"""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


_UpperCamelCase       :						List[Any]     =   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_UpperCamelCase       :						Dict     =   {
    "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
_UpperCamelCase       :						List[Any]     =   {
    "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
}
_UpperCamelCase       :						Dict     =   sorted(arg_to_scheduler.keys())
_UpperCamelCase       :						str     =   "{" + ", ".join(arg_to_scheduler_choices) + "}"


class 					UpperCAmelCase_     (   pl.LightningModule):


      def __init__(       self		,      a		,      a=None		,      a="base"		,      a=None		,      a=None		,      a=None		,      **a		,      )	-> List[str]:
             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							)
             lowercase__      :		int          =     0
             lowercase__      :		Tuple          =     Path(self.hparams.output_dir							)
             lowercase__      :		Optional[Any]          =     self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    lowercase__      :		int          =     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:
                    lowercase__      :		PretrainedConfig          =     config

             lowercase__      :		Optional[int]          =     ('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:
                    lowercase__      :		List[Any]          =     AutoTokenizer.from_pretrained(
                        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,      cache_dir=a		,      )
             else:
                    lowercase__      :		PreTrainedTokenizer          =     tokenizer
             lowercase__      :		Tuple          =     MODEL_MODES[mode]
             if model is None:
                    lowercase__      :		Optional[Any]          =     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:
                    lowercase__      :		Any          =     model


      def 		_UpperCAmelCase	(       self		,      *a		,      **a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     self.model_type.from_pretrained(*a		,      **a							)


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


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		str          =     self.model
             lowercase__      :		Optional[int]          =     ['bias', 'LayerNorm.weight']
             lowercase__      :		List[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:
                    lowercase__      :		int          =     Adafactor(
                        a		,      lr=self.hparams.learning_rate		,      scale_parameter=a		,      relative_step=a							)

             else:
                    lowercase__      :		Optional[int]          =     AdamW(
                        a		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon							)
             lowercase__      :		List[Any]          =     optimizer

             lowercase__      :		Dict          =     self.get_lr_scheduler()

             return [optimizer], [scheduler]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Tuple:
             return self.validation_step(a		,      a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> int:
             return self.validation_end(a							)


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Any          =     max(1		,      self.hparams.gpus							)  # TODO: consider num_tpu_cores
             lowercase__      :		List[Any]          =     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		,      a							)	-> Tuple:
             if mode == "test":
                    lowercase__      :		Any          =     len(self.test_dataloader().dataset							)
             else:
                    lowercase__      :		int          =     self.get_dataloader('train'		,      self.hparams.train_batch_size		,      shuffle=a							)
                    lowercase__      :		Union[str, Any]          =     len(self.train_dataloader().dataset							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a = False							)	-> Optional[Any]:
             raise NotImplementedError('You must implement this for your task'							)


      def 		_UpperCAmelCase	(       self							)	-> int:
             return self.train_loader


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             return self.get_dataloader('dev'		,      self.hparams.eval_batch_size		,      shuffle=a							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             return self.get_dataloader('test'		,      self.hparams.eval_batch_size		,      shuffle=a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Union[str, Any]:
             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 		_UpperCAmelCase	(       self		,      a							)	-> None:
             lowercase__      :		Optional[int]          =     self.output_dir.joinpath('best_tfmr'							)
             lowercase__      :		Union[str, Any]          =     self.step_count
             self.model.save_pretrained(a							)
             self.tokenizer.save_pretrained(a							)


      @staticmethod
      def 		_UpperCAmelCase	(       a		,      a							)	-> Union[str, Any]:
             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=3_2		,      type=a							)
             parser.add_argument('--eval_batch_size'		,      default=3_2		,      type=a							)
             parser.add_argument('--adafactor'		,      action='store_true'							)





class 					UpperCAmelCase_     (   pl.Callback):


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		Tuple          =     trainer.lr_schedulers[0]['scheduler']
             lowercase__      :		Optional[Any]          =     {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
             pl_module.logger.log_metrics(a							)


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> int:
             rank_zero_info('***** Validation results *****'							)
             lowercase__      :		Any          =     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 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Dict:
             rank_zero_info('***** Test results *****'							)
             lowercase__      :		str          =     trainer.callback_metrics
             # Log and save results to file
             lowercase__      :		Optional[int]          =     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     a_					(				_lowerCAmelCase  :			Union[str, Any]       ,			_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     a_					(				_lowerCAmelCase  :			BaseTransformer       ,			_lowerCAmelCase  :			argparse.Namespace       ,			_lowerCAmelCase  :			List[str]=None       ,			_lowerCAmelCase  :			List[Any]=True       ,			_lowerCAmelCase  :			int=[]       ,			_lowerCAmelCase  :			Union[str, Any]=None       ,			_lowerCAmelCase  :			int=None       ,			**_lowerCAmelCase  :			int       ,			):





       '''simple docstring'''

       pl.seed_everything(args.seed					)

       # init model
       lowercase__      :		Tuple          =     Path(model.hparams.output_dir					)
       odir.mkdir(exist_ok=_lowerCAmelCase					)

       # add custom checkpoints
       if checkpoint_callback is None:
              lowercase__      :		Dict          =     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:
              lowercase__      :		List[Any]          =     LoggingCallback()

       lowercase__      :		Tuple          =     {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              lowercase__      :		Union[str, Any]          =     16
              lowercase__      :		List[str]          =     args.fpaa_opt_level

       if args.gpus > 1:
              lowercase__      :		str          =     'ddp'

       lowercase__      :		Tuple          =     args.accumulate_grad_batches
       lowercase__      :		List[str]          =     extra_train_kwargs.get('accelerator'       ,			_lowerCAmelCase					)
       lowercase__      :		Dict          =     extra_train_kwargs.get('profiler'       ,			_lowerCAmelCase					)

       lowercase__      :		int          =     pl.Trainer.from_argparse_args(
           _lowerCAmelCase       ,			weights_summary=_lowerCAmelCase       ,			callbacks=[logging_callback] + extra_callbacks       ,			logger=_lowerCAmelCase       ,			checkpoint_callback=_lowerCAmelCase       ,			**_lowerCAmelCase       ,			)

       if args.do_train:
              trainer.fit(_lowerCAmelCase					)

       return trainer


"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   List[str]			  =  CustomConfig


      def __init__(       self		,      a							)	-> Optional[int]:
             super().__init__(a							)
             lowercase__      :		Any          =     torch.nn.Linear(config.hidden_size		,      config.hidden_size							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Union[str, Any]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Tuple:
             pass





class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[int]			  =  NoSuperInitConfig


      def __init__(       self		,      a							)	-> int:
             super().__init__(a							)
             lowercase__      :		List[Any]          =     torch.nn.Linear(config.attribute		,      config.attribute							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             pass

