code: 
  
  
  import unittest
  
  from transformers import is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  
  
  if is_torch_available():
  	import torch
  
  	from transformers import CamembertModel
  
  
  
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		CamembertModel.from_pretrained("""camembert-base"""		)
  													model.to(_UpperCAmelCase		)
  
  													a_					=		torch.tensor(
  													    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,    device=_UpperCAmelCase			,    dtype=torch.long			,    )  # J'aime le camembert !
  													with torch.no_grad():
  																			a_					=		model(_UpperCAmelCase		)["""last_hidden_state"""]
  													a_					=		torch.Size((1, 10, 768)		)
  													self.assertEqual(output.shape			,    _UpperCAmelCase		)
  													# compare the actual values for a slice.
  													a_					=		torch.tensor(
  													    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,    device=_UpperCAmelCase			,    dtype=torch.float			,    )
  													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
  													# camembert.eval()
  													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
  
  													self.assertTrue(torch.allclose(output[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		ArgumentParser(
  						    description=(
  						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
  						    )      )
  
  						# Optional arguments for the launch helper
  						parser.add_argument("""--num_cores"""       ,    type=UpperCAmelCase__       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=UpperCAmelCase__       ,    help=(
  						        """The full path to the single TPU training """
  						        """program/script to be launched in parallel, """
  						        """followed by all the arguments for the """
  						        """training script"""
  						    )       ,    )
  
  						# rest from the training program
  						parser.add_argument("""training_script_args"""       ,    nargs=UpperCAmelCase__      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]
  
  						xmp.spawn(mod._mp_fn       ,    args=()       ,    nprocs=args.num_cores      )
  
  
  if __name__ == "__main__":
  	main()