


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


SCREAMING_SNAKE_CASE						:Any			=  logging.getLogger(__name__)


class      __lowerCAmelCase  (			_a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'sequence-classification'



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[int]:




     """simple docstring"""


     if type(lowerCAmelCase_	) == dict:
        snake_case_            =       Namespace(**lowerCAmelCase_	)
     snake_case_            =       glue_output_modes[hparams.task]
     snake_case_            =       glue_tasks_num_labels[hparams.task]

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



  def        lowerCAmelCase__	(  self							:							Any     ,					**_lowerCAmelCase							:							Any	) ->			int:




     """simple docstring"""
     return self.model(**lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

     snake_case_            =       self(**lowerCAmelCase_	)
     snake_case_            =       outputs[0]

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



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       self.hparams
     snake_case_            =       processors[args.task]()
     snake_case_            =       processor.get_labels()

     for mode in ["train", "dev"]:
        snake_case_            =       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	)
           snake_case_            =       (
               processor.get_dev_examples(args.data_dir	)
               if mode == "dev"
               else processor.get_train_examples(args.data_dir	)
           )
           snake_case_            =       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        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							bool = False	) ->			DataLoader:




     """simple docstring"""
     snake_case_            =       "dev" if mode == "test" else mode

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

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



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Any	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

     snake_case_            =       self(**lowerCAmelCase_	)
     snake_case_     ,     snake_case_            =       outputs[:2]
     snake_case_            =       logits.detach().cpu().numpy()
     snake_case_            =       inputs["labels"].detach().cpu().numpy()

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



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Any	) ->			tuple:




     """simple docstring"""
     snake_case_            =       torch.stack([x["val_loss"] for x in outputs]	).mean().detach().cpu().item()
     snake_case_            =       np.concatenate([x["pred"] for x in outputs]     ,					axis=0	)

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

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

     snake_case_            =       {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task     ,					lowerCAmelCase_     ,					lowerCAmelCase_	)}

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



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							list	) ->			dict:




     """simple docstring"""
     snake_case_     ,     snake_case_     ,     snake_case_            =       self._eval_end(lowerCAmelCase_	)
     snake_case_            =       ret["log"]
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							str	) ->			dict:




     """simple docstring"""
     snake_case_     ,     snake_case_     ,     snake_case_            =       self._eval_end(lowerCAmelCase_	)
     snake_case_            =       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        lowerCAmelCase__	(  _lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Dict	) ->			List[Any]:




     """simple docstring"""
     BaseTransformer.add_model_specific_args(lowerCAmelCase_     ,					lowerCAmelCase_	)
     parser.add_argument(
         "--max_seq_length"     ,					default=1_2_8     ,					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       _lowerCAmelCase    (				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       argparse.ArgumentParser()
   add_generic_args(lowerCAmelCase_		,    os.getcwd()				)
   snake_case_            =       GLUETransformer.add_model_specific_args(lowerCAmelCase_		,    os.getcwd()				)
   snake_case_            =       parser.parse_args()

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

   snake_case_            =       GLUETransformer(lowerCAmelCase_				)
   snake_case_            =       generic_train(lowerCAmelCase_		,    lowerCAmelCase_				)

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


if __name__ == "__main__":
   main()










'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														if self.task == "multiple-choice":
																					_a      	=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					_a      	=							{0: '''batch''', 1: '''sequence'''}
														_a      	=							{0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														        ('''token_type_ids''', dynamic_axis),
														    ]   )

