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


snake_case__				= logging.getLogger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  'sequence-classification'



     def __init__(					self				,				A_						) ->				str:






           """simple docstring"""
           if type(A_						) == dict:
                 _lowerCamelCase			    =				Namespace(**A_						)
           _lowerCamelCase			    =				glue_output_modes[hparams.task]
           _lowerCamelCase			    =				glue_tasks_num_labels[hparams.task]

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



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Optional[int]:






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



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[str]:






           """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(**A_						)
           _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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






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

           for mode in ["train", "dev"]:
                 _lowerCamelCase			    =				self._feature_file(A_						)
                 if os.path.exists(A_						) and not args.overwrite_cache:
                       logger.info('''Loading features from cached file %s'''				,				A_						)
                 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(
                           A_				,				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'''				,				A_						)
                       torch.save(A_				,				A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = False						) ->				DataLoader:






           """simple docstring"""
           _lowerCamelCase			    =				'''dev''' if mode == '''test''' else mode

           _lowerCamelCase			    =				self._feature_file(A_						)
           logger.info('''Loading features from cached file %s'''				,				A_						)
           _lowerCamelCase			    =				torch.load(A_						)
           _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(A_				,				A_				,				A_				,				A_						)				,				batch_size=A_				,				shuffle=A_				,				)



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[Any]:






           """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(**A_						)
           _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 		UpperCamelCase_						(					self				,				A_						) ->				tuple:






           """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(A_				,				axis=1						)
           elif self.hparams.glue_output_mode == "regression":
                 _lowerCamelCase			    =				np.squeeze(A_						)

           _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				,				A_				,				A_						)}

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



     def 		UpperCamelCase_						(					self				,				A_						) ->				dict:






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



     def 		UpperCamelCase_						(					self				,				A_						) ->				dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self._eval_end(A_						)
           _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 		UpperCamelCase_						(					A_				,				A_						) ->				str:






           """simple docstring"""
           BaseTransformer.add_model_specific_args(A_				,				A_						)
           parser.add_argument(
               '''--max_seq_length'''				,				default=1_28				,				type=A_				,				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=A_				,				required=A_				,				help='''The GLUE task to run'''				,				)
           parser.add_argument(
               '''--gpus'''				,				default=0				,				type=A_				,				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       __magic_name__(  )						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()
      add_generic_args(__UpperCAmelCase ,						os.getcwd()		)
      _lowerCamelCase			    =				GLUETransformer.add_model_specific_args(__UpperCAmelCase ,						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(__UpperCAmelCase		)
      _lowerCamelCase			    =				generic_train(__UpperCAmelCase ,						__UpperCAmelCase		)

      # 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=__UpperCAmelCase		)		)
            _lowerCamelCase			    =				model.load_from_checkpoint(checkpoints[-1]		)
            return trainer.test(__UpperCAmelCase		)


if __name__ == "__main__":
     main()
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)