


"""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__				:Union[str, Any]			   =	logging.getLogger(__name__)

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

UpperCamelCase__				:int			   =	{
    """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__				:Dict			   =	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class      A(  pl.LightningModule		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__="base"					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	List[Any]:

					"""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(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	0
					_UpperCamelCase					:int					     =	Path(self.hparams.output_dir   )
					_UpperCamelCase					:Union[str, Any]					     =	self.hparams.cache_dir if self.hparams.cache_dir else None
					if config is None:
								_UpperCamelCase					: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=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					else:
								_UpperCamelCase					:PretrainedConfig					     =	config

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

					if tokenizer is None:
								_UpperCamelCase					:int					     =	AutoTokenizer.from_pretrained(
								    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,				cache_dir=SCREAMING_SNAKE_CASE__					,				)
					else:
								_UpperCamelCase					:PreTrainedTokenizer					     =	tokenizer
					_UpperCamelCase					:Dict					     =	MODEL_MODES[mode]
					if model is None:
								_UpperCamelCase					:Optional[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=SCREAMING_SNAKE_CASE__					,				)
					else:
								_UpperCamelCase					:int					     =	model
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	arg_to_scheduler[self.hparams.lr_scheduler]
					_UpperCamelCase					:List[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					:Optional[Any]					     =	self.model
					_UpperCamelCase					:List[str]					     =	['''bias''', '''LayerNorm.weight''']
					_UpperCamelCase					:Any					     =	[
					    {
					        '''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:
								_UpperCamelCase					:List[Any]					     =	Adafactor(
								    SCREAMING_SNAKE_CASE__					,				lr=self.hparams.learning_rate					,				scale_parameter=SCREAMING_SNAKE_CASE__					,				relative_step=SCREAMING_SNAKE_CASE__   )

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

					_UpperCamelCase					:Optional[int]					     =	self.get_lr_scheduler()

					return [optimizer], [scheduler]
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					return self.validation_step(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					return self.validation_end(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	max(1					,				self.hparams.gpus   )  # TODO: consider num_tpu_cores
					_UpperCamelCase					:Dict					     =	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					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					if stage == "test":
								_UpperCamelCase					:Tuple					     =	len(self.test_dataloader().dataset   )
					else:
								_UpperCamelCase					:Tuple					     =	self.get_dataloader('''train'''					,				self.hparams.train_batch_size					,				shuffle=SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:int					     =	len(self.train_dataloader().dataset   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = False   )      ->	Any:

					"""simple docstring"""



					raise NotImplementedError('''You must implement this for your task'''   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return self.train_loader
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return self.get_dataloader('''dev'''					,				self.hparams.eval_batch_size					,				shuffle=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					return self.get_dataloader('''test'''					,				self.hparams.eval_batch_size					,				shuffle=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return os.path.join(
					    self.hparams.data_dir					,				'''cached_{}_{}_{}'''.format(
					        SCREAMING_SNAKE_CASE__					,				list(filter(SCREAMING_SNAKE_CASE__					,				self.hparams.model_name_or_path.split('''/'''   )   )   ).pop()					,				str(self.hparams.max_seq_length   )					,				)					,				)
		@pl.utilities.rank_zero_only
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.output_dir.joinpath('''best_tfmr'''   )
					_UpperCamelCase					:Dict					     =	self.step_count
					self.model.save_pretrained(SCREAMING_SNAKE_CASE__   )
					self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__   )





		@staticmethod
		def   _UpperCamelCase( SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



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






class      A(  pl.Callback		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""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      A(  pl.Callback		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



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






class      A(  pl.Callback		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	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(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



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





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					rank_zero_info('''***** Test results *****'''   )
					_UpperCamelCase					: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(SCREAMING_SNAKE_CASE__					,				'''w'''   ) as writer:
								for key in sorted(SCREAMING_SNAKE_CASE__   ):
											if key not in ["log", "progress_bar"]:
														rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__					,				str(metrics[key]   )   )   )
														writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__					,				str(metrics[key]   )   )   )





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			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__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=True       ,					snake_case__=[]       ,					snake_case__=None       ,					snake_case__=None       ,					**snake_case__       ,					)					->  Optional[int]:
			pl.seed_everything(args.seed		)

			# init model
			_UpperCamelCase					:List[Any]					     =	Path(model.hparams.output_dir		)
			odir.mkdir(exist_ok=snake_case__		)

			# add custom checkpoints
			if checkpoint_callback is None:
						_UpperCamelCase					:Optional[int]					     =	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:
						_UpperCamelCase					:Optional[int]					     =	LoggingCallback()

			_UpperCamelCase					:Tuple					     =	{}

			if args.fpaa:
						_UpperCamelCase					:Tuple					     =	16

			if args.gpus > 1:
						_UpperCamelCase					:int					     =	'''auto'''
						_UpperCamelCase					:Union[str, Any]					     =	'''ddp'''

			_UpperCamelCase					:Optional[int]					     =	args.accumulate_grad_batches
			_UpperCamelCase					:Optional[Any]					     =	None
			_UpperCamelCase					:Tuple					     =	'''auto'''

			_UpperCamelCase					:Dict					     =	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





"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

