




'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormModel,
     )






class 		lowerCAmelCase_     (    unittest.TestCase				):







  '''simple docstring'''

  def __init__(      self							:				List[str] ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any] ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=13 ,   SCREAMING_SNAKE_CASE_							:				Tuple=7 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=True ,   SCREAMING_SNAKE_CASE_							:				int=True ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=True ,   SCREAMING_SNAKE_CASE_							:				Any=True ,   SCREAMING_SNAKE_CASE_							:				int=99 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]=32 ,   SCREAMING_SNAKE_CASE_							:				Dict=5 ,   SCREAMING_SNAKE_CASE_							:				Dict=4 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=37 ,   SCREAMING_SNAKE_CASE_							:				str="gelu" ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=0.1 ,   SCREAMING_SNAKE_CASE_							:				List[Any]=0.1 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=5_12 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=16 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=2 ,   SCREAMING_SNAKE_CASE_							:				Any=0.02 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=4 ,   )       ->						List[Any]:







       '''simple docstring'''



       A:						Union[str, Any]						=						parent
       A:						Any						=						batch_size
       A:						Dict						=						seq_length
       A:						Optional[int]						=						is_training
       A:						int						=						use_attention_mask
       A:						Union[str, Any]						=						use_token_type_ids
       A:						List[str]						=						use_labels
       A:						Optional[Any]						=						vocab_size
       A:						int						=						hidden_size
       A:						Tuple						=						num_hidden_layers
       A:						List[str]						=						num_attention_heads
       A:						List[str]						=						intermediate_size
       A:						str						=						hidden_act
       A:						Any						=						hidden_dropout_prob
       A:						Optional[int]						=						attention_probs_dropout_prob
       A:						List[str]						=						max_position_embeddings
       A:						Optional[Any]						=						type_vocab_size
       A:						Tuple						=						type_sequence_label_size
       A:						Any						=						initializer_range
       A:						Optional[Any]						=						num_choices



  def  _snake_case			(      self							:				str     )       ->						Dict:







       '''simple docstring'''



       A:						str						=						ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size     )

       A:						str						=						None
       if self.use_attention_mask:
            A:						Optional[int]						=						random_attention_mask([self.batch_size, self.seq_length]     )

       A:						Optional[Any]						=						None
       if self.use_token_type_ids:
            A:						int						=						ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size     )

       A:						Optional[int]						=						RobertaPreLayerNormConfig(
           vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   type_vocab_size=self.type_vocab_size ,   is_decoder=UpperCamelCase__ ,   initializer_range=self.initializer_range ,   )

       return config, input_ids, token_type_ids, attention_mask



  def  _snake_case			(      self							:				int     )       ->						Tuple:







       '''simple docstring'''



       A:						Optional[int]						=						self.prepare_config_and_inputs()
       A:						int						=						config_and_inputs
       A:						Any						=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict




  def  _snake_case			(      self							:				Dict     )       ->						List[Any]:







       '''simple docstring'''



       A:						str						=						self.prepare_config_and_inputs()
       A:						Tuple						=						config_and_inputs

       A:						List[Any]						=						True
       A:						str						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
       A:						int						=						ids_tensor([self.batch_size, self.seq_length] ,   vocab_size=2     )

       return (
           config,
           input_ids,
           token_type_ids,
           encoder_hidden_states,
           encoder_attention_mask,
       )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 		lowerCAmelCase_     (    lowerCAmelCase__    , unittest.TestCase				):







  '''simple docstring'''

  UpperCamelCase_       :	str				       =							True

  UpperCamelCase_       :	List[Any]				       =							(
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )



  def  _snake_case			(      self							:				List[str]     )       ->						Union[str, Any]:







       '''simple docstring'''



       A:						Optional[int]						=						FlaxRobertaPreLayerNormModelTester(self     )




  @slow
  def  _snake_case			(      self							:				Dict     )       ->						List[Any]:







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
            A:						Dict						=						model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,   from_pt=UpperCamelCase__     )
            A:						Dict						=						model(np.ones((1, 1)     )     )
            self.assertIsNotNone(UpperCamelCase__     )






@require_flax
class 		lowerCAmelCase_     (    unittest.TestCase				):







  '''simple docstring'''

  @slow
  def  _snake_case			(      self							:				Tuple     )       ->						Any:







       '''simple docstring'''



       A:						Optional[int]						=						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,   from_pt=UpperCamelCase__     )

       A:						Tuple						=						np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] ,   dtype=jnp.intaa     )
       A:						List[str]						=						model(UpperCamelCase__     )[0]
       A:						List[str]						=						[1, 11, 5_02_65]
       self.assertEqual(list(output.shape     ) ,   UpperCamelCase__     )
       # compare the actual values for a slice.
       A:						Dict						=						np.array(
           [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] ,   dtype=np.floataa     )
       self.assertTrue(np.allclose(output[:, :3, :3] ,   UpperCamelCase__ ,   atol=1E-4     )     )




  @slow
  def  _snake_case			(      self							:				Dict     )       ->						Optional[int]:







       '''simple docstring'''



       A:						List[str]						=						FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,   from_pt=UpperCamelCase__     )

       A:						Optional[int]						=						np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] ,   dtype=jnp.intaa     )
       A:						List[str]						=						model(UpperCamelCase__     )[0]
       # compare the actual values for a slice.
       A:						Dict						=						np.array(
           [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] ,   dtype=np.floataa     )
       self.assertTrue(np.allclose(output[:, :3, :3] ,   UpperCamelCase__ ,   atol=1E-4     )     )












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__       :      Dict  =   logging.getLogger(__name__)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      """sequence-classification"""







    def __init__(    self     ,  UpperCamelCase__     )     ->      List[Any]:
           if type(UpperCamelCase__     ) == dict:
                  lowerCamelCase   :      int			 =					Namespace(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					glue_output_modes[hparams.task]
           lowerCamelCase   :      int			 =					glue_tasks_num_labels[hparams.task]

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







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Tuple:
           return self.model(**UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase   :      Union[str, Any]			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

           lowerCamelCase   :      Optional[int]			 =					self(**UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					outputs[0]

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







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Any			 =					self.hparams
           lowerCamelCase   :      Union[str, Any]			 =					processors[args.task]()
           lowerCamelCase   :      Optional[int]			 =					processor.get_labels()

           for mode in ["train", "dev"]:
                  lowerCamelCase   :      Optional[Any]			 =					self._feature_file(UpperCamelCase__     )
                  if os.path.exists(UpperCamelCase__     ) and not args.overwrite_cache:
                         logger.info("Loading features from cached file %s"     ,  UpperCamelCase__     )
                  else:
                         logger.info("Creating features from dataset file at %s"     ,  args.data_dir     )
                         lowerCamelCase   :      List[str]			 =					(
                             processor.get_dev_examples(args.data_dir     )
                             if mode == "dev"
                             else processor.get_train_examples(args.data_dir     )
                         )
                         lowerCamelCase   :      Dict			 =					convert_examples_to_features(
                             UpperCamelCase__     ,  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"     ,  UpperCamelCase__     )
                         torch.save(UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     )     ->      DataLoader:
           lowerCamelCase   :      str			 =					"dev" if mode == "test" else mode

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

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      Dict			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

           lowerCamelCase   :      Dict			 =					self(**UpperCamelCase__     )
           lowerCamelCase ,      lowerCamelCase   :      Any			 =					outputs[:2]
           lowerCamelCase   :      Union[str, Any]			 =					logits.detach().cpu().numpy()
           lowerCamelCase   :      Optional[Any]			 =					inputs["labels"].detach().cpu().numpy()

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







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      tuple:
           lowerCamelCase   :      Union[str, Any]			 =					torch.stack([x["val_loss"] for x in outputs]     ).mean().detach().cpu().item()
           lowerCamelCase   :      Optional[int]			 =					np.concatenate([x["pred"] for x in outputs]     ,  axis=0     )

           if self.hparams.glue_output_mode == "classification":
                  lowerCamelCase   :      Union[str, Any]			 =					np.argmax(UpperCamelCase__     ,  axis=1     )
           elif self.hparams.glue_output_mode == "regression":
                  lowerCamelCase   :      str			 =					np.squeeze(UpperCamelCase__     )

           lowerCamelCase   :      List[Any]			 =					np.concatenate([x["target"] for x in outputs]     ,  axis=0     )
           lowerCamelCase   :      List[str]			 =					[[] for _ in range(out_label_ids.shape[0]     )]
           lowerCamelCase   :      Optional[int]			 =					[[] for _ in range(out_label_ids.shape[0]     )]

           lowerCamelCase   :      Dict			 =					{**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task     ,  UpperCamelCase__     ,  UpperCamelCase__     )}

           lowerCamelCase   :      List[str]			 =					dict(results.items()     )
           lowerCamelCase   :      Optional[int]			 =					results
           return ret, preds_list, out_label_list







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					ret["log"]
           return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					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     _lowercase     (    UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           BaseTransformer.add_model_specific_args(UpperCamelCase__     ,  UpperCamelCase__     )
           parser.add_argument(
               "--max_seq_length"     ,  default=128     ,  type=UpperCamelCase__     ,  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=UpperCamelCase__     ,  required=UpperCamelCase__     ,  help="The GLUE task to run"     ,  )
           parser.add_argument(
               "--gpus"     ,  default=0     ,  type=UpperCamelCase__     ,  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      A      (      )	-> int:
       lowerCamelCase   :      int			 =					argparse.ArgumentParser()
       add_generic_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					GLUETransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					parser.parse_args()

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

       lowerCamelCase   :      int			 =					GLUETransformer(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					generic_train(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
       main()






