




'''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


a_      :		Dict							      =       logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

a_      :		str							      =       {
    '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
a_      :		Tuple							      =       {
    '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
}
a_      :		str							      =       sorted(arg_to_scheduler.keys())
a_      :		Union[str, Any]							      =       '{' + ', '.join(arg_to_scheduler_choices) + '}'


class   __UpperCamelCase       ( pl.LightningModule				):






       def __init__(							self,		lowerCAmelCase,		lowerCAmelCase=None,		lowerCAmelCase="base",		lowerCAmelCase=None,		lowerCAmelCase=None,		lowerCAmelCase=None,		**lowerCAmelCase,		):



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

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

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






       def      lowercase__     (							self,		*lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model
              lowerCamelCase_							=['''bias''', '''LayerNorm.weight''']
              lowerCamelCase_							=[
                  {
                      '''params''': [
                          p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )
                      ],  # check this named paramters
                      '''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:
                     lowerCamelCase_							=Adafactor(
                         _UpperCamelCase,		lr=self.hparams.learning_rate,		scale_parameter=_UpperCamelCase,		relative_step=_UpperCamelCase  )

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

              lowerCamelCase_							=self.get_lr_scheduler()

              return [optimizer], [scheduler]






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              return self.validation_step(_UpperCamelCase,		_UpperCamelCase  )






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              return self.validation_end(_UpperCamelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = False  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







              return self.train_loader






       def      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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






       @pl.utilities.rank_zero_only
       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=self.output_dir.joinpath('''best_tfmr'''  )
              lowerCamelCase_							=self.step_count
              self.model.save_pretrained(_UpperCamelCase  )
              self.tokenizer.save_pretrained(_UpperCamelCase  )






       @staticmethod
       def      lowercase__     (							lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







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





class   __UpperCamelCase       ( pl.Callback				):






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """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   __UpperCamelCase       ( pl.Callback				):






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              for name, param in pl_module.model.rag.named_parameters():
                     if param.grad is None:
                            print(_UpperCamelCase  )





class   __UpperCamelCase       ( pl.Callback				):






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







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





def      a_							(      __snake_case							:			Any   ,       __snake_case							:			List[Any]   )							->						None:



       """simple docstring"""
       parser.add_argument(
           '''--output_dir'''   ,       default=str(Path(__snake_case   ).parent / '''test_run''' / '''model_checkpoints'''   )   ,       type=__snake_case   ,       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=__snake_case   ,       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=__snake_case   )
       parser.add_argument('''--max_grad_norm'''   ,       dest='''gradient_clip_val'''   ,       default=1.0   ,       type=__snake_case   ,       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=__snake_case   ,       default=1   ,       help='''Number of updates steps to accumulate before performing a backward/update pass.'''   ,       )
       parser.add_argument('''--seed'''   ,       type=__snake_case   ,       default=42   ,       help='''random seed for initialization'''   )
       parser.add_argument(
           '''--data_dir'''   ,       default=str(Path(__snake_case   ).parent / '''test_run''' / '''dummy-train-data'''   )   ,       type=__snake_case   ,       help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''   ,       )





def      a_							(      __snake_case							:			BaseTransformer   ,       __snake_case							:			argparse.Namespace   ,       __snake_case							:			int=None   ,       __snake_case							:			Any=True   ,       __snake_case							:			Tuple=[]   ,       __snake_case							:			str=None   ,       __snake_case							:			List[str]=None   ,       **__snake_case							:			Any   ,       )							->						Optional[Any]:



       """simple docstring"""
       pl.seed_everything(args.seed   )

       # init model
       lowerCamelCase_							=Path(model.hparams.output_dir   )
       odir.mkdir(exist_ok=__snake_case   )

       # add custom checkpoints
       if checkpoint_callback is None:
              lowerCamelCase_							=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(__snake_case   )
       if logging_callback is None:
              lowerCamelCase_							=LoggingCallback()

       lowerCamelCase_							={}

       if args.fpaa:
              lowerCamelCase_							=16

       if args.gpus > 1:
              lowerCamelCase_							='''auto'''
              lowerCamelCase_							='''ddp'''

       lowerCamelCase_							=args.accumulate_grad_batches
       lowerCamelCase_							=None
       lowerCamelCase_							='''auto'''

       lowerCamelCase_							=pl.Trainer.from_argparse_args(
           __snake_case   ,       weights_summary=__snake_case   ,       callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]   ,       logger=__snake_case   ,       val_check_interval=1   ,       num_sanity_val_steps=2   ,       **__snake_case   ,       )

       if args.do_train:
              trainer.fit(__snake_case   )

       else:
              print('''RAG modeling tests with new set functions successfuly executed!'''   )
       return trainer











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:							Optional[Any]													=     {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							List[str]													=     [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

       snake_case__					:							Dict													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





