code: 
  
  
  
  """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
  
  
  _lowerCAmelCase					:Dict  =      torch.cuda.is_available()
  _lowerCAmelCase					:Optional[Any]  =      [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  _lowerCAmelCase					:Union[str, Any]  =      ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  _lowerCAmelCase					:Optional[int]  =      {
      """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""": 42,
      """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""": 12,
      """max_target_length""": 12,
      """val_max_target_length""": 12,
      """test_max_target_length""": 12,
      """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_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	str							):
      _UpperCAmelCase							:						Optional[Any]       							=					"""\n""".join(__lowerCamelCase							)
      Path(__lowerCamelCase							).open('''w'''							).writelines(__lowerCamelCase							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]							):
      for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__lowerCamelCase  , F'{split}.source'							)  , __lowerCamelCase							)
          _dump_articles(os.path.join(__lowerCamelCase  , F'{split}.target'							)  , __lowerCamelCase							)
      return tmp_dir
  
  
  class 							_UpperCAmelCase							(				__lowerCamelCase   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls	)							->      Tuple:
           return cls
  
  
  
  
  
       @require_torch_multi_gpu
       def 				__lowerCAmelCase			(      self	)							->      Dict:
           _UpperCAmelCase							:						Any       							=					{
               """no_teacher""": True,
               """freeze_encoder""": True,
               """gpus""": 2,
               """overwrite_output_dir""": True,
               """sortish_sampler""": True,
           }
           self._test_distiller_cli_fork(_lowerCAmelCase ,			check_contents=_lowerCAmelCase	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=True	)							->      Any:
           _UpperCAmelCase							:						Optional[Any]       							=					{
               """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(_lowerCAmelCase	)
           _UpperCAmelCase							:						dict       							=					CHEAP_ARGS.copy()
           _UpperCAmelCase							:						List[str]       							=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
           _UpperCAmelCase							:						int       							=					self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=_lowerCAmelCase ,			output_dir=_lowerCAmelCase ,			**_lowerCAmelCase	)
  
           def convert(A ,			A	):
               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}'
  
           _UpperCAmelCase							:						Optional[int]       							=					[x for x in (convert(_lowerCAmelCase ,			_lowerCAmelCase	) for k, v in args_d.items()) if len(_lowerCAmelCase	)]
           _UpperCAmelCase							:						Optional[Any]       							=					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
           execute_subprocess_async(_lowerCAmelCase ,			env=self.get_env()	)
  
           _UpperCAmelCase							:						Optional[int]       							=					os.listdir(_lowerCAmelCase	)
           _UpperCAmelCase							:						Dict       							=					{os.path.basename(_lowerCAmelCase	) for p in contents}
           _UpperCAmelCase							:						str       							=					[p for p in contents if p.endswith('''ckpt'''	)]
           assert len(_lowerCAmelCase	) > 0
  
           self.assertIn('''test_generations.txt''' ,			_lowerCAmelCase	)
           self.assertIn('''test_results.txt''' ,			_lowerCAmelCase	)
  
           # get the following from the module, (we don't have access to `model` here)
           _UpperCAmelCase							:						Any       							=					os.path.join(_lowerCAmelCase ,			'''metrics.json'''	)
           _UpperCAmelCase							:						Optional[int]       							=					"""rouge2"""
  
           _UpperCAmelCase							:						int       							=					load_json(_lowerCAmelCase	)
           # {'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(_lowerCAmelCase	)
           _UpperCAmelCase							:						Dict       							=					metrics["""val"""][-1]
           self.assertGreaterEqual(last_step_stats['''val_avg_gen_time'''] ,			0.01	)
           self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'] ,			_lowerCAmelCase	)
           self.assertEqual(len(metrics['''test''']	) ,			1	)
           _UpperCAmelCase							:						Optional[int]       							=					int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1	)
           self.assertEqual(len(metrics['''val''']	) ,			_lowerCAmelCase	)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import logging
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.albert.modeling_albert import (
      ALBERT_INPUTS_DOCSTRING,
      ALBERT_START_DOCSTRING,
      AlbertModel,
      AlbertPreTrainedModel,
      AlbertTransformer,
  )
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.getLogger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A=None ,			A=None	)							->      Tuple:
           if current_layer == 0:
               _UpperCAmelCase							:						Optional[Any]       							=					self.embedding_hidden_mapping_in(A	)
           else:
               _UpperCAmelCase							:						Union[str, Any]       							=					hidden_states[0]
  
           _UpperCAmelCase							:						Dict       							=					int(self.config.num_hidden_layers / self.config.num_hidden_groups	)
  
           # Index of the hidden group
           _UpperCAmelCase							:						Dict       							=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)	)
  
           _UpperCAmelCase							:						List[Any]       							=					self.albert_layer_groups[group_idx](
               A ,			A ,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,			)
           _UpperCAmelCase							:						str       							=					layer_group_output[0]
  
           return (hidden_states,)
  
  
  
  @add_start_docstrings(
      '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''	,a	,)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      str:
           super().__init__(A	)
  
           _UpperCAmelCase							:						Dict       							=					AlbertTransformerWithPabee(A	)
  
           self.init_weights()
           _UpperCAmelCase							:						str       							=					0
           _UpperCAmelCase							:						Union[str, Any]       							=					0
           _UpperCAmelCase							:						Dict       							=					0
  
           _UpperCAmelCase							:						str       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           _UpperCAmelCase							:						List[Any]       							=					threshold
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						int       							=					patience
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						Dict       							=					0
           _UpperCAmelCase							:						List[str]       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           _UpperCAmelCase							:						Union[str, Any]       							=					self.inference_layers_num / self.inference_instances_num
           _UpperCAmelCase							:						str       							=					(
               f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
               f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
           )
           print(A	)
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=False ,			)							->      int:
  
           if input_ids is not None and inputs_embeds is not None:
               raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''	)
           elif input_ids is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					input_ids.size()
           elif inputs_embeds is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					inputs_embeds.size()[:-1]
           else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)
  
           _UpperCAmelCase							:						Tuple       							=					input_ids.device if input_ids is not None else inputs_embeds.device
  
           if attention_mask is None:
               _UpperCAmelCase							:						Union[str, Any]       							=					torch.ones(A ,			device=A	)
           if token_type_ids is None:
               _UpperCAmelCase							:						Tuple       							=					torch.zeros(A ,			dtype=torch.long ,			device=A	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					attention_mask.unsqueeze(1	).unsqueeze(2	)
           _UpperCAmelCase							:						Union[str, Any]       							=					extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
           _UpperCAmelCase							:						Dict       							=					(1.0 - extended_attention_mask) * -10_000.0
           _UpperCAmelCase							:						Any       							=					self.get_head_mask(A ,			self.config.num_hidden_layers	)
  
           _UpperCAmelCase							:						int       							=					self.embeddings(
               A ,			position_ids=A ,			token_type_ids=A ,			inputs_embeds=A	)
           _UpperCAmelCase							:						Any       							=					embedding_output
  
           if self.training:
               _UpperCAmelCase							:						Optional[Any]       							=					[]
               for i in range(self.config.num_hidden_layers	):
                   _UpperCAmelCase							:						Union[str, Any]       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						List[str]       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Dict       							=					output_layers[i](output_dropout(A	)	)
                   res.append(A	)
           elif self.patience == 0:  # Use all layers for inference
               _UpperCAmelCase							:						Dict       							=					self.encoder(A ,			A ,			head_mask=A	)
               _UpperCAmelCase							:						Tuple       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
               _UpperCAmelCase							:						Optional[int]       							=					[output_layers[self.config.num_hidden_layers - 1](A	)]
           else:
               _UpperCAmelCase							:						List[Any]       							=					0
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						int       							=					0
               for i in range(self.config.num_hidden_layers	):
                   calculated_layer_num += 1
                   _UpperCAmelCase							:						Tuple       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						Any       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Optional[Any]       							=					output_layers[i](A	)
                   if regression:
                       _UpperCAmelCase							:						str       							=					logits.detach()
                       if patient_result is not None:
                           _UpperCAmelCase							:						str       							=					patient_result.detach()
                       if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						List[Any]       							=					0
                   else:
                       _UpperCAmelCase							:						List[str]       							=					logits.detach().argmax(dim=1	)
                       if patient_result is not None:
                           _UpperCAmelCase							:						Optional[Any]       							=					patient_result.detach().argmax(dim=1	)
                       if (patient_result is not None) and torch.all(labels.eq(A	)	):
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						Tuple       							=					0
  
                   _UpperCAmelCase							:						Any       							=					logits
                   if patient_counter == self.patience:
                       break
               _UpperCAmelCase							:						List[str]       							=					[patient_result]
               self.inference_layers_num += calculated_layer_num
               self.inference_instances_num += 1
  
           return res
  
  
  
  @add_start_docstrings(
      '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. '''	,a	,)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      Optional[int]:
           super().__init__(A	)
           _UpperCAmelCase							:						List[Any]       							=					config.num_labels
  
           _UpperCAmelCase							:						Optional[int]       							=					AlbertModelWithPabee(A	)
           _UpperCAmelCase							:						List[str]       							=					nn.Dropout(config.classifier_dropout_prob	)
           _UpperCAmelCase							:						Optional[Any]       							=					nn.ModuleList(
               [nn.Linear(config.hidden_size ,			self.config.num_labels	) for _ in range(config.num_hidden_layers	)]	)
  
           self.init_weights()
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      List[str]:
           _UpperCAmelCase							:						Optional[Any]       							=					self.albert(
               input_ids=A ,			attention_mask=A ,			token_type_ids=A ,			position_ids=A ,			head_mask=A ,			inputs_embeds=A ,			output_dropout=self.dropout ,			output_layers=self.classifiers ,			regression=self.num_labels == 1 ,			)
  
           _UpperCAmelCase							:						int       							=					(logits[-1],)
  
           if labels is not None:
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						Tuple       							=					0
               for ix, logits_item in enumerate(A	):
                   if self.num_labels == 1:
                       #  We are doing regression
                       _UpperCAmelCase							:						Dict       							=					MSELoss()
                       _UpperCAmelCase							:						Optional[int]       							=					loss_fct(logits_item.view(-1	) ,			labels.view(-1	)	)
                   else:
                       _UpperCAmelCase							:						List[str]       							=					CrossEntropyLoss()
                       _UpperCAmelCase							:						Any       							=					loss_fct(logits_item.view(-1 ,			self.num_labels	) ,			labels.view(-1	)	)
                   if total_loss is None:
                       _UpperCAmelCase							:						Optional[Any]       							=					loss
                   else:
                       total_loss += loss * (ix + 1)
                   total_weights += ix + 1
               _UpperCAmelCase							:						Any       							=					(total_loss / total_weights,) + outputs
  
           return outputs
  
  
  