'''simple docstring'''




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  {
    '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       '''mvp'''
			UpperCAmelCase_			:     Any      =       ['''past_key_values''']
			UpperCAmelCase_			:     Tuple      =       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



			def __init__(   self       ,    __lowerCAmelCase=50267       ,    __lowerCAmelCase=1024       ,    __lowerCAmelCase=12       ,    __lowerCAmelCase=4096       ,    __lowerCAmelCase=16       ,    __lowerCAmelCase=12       ,    __lowerCAmelCase=4096       ,    __lowerCAmelCase=16       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase="gelu"       ,    __lowerCAmelCase=1024       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=0.02       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=0       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=100       ,    __lowerCAmelCase=800       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  vocab_size
					lowerCAmelCase   					=  max_position_embeddings
					lowerCAmelCase   					=  d_model
					lowerCAmelCase   					=  encoder_ffn_dim
					lowerCAmelCase   					=  encoder_layers
					lowerCAmelCase   					=  encoder_attention_heads
					lowerCAmelCase   					=  decoder_ffn_dim
					lowerCAmelCase   					=  decoder_layers
					lowerCAmelCase   					=  decoder_attention_heads
					lowerCAmelCase   					=  dropout
					lowerCAmelCase   					=  attention_dropout
					lowerCAmelCase   					=  activation_dropout
					lowerCAmelCase   					=  activation_function
					lowerCAmelCase   					=  init_std
					lowerCAmelCase   					=  encoder_layerdrop
					lowerCAmelCase   					=  decoder_layerdrop
					lowerCAmelCase   					=  classifier_dropout
					lowerCAmelCase   					=  use_cache
					lowerCAmelCase   					=  encoder_layers
					lowerCAmelCase   					=  scale_embedding  # scale factor will be sqrt(d_model) if True
					lowerCAmelCase   					=  use_prompt
					lowerCAmelCase   					=  prompt_length
					lowerCAmelCase   					=  prompt_mid_dim

					super().__init__(
					    pad_token_id=__lowerCAmelCase       ,    bos_token_id=__lowerCAmelCase       ,    eos_token_id=__lowerCAmelCase       ,    is_encoder_decoder=__lowerCAmelCase       ,    decoder_start_token_id=__lowerCAmelCase       ,    forced_eos_token_id=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )

					if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  self.bos_token_id
							warnings.warn(
							    f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
							    """The config can simply be saved and uploaded again to be fixed.""")





'''simple docstring'''




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


__lowercase          =  logging.getLogger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Any      =       '''sequence-classification'''



			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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

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



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.model(**__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase   					=  outputs[0]

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.hparams
					lowerCAmelCase   					=  processors[args.task]()
					lowerCAmelCase   					=  processor.get_labels()

					for mode in ["train", "dev"]:
							lowerCAmelCase   					=  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)
									lowerCAmelCase   					=  (
									    processor.get_dev_examples(args.data_dir)
									    if mode == """dev"""
									    else processor.get_train_examples(args.data_dir)
									)
									lowerCAmelCase   					=  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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = False):




					"""simple docstring"""


					lowerCAmelCase   					=  """dev""" if mode == """test""" else mode

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

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

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

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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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

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

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

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

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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  ret["""log"""]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					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 				snake_case__							(			)    ->	Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  argparse.ArgumentParser()
		add_generic_args(_A      ,   os.getcwd() )
		lowerCAmelCase   					=  GLUETransformer.add_model_specific_args(_A      ,   os.getcwd() )
		lowerCAmelCase   					=  parser.parse_args()

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

		lowerCAmelCase   					=  GLUETransformer(_A )
		lowerCAmelCase   					=  generic_train(_A      ,   _A )

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


if __name__ == "__main__":
						main()




