



'''simple docstring'''





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase_			=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase_			=  {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase_			=  {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase_			=  sorted(arg_to_scheduler.keys())
lowercase_			=  "{" + ", ".join(arg_to_scheduler_choices) + "}"






class __A      (  pl.LightningModule						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=None			,       A="base"			,       A=None			,       A=None			,       A=None			,       **A			,       )   ->					int:


      """simple docstring"""



      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(A	)
      _a     =  0
      _a     =  Path(self.hparams.output_dir	)
      _a     =  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           _a     =  AutoConfig.from_pretrained(
               self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,       **({'''num_labels''': num_labels} if num_labels is not None else {})			,       cache_dir=A			,       **A			,       )
      else:
           _a     =  config

      _a     =  ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
           if getattr(self.hparams			,       A			,       A	):
                assert hasattr(self.config			,       A	), f'''model config doesn\'t have a `{p}` attribute'''
                setattr(self.config			,       A			,       getattr(self.hparams			,       A	)	)

      if tokenizer is None:
           _a     =  AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,       cache_dir=A			,       )
      else:
           _a     =  tokenizer
      _a     =  MODEL_MODES[mode]
      if model is None:
           _a     =  self.model_type.from_pretrained(
               self.hparams.model_name_or_path			,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path	)			,       config=self.config			,       cache_dir=A			,       )
      else:
           _a     =  model



 def  a__  (self			,       *A			,       **A	)   ->					int:


      """simple docstring"""



      _a     =  self.model_type.from_pretrained(*A			,       **A	)



 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  arg_to_scheduler[self.hparams.lr_scheduler]
      _a     =  get_schedule_func(
          self.opt			,       num_warmup_steps=self.hparams.warmup_steps			,       num_training_steps=self.total_steps()	)
      _a     =  {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  self.model
      _a     =  ['''bias''', '''LayerNorm.weight''']
      _a     =  [
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
              '''weight_decay''': self.hparams.weight_decay,
          },
          {
              '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
              '''weight_decay''': 0.0,
          },
      ]
      if self.hparams.adafactor:
           _a     =  Adafactor(
               A			,       lr=self.hparams.learning_rate			,       scale_parameter=A			,       relative_step=A	)

      else:
           _a     =  AdamW(
               A			,       lr=self.hparams.learning_rate			,       eps=self.hparams.adam_epsilon	)
      _a     =  optimizer

      _a     =  self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def  a__  (self			,       A			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      return self.validation_step(A			,       A	)



 def  a__  (self			,       A	)   ->					List[Any]:


      """simple docstring"""



      return self.validation_end(A	)



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  max(1			,       self.hparams.gpus	)  # TODO: consider num_tpu_cores
      _a     =  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



 def  a__  (self			,       A	)   ->					List[Any]:


      """simple docstring"""



      if mode == "test":
           _a     =  len(self.test_dataloader().dataset	)
      else:
           _a     =  self.get_dataloader('''train'''			,       self.hparams.train_batch_size			,       shuffle=A	)
           _a     =  len(self.train_dataloader().dataset	)



 def  a__  (self			,       A			,       A			,       A = False	)   ->					Dict:


      """simple docstring"""



      raise NotImplementedError('''You must implement this for your task'''	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      return self.train_loader



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      return self.get_dataloader('''dev'''			,       self.hparams.eval_batch_size			,       shuffle=A	)



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      return self.get_dataloader('''test'''			,       self.hparams.eval_batch_size			,       shuffle=A	)



 def  a__  (self			,       A	)   ->					Optional[Any]:


      """simple docstring"""



      return os.path.join(
          self.hparams.data_dir			,       '''cached_{}_{}_{}'''.format(
              A			,       list(filter(A			,       self.hparams.model_name_or_path.split('''/'''	)	)	).pop()			,       str(self.hparams.max_seq_length	)			,       )			,       )



 @pl.utilities.rank_zero_only
 def  a__  (self			,       A	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.output_dir.joinpath('''best_tfmr'''	)
      _a     =  self.step_count
      self.model.save_pretrained(A	)
      self.tokenizer.save_pretrained(A	)



 @staticmethod
 def  a__  (A			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      parser.add_argument(
          '''--model_name_or_path'''			,       default=A			,       type=A			,       required=A			,       help='''Path to pretrained model or model identifier from huggingface.co/models'''			,       )
      parser.add_argument(
          '''--config_name'''			,       default=''''''			,       type=A			,       help='''Pretrained config name or path if not the same as model_name'''	)
      parser.add_argument(
          '''--tokenizer_name'''			,       default=A			,       type=A			,       help='''Pretrained tokenizer name or path if not the same as model_name'''			,       )
      parser.add_argument(
          '''--cache_dir'''			,       default=''''''			,       type=A			,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''			,       )
      parser.add_argument(
          '''--encoder_layerdrop'''			,       type=A			,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--decoder_layerdrop'''			,       type=A			,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--dropout'''			,       type=A			,       help='''Dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--attention_dropout'''			,       type=A			,       help='''Attention dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument('''--learning_rate'''			,       default=5E-5			,       type=A			,       help='''The initial learning rate for Adam.'''	)
      parser.add_argument(
          '''--lr_scheduler'''			,       default='''linear'''			,       choices=A			,       metavar=A			,       type=A			,       help='''Learning rate scheduler'''			,       )
      parser.add_argument('''--weight_decay'''			,       default=0.0			,       type=A			,       help='''Weight decay if we apply some.'''	)
      parser.add_argument('''--adam_epsilon'''			,       default=1E-8			,       type=A			,       help='''Epsilon for Adam optimizer.'''	)
      parser.add_argument('''--warmup_steps'''			,       default=0			,       type=A			,       help='''Linear warmup over warmup_steps.'''	)
      parser.add_argument('''--num_workers'''			,       default=4			,       type=A			,       help='''kwarg passed to DataLoader'''	)
      parser.add_argument('''--num_train_epochs'''			,       dest='''max_epochs'''			,       default=3			,       type=A	)
      parser.add_argument('''--train_batch_size'''			,       default=32			,       type=A	)
      parser.add_argument('''--eval_batch_size'''			,       default=32			,       type=A	)
      parser.add_argument('''--adafactor'''			,       action='''store_true'''	)






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      _a     =  trainer.lr_schedulers[0]['''scheduler''']
      _a     =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
      pl_module.logger.log_metrics(A	)



 def  a__  (self			,       A			,       A	)   ->					Optional[Any]:


      """simple docstring"""



      rank_zero_info('''***** Validation results *****'''	)
      _a     =  trainer.callback_metrics
      # Log results
      for key in sorted(A	):
           if key not in ["log", "progress_bar"]:
                rank_zero_info('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)



 def  a__  (self			,       A			,       A	)   ->					int:


      """simple docstring"""



      rank_zero_info('''***** Test results *****'''	)
      _a     =  trainer.callback_metrics
      # Log and save results to file
      _a     =  os.path.join(pl_module.hparams.output_dir			,       '''test_results.txt'''	)
      with open(A			,       '''w'''	) as writer:
           for key in sorted(A	):
                if key not in ["log", "progress_bar"]:
                     rank_zero_info('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)
                     writer.write('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)





def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     parser.add_argument(
         '''--output_dir'''  ,     default=snake_case__  ,     type=snake_case__  ,     required=snake_case__  ,     help='''The output directory where the model predictions and checkpoints will be written.'''  ,     )
     parser.add_argument(
         '''--fp16'''  ,     action='''store_true'''  ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''  ,     )

     parser.add_argument(
         '''--fp16_opt_level'''  ,     type=snake_case__  ,     default='''O2'''  ,     help=(
             '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
             '''See details at https://nvidia.github.io/apex/amp.html'''
         )  ,     )
     parser.add_argument('''--n_tpu_cores'''  ,     dest='''tpu_cores'''  ,     type=snake_case__)
     parser.add_argument('''--max_grad_norm'''  ,     dest='''gradient_clip_val'''  ,     default=1.0  ,     type=snake_case__  ,     help='''Max gradient norm''')
     parser.add_argument('''--do_train'''  ,     action='''store_true'''  ,     help='''Whether to run training.''')
     parser.add_argument('''--do_predict'''  ,     action='''store_true'''  ,     help='''Whether to run predictions on the test set.''')
     parser.add_argument(
         '''--gradient_accumulation_steps'''  ,     dest='''accumulate_grad_batches'''  ,     type=snake_case__  ,     default=1  ,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,     )
     parser.add_argument('''--seed'''  ,     type=snake_case__  ,     default=42  ,     help='''random seed for initialization''')
     parser.add_argument(
         '''--data_dir'''  ,     default=snake_case__  ,     type=snake_case__  ,     required=snake_case__  ,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,     )




def lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=True  ,     __A=[]  ,     __A=None  ,     __A=None  ,     **__A  ,     ):



     """simple docstring"""



     pl.seed_everything(args.seed)

     # init model
     _a     =  Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=snake_case__)

     # add custom checkpoints
     if checkpoint_callback is None:
          _a     =  pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir  ,     prefix='''checkpoint'''  ,     monitor='''val_loss'''  ,     mode='''min'''  ,     save_top_k=1)
     if early_stopping_callback:
          extra_callbacks.append(snake_case__)
     if logging_callback is None:
          _a     =  LoggingCallback()

     _a     =  {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _a     =  16
          _a     =  args.fpaa_opt_level

     if args.gpus > 1:
          _a     =  '''ddp'''

     _a     =  args.accumulate_grad_batches
     _a     =  extra_train_kwargs.get('''accelerator'''  ,     snake_case__)
     _a     =  extra_train_kwargs.get('''profiler'''  ,     snake_case__)

     _a     =  pl.Trainer.from_argparse_args(
         snake_case__  ,     weights_summary=snake_case__  ,     callbacks=[logging_callback] + extra_callbacks  ,     logger=snake_case__  ,     checkpoint_callback=snake_case__  ,     **snake_case__  ,     )

     if args.do_train:
          trainer.fit(snake_case__)

     return trainer








'''simple docstring'''





import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowercase_			=  logging.getLogger(__name__)




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  argparse.ArgumentParser(description='''PyTorch Transformer Language Model''')
     parser.add_argument('''--model_name'''  ,     type=__A  ,     default='''transfo-xl-wt103'''  ,     help='''pretrained model name''')
     parser.add_argument(
         '''--split'''  ,     type=__A  ,     default='''test'''  ,     choices=['''all''', '''valid''', '''test''']  ,     help='''which split to evaluate''')
     parser.add_argument('''--batch_size'''  ,     type=__A  ,     default=10  ,     help='''batch size''')
     parser.add_argument('''--tgt_len'''  ,     type=__A  ,     default=128  ,     help='''number of tokens to predict''')
     parser.add_argument('''--ext_len'''  ,     type=__A  ,     default=0  ,     help='''length of the extended context''')
     parser.add_argument('''--mem_len'''  ,     type=__A  ,     default=1_600  ,     help='''length of the retained previous heads''')
     parser.add_argument('''--clamp_len'''  ,     type=__A  ,     default=1_000  ,     help='''max positional embedding index''')
     parser.add_argument('''--no_cuda'''  ,     action='''store_true'''  ,     help='''Do not use CUDA even though CUA is available''')
     parser.add_argument('''--work_dir'''  ,     type=__A  ,     required=__A  ,     help='''path to the work_dir''')
     parser.add_argument('''--no_log'''  ,     action='''store_true'''  ,     help='''do not log the eval result''')
     parser.add_argument('''--same_length'''  ,     action='''store_true'''  ,     help='''set same length attention with masking''')
     parser.add_argument('''--server_ip'''  ,     type=__A  ,     default=''''''  ,     help='''Can be used for distant debugging.''')
     parser.add_argument('''--server_port'''  ,     type=__A  ,     default=''''''  ,     help='''Can be used for distant debugging.''')
     _a     =  parser.parse_args()
     assert args.ext_len >= 0, "extended context length must be non-negative"

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('''Waiting for debugger attach''')
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,     redirect_output=__A)
          ptvsd.wait_for_attach()

     _a     =  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
     logger.info('''device: {}'''.format(__A))

     # Load a pre-processed dataset
     # You can also build the corpus yourself using TransfoXLCorpus methods
     # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
     # and tokenizing the dataset
     # The pre-processed corpus is a convertion (using the conversion script )
     _a     =  TransfoXLCorpus.from_pretrained(args.model_name)

     _a     =  corpus.get_iterator('''valid'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)
     _a     =  corpus.get_iterator('''test'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)

     # Load a pre-trained model
     _a     =  TransfoXLLMHeadModel.from_pretrained(args.model_name)
     model.to(__A)

     logger.info(
         '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
             args.batch_size  ,     args.tgt_len  ,     args.ext_len  ,     args.mem_len  ,     args.clamp_len))

     model.reset_memory_length(args.mem_len)
     if args.clamp_len > 0:
          _a     =  args.clamp_len
     if args.same_length:
          _a     =  True

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(__A):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          _a  ,   _a     =  0, 0.0
          _a     =  time.time()
          with torch.no_grad():
               _a     =  None
               for idx, (data, target, seq_len) in enumerate(__A):
                    _a     =  model(__A  ,     lm_labels=__A  ,     mems=__A)
                    _a  ,   _a  ,   _a     =  ret
                    _a     =  loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               _a     =  time.time() - start_time
          logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(__A  ,     1_000 * total_time / (idx + 1)))
          return total_loss / total_len

     # Run on test data.
     if args.split == "all":
          _a     =  evaluate(__A)
          _a     =  evaluate(__A)
     elif args.split == "valid":
          _a     =  evaluate(__A)
          _a     =  None
     elif args.split == "test":
          _a     =  evaluate(__A)
          _a     =  None

     def format_log(__A  ,     __A):
          _a     =  '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(__A  ,     __A  ,     math.exp(__A))
          return log_str

     _a     =  ''''''
     if valid_loss is not None:
          log_str += format_log(__A  ,     '''valid''')
     if test_loss is not None:
          log_str += format_log(__A  ,     '''test''')

     logger.info('''=''' * 100)
     logger.info(__A)
     logger.info('''=''' * 100)


if __name__ == "__main__":
  main()



