests whether the top_k_top_p function behaves as expected
      def test_top_k_top_p_filtering(self):
          logits = torch.tensor(
              [
                  [
                      8.2220991,  # 3rd highest value; idx. 0
                      -0.5620044,
                      5.23229752,
                      4.0386393,
                      -6.8798378,
                      -0.54785802,
                      -3.2012153,
                      2.92777176,
                      1.88171953,
                      7.35341276,  # 5th highest value; idx. 9
                      8.43207833,  # 2nd highest value; idx. 10
                      -9.85711836,
                      -5.96209236,
                      -1.13039161,
                      -7.1115294,
                      -0.8369633,
                      -5.3186408,
                      7.06427407,
                      0.81369344,
                      -0.82023817,
                      -5.9179796,
                      0.58813443,
                      -6.99778438,
                      4.71551189,
                      -0.18771637,
                      7.44020759,  # 4th highest value; idx. 25
                      9.38450987,  # 1st highest value; idx. 26
                      2.12662941,
                      -9.32562038,
                      2.35652522,
                  ],  # cummulative prob of 5 highest values <= 0.6
                  [
                      0.58425518,
                      4.53139238,
                      -5.57510464,
                      -6.28030699,
                      -7.19529503,
                      -4.02122551,
                      1.39337037,
                      -6.06707057,
                      1.59480517,
                      -9.643119,
                      0.03907799,
                      0.67231762,
                      -8.88206726,
                      6.27115922,  # 4th highest value; idx. 13
                      2.28520723,
                      4.82767506,
                      4.30421368,
                      8.8275313,  # 2nd highest value; idx. 17
                      5.44029958,  # 5th highest value; idx. 18
                      -4.4735794,
                      7.38579536,  # 3rd highest value; idx. 20
                      -2.91051663,
                      2.61946077,
                      -2.5674762,
                      -9.48959302,
                      -4.02922645,
                      -1.35416918,
                      9.67702323,  # 1st highest value; idx. 27
                      -5.89478553,
                      1.85370467,
                  ],  # cummulative prob of 5 highest values <= 0.6
              ],
              dtype=torch.float,
              device=torch_device,
          )
  
          non_inf_expected_idx = torch.tensor(
              [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
              dtype=torch.long,
              device=torch_device,
          )  # expected non filtered idx as noted above
  
          non_inf_expected_output = torch.tensor(
              [
                  8.2221,
                  7.3534,
                  8.4321,
                  7.4402,
                  9.3845,
                  6.2712,
                  8.8275,
                  5.4403,
                  7.3858,
                  9.6770,
              ],  # expected non filtered values as noted above
              dtype=torch.float,
              device=torch_device,
          )
  
          output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
          non_inf_output = output[output != -float("inf")].to(device=torch_device)
          non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)
  
          self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
          self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))
  