'''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


UpperCamelCase__     :	Tuple				=			logging.getLogger(__name__)


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      int      							=   '''sequence-classification'''




      def __init__(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         if type(lowerCamelCase_							) == dict:
            UpperCAmelCase__							:				Dict   				=  Namespace(**lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  glue_output_modes[hparams.task]
         UpperCAmelCase__							:				List[Any]   				=  glue_tasks_num_labels[hparams.task]

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




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.model(**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

         UpperCAmelCase__							:				Union[str, Any]   				=  self(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  outputs[0]

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




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.hparams
         UpperCAmelCase__							:				int   				=  processors[args.task]()
         UpperCAmelCase__							:				Dict   				=  processor.get_labels()

         for mode in ["train", "dev"]:
            UpperCAmelCase__							:				Dict   				=  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							)
               UpperCAmelCase__							:				List[str]   				=  (
                   processor.get_dev_examples(args.data_dir							)
                   if mode == '''dev'''
                   else processor.get_train_examples(args.data_dir							)
               )
               UpperCAmelCase__							:				Dict   				=  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:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  '''dev''' if mode == '''test''' else mode

         UpperCAmelCase__							:				Union[str, Any]   				=  self._feature_file(lowerCamelCase_							)
         logger.info('''Loading features from cached file %s'''					,lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  torch.tensor([f.input_ids for f in features]					,dtype=torch.long							)
         UpperCAmelCase__							:				Any   				=  torch.tensor([f.attention_mask for f in features]					,dtype=torch.long							)
         UpperCAmelCase__							:				Any   				=  torch.tensor([f.token_type_ids for f in features]					,dtype=torch.long							)
         if self.hparams.glue_output_mode == "classification":
            UpperCAmelCase__							:				Any   				=  torch.tensor([f.label for f in features]					,dtype=torch.long							)
         elif self.hparams.glue_output_mode == "regression":
            UpperCAmelCase__							:				List[Any]   				=  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_							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

         UpperCAmelCase__							:				Any   				=  self(**lowerCamelCase_							)
         UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  outputs[:2]
         UpperCAmelCase__							:				Any   				=  logits.detach().cpu().numpy()
         UpperCAmelCase__							:				List[Any]   				=  inputs['''labels'''].detach().cpu().numpy()

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  torch.stack([x['''val_loss'''] for x in outputs]							).mean().detach().cpu().item()
         UpperCAmelCase__							:				Union[str, Any]   				=  np.concatenate([x['''pred'''] for x in outputs]					,axis=0							)

         if self.hparams.glue_output_mode == "classification":
            UpperCAmelCase__							:				Optional[Any]   				=  np.argmax(lowerCamelCase_					,axis=1							)
         elif self.hparams.glue_output_mode == "regression":
            UpperCAmelCase__							:				int   				=  np.squeeze(lowerCamelCase_							)

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

         UpperCAmelCase__							:				List[Any]   				=  {**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task					,lowerCamelCase_					,lowerCamelCase_							)}

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			dict:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Any   				=  self._eval_end(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  ret['''log''']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			dict:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  self._eval_end(lowerCamelCase_							)
         UpperCAmelCase__							:				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_							)			->			Optional[Any]:



         '''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 __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  argparse.ArgumentParser()
   add_generic_args(_A  ,			os.getcwd()    )
   UpperCAmelCase__							:				str   				=  GLUETransformer.add_model_specific_args(_A  ,			os.getcwd()    )
   UpperCAmelCase__							:				int   				=  parser.parse_args()

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

   UpperCAmelCase__							:				str   				=  GLUETransformer(_A    )
   UpperCAmelCase__							:				List[Any]   				=  generic_train(_A  ,			_A    )

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


if __name__ == "__main__":
  main()





'''simple docstring'''

from typing import List, Optional, Union

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


UpperCamelCase__     :	List[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   '''time_series_transformer'''
      UpperCAmelCase_      :      List[Any]      							=   {
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }




      def __init__(				self					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = "student_t"					,lowerCamelCase_ = "nll"					,lowerCamelCase_ = 1					,lowerCamelCase_ = [1, 2, 3, 4, 5, 6, 7]					,lowerCamelCase_ = "mean"					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = 32					,lowerCamelCase_ = 32					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = True					,lowerCamelCase_ = "gelu"					,lowerCamelCase_ = 64					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 100					,lowerCamelCase_ = 0.02					,lowerCamelCase_=True					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  prediction_length
         UpperCAmelCase__							:				Optional[Any]   				=  context_length or prediction_length
         UpperCAmelCase__							:				Union[str, Any]   				=  distribution_output
         UpperCAmelCase__							:				List[Any]   				=  loss
         UpperCAmelCase__							:				Optional[int]   				=  input_size
         UpperCAmelCase__							:				Tuple   				=  num_time_features
         UpperCAmelCase__							:				str   				=  lags_sequence
         UpperCAmelCase__							:				Union[str, Any]   				=  scaling
         UpperCAmelCase__							:				Optional[int]   				=  num_dynamic_real_features
         UpperCAmelCase__							:				Optional[Any]   				=  num_static_real_features
         UpperCAmelCase__							:				str   				=  num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
            if len(lowerCamelCase_							) != num_static_categorical_features:
               raise ValueError(
                   '''The cardinality should be a list of the same length as `num_static_categorical_features`'''							)
            UpperCAmelCase__							:				Union[str, Any]   				=  cardinality
         else:
            UpperCAmelCase__							:				List[str]   				=  [0]
         if embedding_dimension and num_static_categorical_features > 0:
            if len(lowerCamelCase_							) != num_static_categorical_features:
               raise ValueError(
                   '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''							)
            UpperCAmelCase__							:				int   				=  embedding_dimension
         else:
            UpperCAmelCase__							:				Union[str, Any]   				=  [min(50					,(cat + 1) // 2							) for cat in self.cardinality]
         UpperCAmelCase__							:				Dict   				=  num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase__							:				int   				=  input_size * len(lowerCamelCase_							) + self._number_of_features
         UpperCAmelCase__							:				List[Any]   				=  d_model
         UpperCAmelCase__							:				Optional[Any]   				=  encoder_attention_heads
         UpperCAmelCase__							:				Optional[int]   				=  decoder_attention_heads
         UpperCAmelCase__							:				Union[str, Any]   				=  encoder_ffn_dim
         UpperCAmelCase__							:				Union[str, Any]   				=  decoder_ffn_dim
         UpperCAmelCase__							:				Tuple   				=  encoder_layers
         UpperCAmelCase__							:				Optional[Any]   				=  decoder_layers

         UpperCAmelCase__							:				Optional[Any]   				=  dropout
         UpperCAmelCase__							:				Any   				=  attention_dropout
         UpperCAmelCase__							:				Optional[int]   				=  activation_dropout
         UpperCAmelCase__							:				List[Any]   				=  encoder_layerdrop
         UpperCAmelCase__							:				Optional[int]   				=  decoder_layerdrop

         UpperCAmelCase__							:				List[Any]   				=  activation_function
         UpperCAmelCase__							:				List[Any]   				=  init_std

         UpperCAmelCase__							:				Optional[Any]   				=  use_cache

         super().__init__(is_encoder_decoder=lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return (
             sum(self.embedding_dimension							)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )




