class_docstr: Full testing suite of the `BetterTransformers` integration into Hugging Face
  `transformers` ecosystem. Check the docstring of each test to understand the
  purpose of each test. Basically we test:
  - if the conversion dictionnary is consistent, ie if the converted model exists
  in HuggingFace `transformers` library.
  - if the converted model produces the same logits as the original model.
  - if the converted model is faster than the original model.
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docstr: This tests if a model loaded with `accelerate` will be successfully converted
  into its BetterTransformers format.
  If this works for roberta, it should work for all other models too.
docstr_tok: ["This","tests","if","a","model","loaded","with","`","accelerate","`","will","be","successfully","converted","into","its","BetterTransformers","format",".","If","this","works","for","roberta",",","it","should","work","for","all","other","models","too","."]
code: def check_accelerate_compatibility_cpu_gpu(
      self, keep_original_model=True, max_memory=None
  ):
      r"""
      This tests if a model loaded with `accelerate` will be successfully converted
      into its BetterTransformers format.
      If this works for roberta, it should work for all other models too.
      """
  
      hf_model = AutoModel.from_pretrained(
          "xlm-roberta-base", device_map="auto", max_memory=max_memory
      ).eval()
      bt_model = BetterTransformer.transform(
          hf_model,
          keep_original_model=keep_original_model,
          max_memory=max_memory,
      )
  
      inputs_ids = torch.LongTensor(
          [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]
      )
      attention_mask = torch.Tensor(
          [[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0]]
      )
  
      # Check that the model has been dispatched on CPU and GPU
      self.assertSetEqual(
          set(hf_model.hf_device_map.values()), set(max_memory)
      )
      self.assertSetEqual(
          set(bt_model.hf_device_map.values()), set(max_memory)
      )
  
      # Check that the model has weights on GPU and CPU
      self.assertEqual(
          bt_model.encoder.layer[0].in_proj_weight.device,
          torch.device("cuda:0"),
      )
      # Weights that are offloaded on the CPU are offloaded on the `meta` device
      if "cpu" in set(max_memory):
          self.assertEqual(
              bt_model.encoder.layer[-1].in_proj_weight.device,
              torch.device("meta"),
          )
  
      # Forward pass should work
      output_bt = bt_model(inputs_ids, attention_mask)
      output_hf = hf_model(inputs_ids, attention_mask)
  
      # Assert that the output has been correctly set to the CPU!
      self.assertEqual(output_bt[0].device, torch.device("cpu"))
  
      # Final step: check the logits
      self.assertTrue(
          torch.allclose(
              output_bt[0][0, :3], output_hf[0][0, :3], atol=1e-3
          )
      )
  
      # Check that the padding has been taken into account correctly - this checks also if the hooks
      # have been correctly set.
      self.assertTrue(
          torch.allclose(
              output_bt[0][1, 3:], torch.zeros_like(output_bt[0][1, 3:])
          )
      )
      gc.collect()
  
imports: import gc
  import timeit
  import pytest
  import torch
  import transformers
  from parameterized import parameterized
  from testing_utils import MODELS_DICT, BetterTransformersTestMixin
  from transformers import AutoModel, AutoProcessor, AutoTokenizer
  from optimum.bettertransformer import BetterTransformer
  from optimum.pipelines import pipeline
  from optimum.utils.testing_utils import grid_parameters, require_accelerate, require_torch_gpu
label_desc: Use image node_id 7 for calling the BetterTransformersEncoderTest obj's underlying member method code with example usage: obj.check_accelerate_compatibility_cpu_gpu(keep_original_model, max_memory) without return types