



"""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	)







"""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


