


'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


a__  :					Any        =       logging.get_logger(__name__)

a__  :					List[str]        =       {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'longformer'


    def __init__(     self  ,					a = 5_12  ,					a = 2  ,					a = 1  ,					a = 0  ,					a = 2  ,					a = 3_05_22  ,					a = 7_68  ,					a = 12  ,					a = 12  ,					a = 30_72  ,					a = "gelu"  ,					a = 0.1  ,					a = 0.1  ,					a = 5_12  ,					a = 2  ,					a = 0.02  ,					a = 1e-12  ,					a = False  ,					**a  ,					):
          super().__init__(pad_token_id=a  ,					**a    )

          UpperCamelCase__										=				attention_window
          UpperCamelCase__										=				sep_token_id
          UpperCamelCase__										=				bos_token_id
          UpperCamelCase__										=				eos_token_id
          UpperCamelCase__										=				vocab_size
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				type_vocab_size
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				layer_norm_eps
          UpperCamelCase__										=				onnx_export

class 					lowercase_     (    a__   ):


    def __init__(     self  ,					a  ,					a = "default"  ,					a = None    ):
          super().__init__(a  ,					a  ,					a    )
          UpperCamelCase__										=				True


    @property
    def    __a							(     self    ):
          if self.task == "multiple-choice":
                UpperCamelCase__										=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                UpperCamelCase__										=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("global_attention_mask", dynamic_axis),
              ]    )


    @property
    def    __a							(     self    ):
          UpperCamelCase__										=				super().outputs
          if self.task == "default":
                UpperCamelCase__										=				{0: "batch"}
          return outputs


    @property
    def    __a							(     self    ):
          return 1e-4


    @property
    def    __a							(     self    ):
          # needs to be >= 14 to support tril operator
          return max(super().default_onnx_opset  ,					14    )


    def    __a							(     self  ,					a  ,					a = -1  ,					a = -1  ,					a = False  ,					a = None  ,					):
          UpperCamelCase__										=				super().generate_dummy_inputs(
              preprocessor=a  ,					batch_size=a  ,					seq_length=a  ,					is_pair=a  ,					framework=a    )
          import torch

          # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
          # makes the export fail randomly
          UpperCamelCase__										=				torch.zeros_like(inputs["input_ids"]    )
          # make every second token global
          UpperCamelCase__										=				1

          return inputs












import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a__:      List[str]									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''sequence-classification'''



 def __init__(							self,__lowerCamelCase ):
        if type(__lowerCamelCase ) == dict:
               A__											=						Namespace(**__lowerCamelCase )
        A__											=						glue_output_modes[hparams.task]
        A__											=						glue_tasks_num_labels[hparams.task]

        super().__init__(__lowerCamelCase,__lowerCamelCase,self.mode )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return self.model(**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__											=						outputs[0]

        A__											=						self.trainer.lr_schedulers[0]['''scheduler''']
        A__											=						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss, "log": tensorboard_logs}



 def 				UpperCamelCase			(							self ):
        A__											=						self.hparams
        A__											=						processors[args.task]()
        A__											=						processor.get_labels()

        for mode in ["train", "dev"]:
               A__											=						self._feature_file(__lowerCamelCase )
               if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                      logger.info('''Loading features from cached file %s''',__lowerCamelCase )
               else:
                      logger.info('''Creating features from dataset file at %s''',args.data_dir )
                      A__											=						(
                          processor.get_dev_examples(args.data_dir )
                          if mode == '''dev'''
                          else processor.get_train_examples(args.data_dir )
                      )
                      A__											=						convert_examples_to_features(
                          __lowerCamelCase,self.tokenizer,max_length=args.max_seq_length,label_list=self.labels,output_mode=args.glue_output_mode,)
                      logger.info('''Saving features into cached file %s''',__lowerCamelCase )
                      torch.save(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = False ):
        A__											=						'''dev''' if mode == '''test''' else mode

        A__											=						self._feature_file(__lowerCamelCase )
        logger.info('''Loading features from cached file %s''',__lowerCamelCase )
        A__											=						torch.load(__lowerCamelCase )
        A__											=						torch.tensor([f.input_ids for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.attention_mask for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.token_type_ids for f in features],dtype=torch.long )
        if self.hparams.glue_output_mode == "classification":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.long )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.float )

        return DataLoader(
            TensorDataset(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ),batch_size=__lowerCamelCase,shuffle=__lowerCamelCase,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__					,	A__											=						outputs[:2]
        A__											=						logits.detach().cpu().numpy()
        A__											=						inputs['''labels'''].detach().cpu().numpy()

        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
        A__											=						np.concatenate([x['''pred'''] for x in outputs],axis=0 )

        if self.hparams.glue_output_mode == "classification":
               A__											=						np.argmax(__lowerCamelCase,axis=1 )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						np.squeeze(__lowerCamelCase )

        A__											=						np.concatenate([x['''target'''] for x in outputs],axis=0 )
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]

        A__											=						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,__lowerCamelCase,__lowerCamelCase )}

        A__											=						dict(results.items() )
        A__											=						results
        return ret, preds_list, out_label_list



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




 @staticmethod
 def 				UpperCamelCase			(							__lowerCamelCase,__lowerCamelCase ):
        BaseTransformer.add_model_specific_args(__lowerCamelCase,__lowerCamelCase )
        parser.add_argument(
            '''--max_seq_length''',default=128,type=__lowerCamelCase,help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            ),)

        parser.add_argument(
            '''--task''',default='''''',type=__lowerCamelCase,required=__lowerCamelCase,help='''The GLUE task to run''',)
        parser.add_argument(
            '''--gpus''',default=0,type=__lowerCamelCase,help='''The number of GPUs allocated for this, it is by default 0 meaning none''',)

        parser.add_argument(
            '''--overwrite_cache''',action='''store_true''',help='''Overwrite the cached training and evaluation sets''' )

        return parser


def 	UpperCamelCase__(       )->Any:
       A__											=						argparse.ArgumentParser()
       add_generic_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						GLUETransformer.add_model_specific_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              A__											=						os.path.join(
                  '''./results''' ,     f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'  )}" ,     )
              os.makedirs(args.output_dir  )

       A__											=						GLUETransformer(UpperCamelCase__  )
       A__											=						generic_train(UpperCamelCase__ ,     UpperCamelCase__  )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              A__											=						sorted(glob.glob(os.path.join(args.output_dir ,     '''checkpoint-epoch=*.ckpt'''  ) ,     recursive=UpperCamelCase__  )  )
              A__											=						model.load_from_checkpoint(checkpoints[-1]  )
              return trainer.test(UpperCamelCase__  )


if __name__ == "__main__":
       main()





