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


__snake_case         =     logging.getLogger(__name__)

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

__snake_case         =     {
    """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
__snake_case         =     {
    """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
}
__snake_case         =     sorted(arg_to_scheduler.keys())
__snake_case         =     """{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class       __snake_case (  pl.LightningModule ):
 def __init__(	self		, snake_case__		, snake_case__=None		, snake_case__="base"		, snake_case__=None		, snake_case__=None		, snake_case__=None		, **snake_case__		, )						->				str:
       '''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(snake_case_    )
       UpperCAmelCase     :			str     	=0
       UpperCAmelCase     :			Tuple     	=Path(self.hparams.output_dir    )
       UpperCAmelCase     :			Optional[int]     	=self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
             UpperCAmelCase     :			List[str]     	=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=snake_case_		, **snake_case_		, )
       else:
             UpperCAmelCase     :			PretrainedConfig     	=config

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

       if tokenizer is None:
             UpperCAmelCase     :			Tuple     	=AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		, cache_dir=snake_case_		, )
       else:
             UpperCAmelCase     :			PreTrainedTokenizer     	=tokenizer
       UpperCAmelCase     :			List[Any]     	=MODEL_MODES[mode]
       if model is None:
             UpperCAmelCase     :			int     	=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=snake_case_		, )
       else:
             UpperCAmelCase     :			Optional[int]     	=model
 def 						UpperCAmelCase__							(	self		, *snake_case__		, **snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.model_type.from_pretrained(*snake_case_		, **snake_case_    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=arg_to_scheduler[self.hparams.lr_scheduler]
       UpperCAmelCase     :			Union[str, Any]     	=get_schedule_func(
           self.opt		, num_warmup_steps=self.hparams.warmup_steps		, num_training_steps=self.total_steps()    )
       UpperCAmelCase     :			Union[str, Any]     	={"scheduler": scheduler, "interval": "step", "frequency": 1}
       return scheduler
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.model
       UpperCAmelCase     :			List[Any]     	=["bias", "LayerNorm.weight"]
       UpperCAmelCase     :			Optional[Any]     	=[
           {
               "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     :			int     	=Adafactor(
                 snake_case_		, lr=self.hparams.learning_rate		, scale_parameter=snake_case_		, relative_step=snake_case_    )

       else:
             UpperCAmelCase     :			Tuple     	=AdamW(
                 snake_case_		, lr=self.hparams.learning_rate		, eps=self.hparams.adam_epsilon    )
       UpperCAmelCase     :			Any     	=optimizer

       UpperCAmelCase     :			List[str]     	=self.get_lr_scheduler()

       return [optimizer], [scheduler]
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Dict:
       '''simple docstring'''





       return self.validation_step(snake_case_		, snake_case_    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[str]:
       '''simple docstring'''





       return self.validation_end(snake_case_    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=max(1		, self.hparams.gpus    )  # TODO: consider num_tpu_cores
       UpperCAmelCase     :			List[str]     	=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		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       if stage == "test":
             UpperCAmelCase     :			int     	=len(self.test_dataloader().dataset    )
       else:
             UpperCAmelCase     :			Tuple     	=self.get_dataloader('''train'''		, self.hparams.train_batch_size		, shuffle=snake_case_    )
             UpperCAmelCase     :			Union[str, Any]     	=len(self.train_dataloader().dataset    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__ = False    )						->				str:
       '''simple docstring'''





       raise NotImplementedError('''You must implement this for your task'''    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       return self.train_loader
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       return self.get_dataloader('''dev'''		, self.hparams.eval_batch_size		, shuffle=snake_case_    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       return self.get_dataloader('''test'''		, self.hparams.eval_batch_size		, shuffle=snake_case_    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Dict:
       '''simple docstring'''





       return os.path.join(
           self.hparams.data_dir		, '''cached_{}_{}_{}'''.format(
               snake_case_		, list(filter(snake_case_		, self.hparams.model_name_or_path.split('''/'''    )    )    ).pop()		, str(self.hparams.max_seq_length    )		, )		, )
 @pl.utilities.rank_zero_only
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.output_dir.joinpath('''best_tfmr'''    )
       UpperCAmelCase     :			int     	=self.step_count
       self.model.save_pretrained(snake_case_    )
       self.tokenizer.save_pretrained(snake_case_    )






 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__		, snake_case__    )						->				str:
       '''simple docstring'''





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







class       __snake_case (  pl.Callback ):
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				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       __snake_case (  pl.Callback ):
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=trainer.lr_schedulers[0]["scheduler"]
       UpperCAmelCase     :			int     	={f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
       pl_module.logger.log_metrics(snake_case_    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Tuple:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	List[Any]:






      '''simple docstring'''





      parser.add_argument(
          '''--output_dir'''		,       default=__lowerCAmelCase		,       type=__lowerCAmelCase		,       required=__lowerCAmelCase		,       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=__lowerCAmelCase		,       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=__lowerCAmelCase    )
      parser.add_argument('''--max_grad_norm'''		,       dest='''gradient_clip_val'''		,       default=1.0		,       type=__lowerCAmelCase		,       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=__lowerCAmelCase		,       default=1		,       help='''Number of updates steps to accumulate before performing a backward/update pass.'''		,       )
      parser.add_argument('''--seed'''		,       type=__lowerCAmelCase		,       default=42		,       help='''random seed for initialization'''    )
      parser.add_argument(
          '''--data_dir'''		,       default=__lowerCAmelCase		,       type=__lowerCAmelCase		,       required=__lowerCAmelCase		,       help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''		,       )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       __lowerCAmelCase=[]		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       )->	List[str]:






      '''simple docstring'''





      pl.seed_everything(args.seed    )

      # init model
      UpperCAmelCase     :			str     	=Path(model.hparams.output_dir    )
      odir.mkdir(exist_ok=__lowerCAmelCase    )

      # add custom checkpoints
      if checkpoint_callback is None:
            UpperCAmelCase     :			str     	=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(__lowerCAmelCase    )
      if logging_callback is None:
            UpperCAmelCase     :			Optional[int]     	=LoggingCallback()

      UpperCAmelCase     :			List[Any]     	={}

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

      if args.gpus > 1:
            UpperCAmelCase     :			Dict     	="auto"  # "ddp"
            UpperCAmelCase     :			str     	="ddp"

      UpperCAmelCase     :			Optional[int]     	=args.accumulate_grad_batches
      UpperCAmelCase     :			Tuple     	=None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
      UpperCAmelCase     :			Union[str, Any]     	="auto"

      UpperCAmelCase     :			Dict     	=pl.Trainer.from_argparse_args(
          __lowerCAmelCase		,       weights_summary=__lowerCAmelCase		,       callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]		,       logger=__lowerCAmelCase		,       **__lowerCAmelCase		,       )

      if args.do_train:
            trainer.fit(__lowerCAmelCase    )

      return trainer


"""simple docstring"""
from ..utils import requires_backends


_a   :      List[str]       =				None






class   _UpperCAmelCase					:
    def __init__(     self    ,     *snake_case_    ,     **snake_case_  ):
      requires_backends(self    ,     ["detectron2"]  )


    @classmethod
    def        lowerCamelCase__						(     cls    ,     *snake_case_    ,     **snake_case_  ):
      requires_backends(cls    ,     ["detectron2"]  )

