code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  import os
  
  
  _a      :				List[Any]											=					{
      """pytorch""": {
          """1.13""": {
              """torch""": """1.13.1""",
              """torchvision""": """0.14.1""",
              """torchaudio""": """0.13.1""",
              """python""": 3.9,
              """cuda""": """cu116""",
              """install""": (
                  """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                  """ --extra-index-url https://download.pytorch.org/whl/cu116"""
              ),
              """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
          },
          """1.12""": {
              """torch""": """1.12.1""",
              """torchvision""": """0.13.1""",
              """torchaudio""": """0.12.1""",
              """python""": 3.9,
              """cuda""": """cu113""",
              """install""": (
                  """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                  """ --extra-index-url https://download.pytorch.org/whl/cu113"""
              ),
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """1.11""": {
              """torch""": """1.11.0""",
              """torchvision""": """0.12.0""",
              """torchaudio""": """0.11.0""",
              """python""": 3.9,
              """cuda""": """cu113""",
              """install""": (
                  """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                  """ --extra-index-url https://download.pytorch.org/whl/cu113"""
              ),
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """1.10""": {
              """torch""": """1.10.2""",
              """torchvision""": """0.11.3""",
              """torchaudio""": """0.10.2""",
              """python""": 3.9,
              """cuda""": """cu113""",
              """install""": (
                  """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                  """ --extra-index-url https://download.pytorch.org/whl/cu113"""
              ),
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          # torchaudio < 0.10 has no CUDA-enabled binary distributions
          """1.9""": {
              """torch""": """1.9.1""",
              """torchvision""": """0.10.1""",
              """torchaudio""": """0.9.1""",
              """python""": 3.9,
              """cuda""": """cu111""",
              """install""": (
                  """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                  """ --extra-index-url https://download.pytorch.org/whl/cu111"""
              ),
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
      },
      """tensorflow""": {
          """2.11""": {
              """tensorflow""": """2.11.1""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """2.10""": {
              """tensorflow""": """2.10.1""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """2.9""": {
              """tensorflow""": """2.9.3""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """2.8""": {
              """tensorflow""": """2.8.2""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """2.7""": {
              """tensorflow""": """2.7.3""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """2.6""": {
              """tensorflow""": """2.6.5""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
          """2.5""": {
              """tensorflow""": """2.5.3""",
              """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
              """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
          },
      },
  }
  
  
  if __name__ == "__main__":
  	_a      :				Union[str, Any]											=					argparse.ArgumentParser("""Choose the framework and version to install""")
  	parser.add_argument(
  	    """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
  	)
  	parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
  	_a      :				int											=					parser.parse_args()
  
  	_a      :				Any											=					past_versions_testing[args.framework][args.version]
  
  	os.system(f'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
  	print(f'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
  
  	_a      :				Union[str, Any]											=					""""""
  	if args.framework == "pytorch":
  		_a      :				Tuple											=					info["""cuda"""]
  	os.system(f'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
  	print(f'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  _a      :				Optional[Any]											=					torch.cuda.is_available()
  _a      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  _a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  _a      :				Optional[Any]											=					{
      """max_tokens_per_batch""": None,
      """supervise_forward""": True,
      """normalize_hidden""": True,
      """label_smoothing""": 0.2,
      """eval_max_gen_length""": None,
      """eval_beams""": 1,
      """val_metric""": """loss""",
      """save_top_k""": 1,
      """adafactor""": True,
      """early_stopping_patience""": 2,
      """logger_name""": """default""",
      """length_penalty""": 0.5,
      """cache_dir""": """""",
      """task""": """summarization""",
      """num_workers""": 2,
      """alpha_hid""": 0,
      """freeze_embeds""": True,
      """enc_only""": False,
      """tgt_suffix""": """""",
      """resume_from_checkpoint""": None,
      """sortish_sampler""": True,
      """student_decoder_layers""": 1,
      """val_check_interval""": 1.0,
      """output_dir""": """""",
      """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      """no_teacher""": False,
      """fp16_opt_level""": """O1""",
      """gpus""": 1 if CUDA_AVAILABLE else 0,
      """n_tpu_cores""": 0,
      """max_grad_norm""": 1.0,
      """do_train""": True,
      """do_predict""": True,
      """accumulate_grad_batches""": 1,
      """server_ip""": """""",
      """server_port""": """""",
      """seed""": 4_2,
      """model_name_or_path""": """sshleifer/bart-tiny-random""",
      """config_name""": """""",
      """tokenizer_name""": """facebook/bart-large""",
      """do_lower_case""": False,
      """learning_rate""": 0.3,
      """lr_scheduler""": """linear""",
      """weight_decay""": 0.0,
      """adam_epsilon""": 1E-08,
      """warmup_steps""": 0,
      """max_epochs""": 1,
      """train_batch_size""": 2,
      """eval_batch_size""": 2,
      """max_source_length""": 1_2,
      """max_target_length""": 1_2,
      """val_max_target_length""": 1_2,
      """test_max_target_length""": 1_2,
      """fast_dev_run""": False,
      """no_cache""": False,
      """n_train""": -1,
      """n_val""": -1,
      """n_test""": -1,
      """student_encoder_layers""": 1,
      """freeze_encoder""": False,
      """auto_scale_batch_size""": False,
      """overwrite_output_dir""": False,
      """student""": None,
  }
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Any:
  			__lowerCAmelCase      		=       """\n""".join(lowercase			)
  			Path(lowercase			).open("""w"""			).writelines(lowercase			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
  			for split in ["train", "val", "test"]:
  						_dump_articles(os.path.join(lowercase      ,			f'{split}.source'			)      ,			lowercase			)
  						_dump_articles(os.path.join(lowercase      ,			f'{split}.target'			)      ,			lowercase			)
  			return tmp_dir
  
  
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		@classmethod
  		def 						lowerCamelCase__    (						cls    ):
  
  
  
  					'''simple docstring'''
  
  					return cls
  		@require_torch_multi_gpu
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       {
  					    """no_teacher""": True,
  					    """freeze_encoder""": True,
  					    """gpus""": 2,
  					    """overwrite_output_dir""": True,
  					    """sortish_sampler""": True,
  					}
  					self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE,check_contents=__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       {
  					    """label_smoothing""": 0.0,
  					    """early_stopping_patience""": -1,
  					    """train_batch_size""": 1,
  					    """eval_batch_size""": 2,
  					    """max_epochs""": 2,
  					    """alpha_mlm""": 0.2,
  					    """alpha_ce""": 0.8,
  					    """do_predict""": True,
  					    """model_name_or_path""": """sshleifer/tinier_bart""",
  					    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
  					    """val_check_interval""": 0.5,
  					}
  					default_updates.update(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       CHEAP_ARGS.copy()
  					__lowerCAmelCase      		=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
  					__lowerCAmelCase      		=       self.get_auto_remove_tmp_dir()
  					args_d.update(data_dir=__SCREAMING_SNAKE_CASE,output_dir=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  
  					def convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  								if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
  											return ""
  								if v is False or v is None:
  											return ""
  								if v is True:  # or len(str(v))==0:
  											return f'--{k}'
  								return f'--{k}={v}'
  
  					__lowerCAmelCase      		=       [x for x in (convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE    )]
  					__lowerCAmelCase      		=       [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
  					execute_subprocess_async(__SCREAMING_SNAKE_CASE,env=self.get_env()    )
  
  					__lowerCAmelCase      		=       os.listdir(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {os.path.basename(__SCREAMING_SNAKE_CASE    ) for p in contents}
  					__lowerCAmelCase      		=       [p for p in contents if p.endswith("""ckpt"""    )]
  					assert len(__SCREAMING_SNAKE_CASE    ) > 0
  
  					self.assertIn("""test_generations.txt""",__SCREAMING_SNAKE_CASE    )
  					self.assertIn("""test_results.txt""",__SCREAMING_SNAKE_CASE    )
  
  					# get the following from the module, (we don't have access to `model` here)
  					__lowerCAmelCase      		=       os.path.join(__SCREAMING_SNAKE_CASE,"""metrics.json"""    )
  					__lowerCAmelCase      		=       """rouge2"""
  
  					__lowerCAmelCase      		=       load_json(__SCREAMING_SNAKE_CASE    )
  					# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
  					print(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       metrics["""val"""][-1]
  					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""],0.01    )
  					self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],__SCREAMING_SNAKE_CASE    )
  					self.assertEqual(len(metrics["""test"""]    ),1    )
  					__lowerCAmelCase      		=       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1    )
  					self.assertEqual(len(metrics["""val"""]    ),__SCREAMING_SNAKE_CASE    )
  
  