



'''simple docstring'''





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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}






class __A    (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	str										=	'yoso'



 def __init__(self			,       A=50_265			,       A=768			,       A=12			,       A=12			,       A=3_072			,       A="gelu"			,       A=0.1			,       A=0.1			,       A=4_096			,       A=1			,       A=0.02			,       A=1E-12			,       A="absolute"			,       A=True			,       A=9			,       A=64			,       A=None			,       A=True			,       A=True			,       A=1			,       A=0			,       A=2			,       **A			,       )   ->					Dict:


      """simple docstring"""



      super().__init__(pad_token_id=A			,       bos_token_id=A			,       eos_token_id=A			,       **A	)

      _a     =  vocab_size
      _a     =  max_position_embeddings
      _a     =  hidden_size
      _a     =  num_hidden_layers
      _a     =  num_attention_heads
      _a     =  intermediate_size
      _a     =  hidden_act
      _a     =  hidden_dropout_prob
      _a     =  attention_probs_dropout_prob
      _a     =  initializer_range
      _a     =  type_vocab_size
      _a     =  layer_norm_eps
      _a     =  position_embedding_type
      _a     =  use_expectation
      _a     =  hash_code_len
      _a     =  num_hash
      _a     =  conv_window
      _a     =  use_fast_hash
      _a     =  lsh_backward








'''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	)   ->					List[str]:


      """simple docstring"""



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



 def  a__  (self	)   ->					Dict:


      """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	)   ->					List[str]:


      """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	)   ->					Tuple:


      """simple docstring"""



      return self.validation_step(A			,       A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      return self.validation_end(A	)



 def  a__  (self	)   ->					int:


      """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	)   ->					int:


      """simple docstring"""



      if stage == "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	)   ->					Optional[int]:


      """simple docstring"""



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



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


      """simple docstring"""



      return self.train_loader



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


      """simple docstring"""



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



 def  a__  (self	)   ->					Any:


      """simple docstring"""



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



 def  a__  (self			,       A	)   ->					Union[str, 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	)   ->					None:


      """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	)   ->					str:


      """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	)   ->					str:


      """simple docstring"""



      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






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	)   ->					Tuple:


      """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	)   ->					Dict:


      """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=__A  ,     type=__A  ,     required=__A  ,     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=__A  ,     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=__A)
     parser.add_argument('''--max_grad_norm'''  ,     dest='''gradient_clip_val'''  ,     default=1.0  ,     type=__A  ,     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=__A  ,     default=1  ,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,     )
     parser.add_argument('''--seed'''  ,     type=__A  ,     default=42  ,     help='''random seed for initialization''')
     parser.add_argument(
         '''--data_dir'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     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=None  ,     __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=__A)

     # 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(__A)
     if logging_callback is None:
          _a     =  LoggingCallback()

     _a     =  {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _a     =  16
          # train_params["amp_level"] = args.fp16_opt_level

     if args.gpus > 1:
          _a     =  '''auto'''  # "ddp"
          _a     =  '''ddp'''

     _a     =  args.accumulate_grad_batches
     _a     =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _a     =  '''auto'''

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

     if args.do_train:
          trainer.fit(__A)

     return trainer



