
"""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


SCREAMING_SNAKE_CASE_					     =	logging.getLogger(__name__)

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

SCREAMING_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
SCREAMING_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
}
SCREAMING_SNAKE_CASE_					     =	sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE_					     =	'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class       lowerCAmelCase_		(				pl.LightningModule ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_=None    ,				snake_case_="base"    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_    ,				)       ->  int:
													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_				)
													__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=snake_case_    ,				**snake_case_    ,				)
													else:
																			__lowerCAmelCase     =  config

													__lowerCAmelCase     =  ("""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:
																			__lowerCAmelCase     =  AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,				cache_dir=snake_case_    ,				)
													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=snake_case_    ,				)
													else:
																			__lowerCAmelCase     =  model




							def    A__						( self    ,				*snake_case_    ,				**snake_case_				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.model_type.from_pretrained(*snake_case_    ,				**snake_case_				)




							def    A__						( self				)       ->  Dict:
													__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    A__						( self				)       ->  int:
													__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(
																			    snake_case_    ,				lr=self.hparams.learning_rate    ,				scale_parameter=snake_case_    ,				relative_step=snake_case_				)

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

													__lowerCAmelCase     =  self.get_lr_scheduler()

													return [optimizer], [scheduler]




							def    A__						( self    ,				snake_case_    ,				snake_case_				)       ->  List[str]:
													return self.validation_step(snake_case_    ,				snake_case_				)




							def    A__						( self    ,				snake_case_				)       ->  Any:
													return self.validation_end(snake_case_				)




							def    A__						( self				)       ->  int:
													__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    A__						( self    ,				snake_case_				)       ->  List[Any]:
													if stage == "test":
																			__lowerCAmelCase     =  len(self.test_dataloader().dataset				)
													else:
																			__lowerCAmelCase     =  self.get_dataloader("""train"""    ,				self.hparams.train_batch_size    ,				shuffle=snake_case_				)
																			__lowerCAmelCase     =  len(self.train_dataloader().dataset				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_ = False				)       ->  Any:
													raise NotImplementedError("""You must implement this for your task"""				)




							def    A__						( self				)       ->  Optional[int]:
													return self.train_loader




							def    A__						( self				)       ->  List[Any]:
													return self.get_dataloader("""dev"""    ,				self.hparams.eval_batch_size    ,				shuffle=snake_case_				)




							def    A__						( self				)       ->  Optional[Any]:
													return self.get_dataloader("""test"""    ,				self.hparams.eval_batch_size    ,				shuffle=snake_case_				)




							def    A__						( self    ,				snake_case_				)       ->  Dict:
													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    A__						( self    ,				snake_case_				)       ->  None:
													__lowerCAmelCase     =  self.output_dir.joinpath("""best_tfmr"""				)
													__lowerCAmelCase     =  self.step_count
													self.model.save_pretrained(snake_case_				)
													self.tokenizer.save_pretrained(snake_case_				)




							@staticmethod
							def    A__						( snake_case_    ,				snake_case_				)       ->  Optional[Any]:
													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=str(Path(snake_case_				).parent / """test_run""" / """cache"""				)    ,				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       lowerCAmelCase_		(				pl.Callback ):



							'''simple docstring'''





							def    A__						( self    ,				snake_case_    ,				snake_case_				)       ->  Optional[Any]:
													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       lowerCAmelCase_		(				pl.Callback ):



							'''simple docstring'''





							def    A__						( self    ,				snake_case_    ,				snake_case_				)       ->  str:
													# print(pl_module.model.rag)
													for name, param in pl_module.model.rag.named_parameters():
																			if param.grad is None:
																									print(snake_case_				)
class       lowerCAmelCase_		(				pl.Callback ):



							'''simple docstring'''





							def    A__						( self    ,				snake_case_    ,				snake_case_				)       ->  str:
													__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(snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_				)       ->  str:
													rank_zero_info("""***** Validation results *****"""				)
													__lowerCAmelCase     =  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    A__						( self    ,				snake_case_    ,				snake_case_				)       ->  Optional[Any]:
													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(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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    """--output_dir""" ,    default=str(Path(_lowerCAmelCase						).parent / """test_run""" / """model_checkpoints"""						) ,    type=_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=str(Path(_lowerCAmelCase						).parent / """test_run""" / """dummy-train-data"""						) ,    type=_lowerCAmelCase ,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" ,    )



def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase=True ,    _lowerCAmelCase=[] ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    **_lowerCAmelCase ,    ):
						pl.seed_everything(args.seed						)

						# init model
						__lowerCAmelCase     =  Path(model.hparams.output_dir						)
						odir.mkdir(exist_ok=_lowerCAmelCase						)

						# 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(_lowerCAmelCase						)
						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(
						    _lowerCAmelCase ,    weights_summary=_lowerCAmelCase ,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] ,    logger=_lowerCAmelCase ,    val_check_interval=1 ,    num_sanity_val_steps=2 ,    **_lowerCAmelCase ,    )

						if args.do_train:
												trainer.fit(_lowerCAmelCase						)

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


"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
