text: # coding=utf-8
  # Copyright 2021 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  
  import copy
  import inspect
  import os
  import tempfile
  import unittest
  
  from transformers import ImageGPTConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import (
      ModelTesterMixin,
      _config_zero_init,
      floats_tensor,
      ids_tensor,
      random_attention_mask,
  )
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
      )
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import ImageGPTFeatureExtractor
  
  
  class ImageGPTModelTester:
      def __init__(
          self,
          parent,
          batch_size=14,
          seq_length=7,
          is_training=True,
          use_token_type_ids=True,
          use_input_mask=True,
          use_labels=True,
          use_mc_token_ids=True,
          vocab_size=99,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=512,
          type_vocab_size=16,
          type_sequence_label_size=2,
          initializer_range=0.02,
          num_labels=3,
          num_choices=4,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.seq_length = seq_length
          self.is_training = is_training
          self.use_token_type_ids = use_token_type_ids
          self.use_input_mask = use_input_mask
          self.use_labels = use_labels
          self.use_mc_token_ids = use_mc_token_ids
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.intermediate_size = intermediate_size
          self.hidden_act = hidden_act
          self.hidden_dropout_prob = hidden_dropout_prob
          self.attention_probs_dropout_prob = attention_probs_dropout_prob
          self.max_position_embeddings = max_position_embeddings
          self.type_vocab_size = type_vocab_size
          self.type_sequence_label_size = type_sequence_label_size
          self.initializer_range = initializer_range
          self.num_labels = num_labels
          self.num_choices = num_choices
          self.scope = None
  
      def get_large_model_config(self):
          return ImageGPTConfig.from_pretrained("imagegpt")
  
      def prepare_config_and_inputs(
          self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
      ):
          pixel_values = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
  
          input_mask = None
          if self.use_input_mask:
              input_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          token_type_ids = None
          if self.use_token_type_ids:
              token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
  
          mc_token_ids = None
          if self.use_mc_token_ids:
              mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
  
          sequence_labels = None
          token_labels = None
          choice_labels = None
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
              token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
              choice_labels = ids_tensor([self.batch_size], self.num_choices)
  
          config = self.get_config(
              gradient_checkpointing=gradient_checkpointing,
              scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
              reorder_and_upcast_attn=reorder_and_upcast_attn,
          )
  
          head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
  
          return (
              config,
              pixel_values,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )
  
      def get_config(
          self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
      ):
          return ImageGPTConfig(
              vocab_size=self.vocab_size,
              n_embd=self.hidden_size,
              n_layer=self.num_hidden_layers,
              n_head=self.num_attention_heads,
              n_inner=self.intermediate_size,
              activation_function=self.hidden_act,
              resid_pdrop=self.hidden_dropout_prob,
              attn_pdrop=self.attention_probs_dropout_prob,
              n_positions=self.max_position_embeddings,
              type_vocab_size=self.type_vocab_size,
              initializer_range=self.initializer_range,
              use_cache=True,
              gradient_checkpointing=gradient_checkpointing,
              scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
              reorder_and_upcast_attn=reorder_and_upcast_attn,
          )
  
      def prepare_config_and_inputs_for_decoder(self):
          (
              config,
              pixel_values,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = self.prepare_config_and_inputs()
  
          encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
  
          return (
              config,
              pixel_values,
              input_mask,
              head_mask,
              token_type_ids,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )
  
      def create_and_check_imagegpt_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
          model = ImageGPTModel(config=config)
          model.to(torch_device)
          model.eval()
  
          result = model(pixel_values, token_type_ids=token_type_ids, head_mask=head_mask)
          result = model(pixel_values, token_type_ids=token_type_ids)
          result = model(pixel_values)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(len(result.past_key_values), config.n_layer)
  
      def create_and_check_lm_head_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
          model = ImageGPTForCausalImageModeling(config)
          model.to(torch_device)
          model.eval()
  
          labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
          result = model(pixel_values, token_type_ids=token_type_ids, labels=labels)
          self.parent.assertEqual(result.loss.shape, ())
          # ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
  
      def create_and_check_imagegpt_for_image_classification(
          self, config, pixel_values, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
      ):
          config.num_labels = self.num_labels
          model = ImageGPTForImageClassification(config)
          model.to(torch_device)
          model.eval()
          result = model(pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
  
          (
              config,
              pixel_values,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = config_and_inputs
  
          inputs_dict = {
              "pixel_values": pixel_values,
              "token_type_ids": token_type_ids,
              "head_mask": head_mask,
          }
  
          return config, inputs_dict
  
  
  @require_torch
  class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
      )
      all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
      test_missing_keys = False
      input_name = "pixel_values"
  
      # as ImageGPTForImageClassification isn't included in any auto mapping, we add labels here
      def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
          inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
  
          if return_labels:
              if model_class.__name__ == "ImageGPTForImageClassification":
                  inputs_dict["labels"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
  
          return inputs_dict
  
      # we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
      def _check_scores(self, batch_size, scores, length, config):
          expected_shape = (batch_size, config.vocab_size - 1)
          self.assertIsInstance(scores, tuple)
          self.assertEqual(len(scores), length)
          self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
  
      def setUp(self):
          self.model_tester = ImageGPTModelTester(self)
          self.config_tester = ConfigTester(self, config_class=ImageGPTConfig, n_embd=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_imagegpt_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_imagegpt_model(*config_and_inputs)
  
      def test_imagegpt_causal_lm(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
  
      def test_imagegpt_image_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_imagegpt_for_image_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = ImageGPTModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
      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()]
  
              expected_arg_names = ["input_ids"]
              self.assertListEqual(arg_names[:1], expected_arg_names)
  
      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["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
  
              # 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["pixel_values"].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_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))
  
              pixel_values = inputs["pixel_values"]
              del inputs["pixel_values"]
  
              wte = model.get_input_embeddings()
              inputs["inputs_embeds"] = wte(pixel_values)
  
              with torch.no_grad():
                  model(**inputs)[0]
  
      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:
                  pixel_values = inputs["pixel_values"]
                  traced_model = torch.jit.trace(model, pixel_values)
              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()
  
              non_persistent_buffers = {}
              for key in loaded_model_state_dict.keys():
                  if key not in model_state_dict.keys():
                      non_persistent_buffers[key] = loaded_model_state_dict[key]
  
              loaded_model_state_dict = {
                  key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
              }
  
              self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
  
              model_buffers = list(model.buffers())
              for non_persistent_buffer in non_persistent_buffers.values():
                  found_buffer = False
                  for i, model_buffer in enumerate(model_buffers):
                      if torch.equal(non_persistent_buffer, model_buffer):
                          found_buffer = True
                          break
  
                  self.assertTrue(found_buffer)
                  model_buffers.pop(i)
  
              models_equal = True
              for layer_name, p1 in model_state_dict.items():
                  if layer_name in loaded_model_state_dict:
                      p2 = loaded_model_state_dict[layer_name]
                      if p1.data.ne(p2.data).sum() > 0:
                          models_equal = False
  
              self.assertTrue(models_equal)
  
  
  # We will verify our results on an image of cute cats
  def prepare_img():
      image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
      return image
  
  
  @require_torch
  @require_vision
  class ImageGPTModelIntegrationTest(unittest.TestCase):
      @cached_property
      def default_feature_extractor(self):
          return ImageGPTFeatureExtractor.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
  
      @slow
      def test_inference_causal_lm_head(self):
          model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
  
          feature_extractor = self.default_feature_extractor
          image = prepare_img()
          inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
  
          # forward pass
          outputs = model(**inputs)
  
          # verify the logits
          expected_shape = torch.Size((1, 1024, 512))
          self.assertEqual(outputs.logits.shape, expected_shape)
  
          expected_slice = torch.tensor(
              [[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
          ).to(torch_device)
  
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))
  