


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


lowerCAmelCase__      		= logging.getLogger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "sequence-classification"


     def __init__(						self ,						__lowerCamelCase)			->				List[Any]:
            if type(__lowerCamelCase) == dict:
                   _A      :       Tuple				    = Namespace(**__lowerCamelCase)
            _A      :       Tuple				    = glue_output_modes[hparams.task]
            _A      :       Union[str, Any]				    = glue_tasks_num_labels[hparams.task]

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


     def  _lowerCamelCase (						self ,						**__lowerCamelCase)			->				List[str]:
            return self.model(**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				str:
            _A      :       List[Any]				    = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

            _A      :       Union[str, Any]				    = self(**__lowerCamelCase)
            _A      :       str				    = outputs[0]

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


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       Tuple				    = self.hparams
            _A      :       Dict				    = processors[args.task]()
            _A      :       str				    = processor.get_labels()

            for mode in ["train", "dev"]:
                   _A      :       Optional[int]				    = 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      :       Any				    = (
                              processor.get_dev_examples(args.data_dir)
                              if mode == "dev"
                              else processor.get_train_examples(args.data_dir)
                          )
                          _A      :       Union[str, Any]				    = 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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = False)			->				DataLoader:
            _A      :       Dict				    = "dev" if mode == "test" else mode

            _A      :       Optional[int]				    = self._feature_file(__lowerCamelCase)
            logger.info("Loading features from cached file %s" ,						__lowerCamelCase)
            _A      :       str				    = torch.load(__lowerCamelCase)
            _A      :       Union[str, Any]				    = torch.tensor([f.input_ids for f in features] ,						dtype=torch.long)
            _A      :       Dict				    = torch.tensor([f.attention_mask for f in features] ,						dtype=torch.long)
            _A      :       Tuple				    = torch.tensor([f.token_type_ids for f in features] ,						dtype=torch.long)
            if self.hparams.glue_output_mode == "classification":
                   _A      :       Optional[Any]				    = torch.tensor([f.label for f in features] ,						dtype=torch.long)
            elif self.hparams.glue_output_mode == "regression":
                   _A      :       int				    = 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 ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Dict				    = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

            _A      :       Union[str, Any]				    = self(**__lowerCamelCase)
            _A    ,			_A      :       Union[str, Any]				    = outputs[:2]
            _A      :       Any				    = logits.detach().cpu().numpy()
            _A      :       int				    = inputs["labels"].detach().cpu().numpy()

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


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

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

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

            _A      :       str				    = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task ,						__lowerCamelCase ,						__lowerCamelCase)}

            _A      :       List[Any]				    = dict(results.items())
            _A      :       str				    = results
            return ret, preds_list, out_label_list


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				dict:
            _A    ,			_A    ,			_A      :       Tuple				    = self._eval_end(__lowerCamelCase)
            _A      :       List[Any]				    = ret["log"]
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				dict:
            _A    ,			_A    ,			_A      :       int				    = self._eval_end(__lowerCamelCase)
            _A      :       Any				    = 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 ,						__lowerCamelCase)			->				List[str]:
            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 							_UpperCAmelCase				():
       _A      :       Optional[Any]				    = argparse.ArgumentParser()
       add_generic_args(UpperCamelCase__				,							os.getcwd()						)
       _A      :       Optional[int]				    = GLUETransformer.add_model_specific_args(UpperCamelCase__				,							os.getcwd()						)
       _A      :       List[str]				    = parser.parse_args()

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

       _A      :       Union[str, Any]				    = GLUETransformer(UpperCamelCase__						)
       _A      :       Optional[int]				    = generic_train(UpperCamelCase__				,							UpperCamelCase__						)

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


if __name__ == "__main__":
    main()




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase__      		= object()

# For specifying empty leaf dict `{}`
lowerCAmelCase__      		= object()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       str				    = tuple((re.compile(x + "$"						) for x in qs)						)
       for i in range(len(UpperCamelCase__						) - len(UpperCamelCase__						) + 1						):
              _A      :       Tuple				    = [x.match(UpperCamelCase__						) for x, y in zip(UpperCamelCase__				,							ks[i:]						)]
              if matches and all(UpperCamelCase__						):
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       def replace(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
              for rule, replacement in rules:
                     if _match(UpperCamelCase__				,							UpperCamelCase__						):
                            return replacement
              return val

       return replace
def 							_UpperCAmelCase				():
       return [
           # embeddings
           (("transformer", "wpe", "embedding"), P("mp"				,							UpperCamelCase__						)),
           (("transformer", "wte", "embedding"), P("mp"				,							UpperCamelCase__						)),
           # atention
           (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("attention", "out_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("attention", "out_proj", "bias"), None),
           # mlp
           (("mlp", "c_fc", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("mlp", "c_fc", "bias"), P("mp"						)),
           (("mlp", "c_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("mlp", "c_proj", "bias"), None),
           # layer norms
           ((r"ln_\d+", "bias"), None),
           ((r"\d+", r"ln_\d+", "scale"), None),
           (("ln_f", "bias"), None),
           (("ln_f", "scale"), None),
       ]






def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       int				    = _get_partition_rules()
       _A      :       Optional[int]				    = _replacement_rules(UpperCamelCase__						)
       _A      :       Optional[int]				    = {k: _unmatched for k in flatten_dict(UpperCamelCase__						)}
       _A      :       List[str]				    = {k: replace(UpperCamelCase__				,							UpperCamelCase__						) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(UpperCamelCase__						)						)
