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


lowerCamelCase__   = logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCamelCase__   = {
    '''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
lowerCamelCase__   = {
    '''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
}
lowerCamelCase__   = sorted(arg_to_scheduler.keys())
lowerCamelCase__   = '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class  _UpperCAmelCase       (			pl.LightningModule   ):



 '''simple docstring'''




 def __init__(						self							:					Union[str, Any]				,      lowercase_							:					argparse.Namespace				,      lowercase_							:					List[Any]=None				,      lowercase_							:					Optional[int]="base"				,      lowercase_							:					Optional[Any]=None				,      lowercase_							:					Optional[int]=None				,      lowercase_							:					Optional[int]=None				,      **lowercase_							:					List[str]				,      )		->     Optional[Any]:


   """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(lowercase_)
   _UpperCamelCase				=  0
   _UpperCamelCase				=  Path(self.hparams.output_dir)
   _UpperCamelCase				=  self.hparams.cache_dir if self.hparams.cache_dir else None
   if config is None:
     _UpperCamelCase				=  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=lowercase_				,      **lowercase_				,      )
   else:
     _UpperCamelCase				=  config

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

   if tokenizer is None:
     _UpperCamelCase				=  AutoTokenizer.from_pretrained(
         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,      cache_dir=lowercase_				,      )
   else:
     _UpperCamelCase				=  tokenizer
   _UpperCamelCase				=  MODEL_MODES[mode]
   if model is None:
     _UpperCamelCase				=  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=lowercase_				,      )
   else:
     _UpperCamelCase				=  model







 def 						__UpperCAmelCase							(						self							:					Tuple				,      *lowercase_							:					Optional[Any]				,      **lowercase_							:					Union[str, Any])		->     Dict:


   """simple docstring"""







   _UpperCamelCase				=  self.model_type.from_pretrained(*lowercase_				,      **lowercase_)







 def 						__UpperCAmelCase							(						self							:					int)		->     List[Any]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Any)		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  self.model
   _UpperCamelCase				=  ["bias", "LayerNorm.weight"]
   _UpperCamelCase				=  [
       {
           "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:
     _UpperCamelCase				=  Adafactor(
         lowercase_				,      lr=self.hparams.learning_rate				,      scale_parameter=lowercase_				,      relative_step=lowercase_)

   else:
     _UpperCamelCase				=  AdamW(
         lowercase_				,      lr=self.hparams.learning_rate				,      eps=self.hparams.adam_epsilon)
   _UpperCamelCase				=  optimizer

   _UpperCamelCase				=  self.get_lr_scheduler()

   return [optimizer], [scheduler]







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Any				,      lowercase_							:					List[Any])		->     Optional[Any]:


   """simple docstring"""







   return self.validation_step(lowercase_				,      lowercase_)







 def 						__UpperCAmelCase							(						self							:					str				,      lowercase_							:					str)		->     int:


   """simple docstring"""







   return self.validation_end(lowercase_)







 def 						__UpperCAmelCase							(						self							:					Any)		->     int:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Union[str, Any]				,      lowercase_							:					str)		->     Tuple:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					str				,      lowercase_							:					int				,      lowercase_							:					bool = False)		->     Dict:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Any)		->     Any:


   """simple docstring"""







   return self.train_loader







 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     List[str]:


   """simple docstring"""







   return self.get_dataloader("dev"				,      self.hparams.eval_batch_size				,      shuffle=lowercase_)







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


   """simple docstring"""







   return self.get_dataloader("test"				,      self.hparams.eval_batch_size				,      shuffle=lowercase_)







 def 						__UpperCAmelCase							(						self							:					Union[str, Any]				,      lowercase_							:					Any)		->     Tuple:


   """simple docstring"""







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







 @pl.utilities.rank_zero_only
 def 						__UpperCAmelCase							(						self							:					Optional[int]				,      lowercase_							:					Dict[str, Any])		->     None:


   """simple docstring"""







   _UpperCamelCase				=  self.output_dir.joinpath("best_tfmr")
   _UpperCamelCase				=  self.step_count
   self.model.save_pretrained(lowercase_)
   self.tokenizer.save_pretrained(lowercase_)







 @staticmethod
 def 						__UpperCAmelCase							(						lowercase_							:					List[str]				,      lowercase_							:					List[str])		->     Union[str, Any]:


   """simple docstring"""







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




class  _UpperCAmelCase       (			pl.Callback   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					Tuple				,      lowercase_							:					Optional[Any]				,      lowercase_							:					Optional[int])		->     Any:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					pl.Trainer				,      lowercase_							:					pl.LightningModule)		->     Optional[int]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Any				,      lowercase_							:					pl.Trainer				,      lowercase_							:					pl.LightningModule)		->     Tuple:


   """simple docstring"""







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


def 							lowerCAmelCase__    (       a__  ,			a__      )	->None:




  '''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__=[]  ,			a__=None  ,			a__=None  ,			**a__  ,			)	->Tuple:




  '''simple docstring'''

  pl.seed_everything(args.seed      )

  # init model
  _UpperCamelCase				=  Path(model.hparams.output_dir      )
  odir.mkdir(exist_ok=a__      )

  # add custom checkpoints
  if checkpoint_callback is None:
    _UpperCamelCase				=  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:
    _UpperCamelCase				=  LoggingCallback()

  _UpperCamelCase				=  {}

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

  if args.gpus > 1:
    _UpperCamelCase				=  "ddp"

  _UpperCamelCase				=  args.accumulate_grad_batches
  _UpperCamelCase				=  extra_train_kwargs.get("accelerator"  ,			a__      )
  _UpperCamelCase				=  extra_train_kwargs.get("profiler"  ,			a__      )

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

  if args.do_train:
    trainer.fit(a__      )

  return trainer





lowerCamelCase__   = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase__   = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase__   = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




