"""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										=     logging.getLogger(__name__)

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

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




class 					UpperCamelCase   (							pl.LightningModule  ):







       def __init__(					self,				lowerCAmelCase__,				lowerCAmelCase__=None,				lowerCAmelCase__="base",				lowerCAmelCase__=None,				lowerCAmelCase__=None,				lowerCAmelCase__=None,				**lowerCAmelCase__,				) -> Union[str, Any]:
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading

            self.save_hyperparameters(lowerCAmelCase__)
            snake_case_   			=   0
            snake_case_   			=   Path(self.hparams.output_dir)
            snake_case_   			=   self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 snake_case_   			=   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=lowerCAmelCase__,				**lowerCAmelCase__,				)
            else:
                 snake_case_   			=   config

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

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







       def 						a_		(					self,				*lowerCAmelCase__,				**lowerCAmelCase__) -> Dict:
            snake_case_   			=   self.model_type.from_pretrained(*lowerCAmelCase__,				**lowerCAmelCase__)







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







       def 						a_		(					self) -> Union[str, Any]:
            snake_case_   			=   self.model
            snake_case_   			=   ['bias', 'LayerNorm.weight']
            snake_case_   			=   [
                {
                    '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:
                 snake_case_   			=   Adafactor(
                     lowerCAmelCase__,				lr=self.hparams.learning_rate,				scale_parameter=lowerCAmelCase__,				relative_step=lowerCAmelCase__)

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

            snake_case_   			=   self.get_lr_scheduler()

            return [optimizer], [scheduler]







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> Dict:
            return self.validation_step(lowerCAmelCase__,				lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__) -> List[Any]:
            return self.validation_end(lowerCAmelCase__)







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







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = False) -> List[Any]:
            raise NotImplementedError('You must implement this for your task')







       def 						a_		(					self) -> Union[str, Any]:
            return self.train_loader







       def 						a_		(					self) -> int:
            return self.get_dataloader('dev',				self.hparams.eval_batch_size,				shuffle=lowerCAmelCase__)







       def 						a_		(					self) -> str:
            return self.get_dataloader('test',				self.hparams.eval_batch_size,				shuffle=lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__) -> Any:
            return os.path.join(
                self.hparams.data_dir,				'cached_{}_{}_{}'.format(
                    lowerCAmelCase__,				list(filter(lowerCAmelCase__,				self.hparams.model_name_or_path.split('/'))).pop(),				str(self.hparams.max_seq_length),				),				)







       @pl.utilities.rank_zero_only
       def 						a_		(					self,				lowerCAmelCase__) -> None:
            snake_case_   			=   self.output_dir.joinpath('best_tfmr')
            snake_case_   			=   self.step_count
            self.model.save_pretrained(lowerCAmelCase__)
            self.tokenizer.save_pretrained(lowerCAmelCase__)







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




class 					UpperCamelCase   (							pl.Callback  ):







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> Union[str, Any]:
            snake_case_   			=   trainer.lr_schedulers[0]['scheduler']
            snake_case_   			=   {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
            pl_module.logger.log_metrics(lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> List[Any]:
            rank_zero_info('***** Validation results *****')
            snake_case_   			=   trainer.callback_metrics
            # Log results
            for key in sorted(lowerCAmelCase__):
                 if key not in ["log", "progress_bar"]:
                      rank_zero_info('{} = {}\n'.format(lowerCAmelCase__,				str(metrics[key])))







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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         '--output_dir'		,   default=UpperCAmelCase		,   type=UpperCAmelCase		,   required=UpperCAmelCase		,   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=UpperCAmelCase		,   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=UpperCAmelCase   )
     parser.add_argument('--max_grad_norm'		,   dest='gradient_clip_val'		,   default=1.0		,   type=UpperCAmelCase		,   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=UpperCAmelCase		,   default=1		,   help='Number of updates steps to accumulate before performing a backward/update pass.'		,   )
     parser.add_argument('--seed'		,   type=UpperCAmelCase		,   default=42		,   help='random seed for initialization'   )
     parser.add_argument(
         '--data_dir'		,   default=UpperCAmelCase		,   type=UpperCAmelCase		,   required=UpperCAmelCase		,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		,   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None		,   UpperCAmelCase=True		,   UpperCAmelCase=[]		,   UpperCAmelCase=None		,   UpperCAmelCase=None		,   **UpperCAmelCase		,   )							-> Optional[int]:
     pl.seed_everything(args.seed   )

     # init model
     snake_case_   			=   Path(model.hparams.output_dir   )
     odir.mkdir(exist_ok=UpperCAmelCase   )

     # add custom checkpoints
     if checkpoint_callback is None:
          snake_case_   			=   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(UpperCAmelCase   )
     if logging_callback is None:
          snake_case_   			=   LoggingCallback()

     snake_case_   			=   {}

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

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

     snake_case_   			=   args.accumulate_grad_batches
     snake_case_   			=   extra_train_kwargs.get('accelerator'		,   UpperCAmelCase   )
     snake_case_   			=   extra_train_kwargs.get('profiler'		,   UpperCAmelCase   )

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

     if args.do_train:
          trainer.fit(UpperCAmelCase   )

     return trainer


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

