



'''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__:     Optional[int]										=    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__:     Union[str, 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__:     str										=    sorted(arg_to_scheduler.keys())
UpperCamelCase__:     List[str]										=    "{" + ", ".join(arg_to_scheduler_choices) + "}"

class        SCREAMING_SNAKE_CASE(							pl.LightningModule    ):







	"""simple docstring"""






	def __init__(		self    :  Any	,    __snake_case    :  argparse.Namespace	,    __snake_case    :  Tuple=None	,    __snake_case    :  Optional[int]="base"	,    __snake_case    :  Dict=None	,    __snake_case    :  int=None	,    __snake_case    :  List[Any]=None	,    **__snake_case    :  List[str]	,    )    ->	List[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_	)
								UpperCAmelCase				: Optional[Any]											=	0
								UpperCAmelCase				: Optional[int]											=	Path(self.hparams.output_dir	)
								UpperCAmelCase				: int											=	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=lowerCamelCase_	,    **lowerCamelCase_	,    )
								else:
															UpperCAmelCase				: PretrainedConfig											=	config

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







	def   A			(		self    :  Optional[Any]	,    *__snake_case    :  Tuple	,    **__snake_case    :  List[Any]	)    ->	str:
								UpperCAmelCase				: Tuple											=	self.model_type.from_pretrained(*lowerCamelCase_	,    **lowerCamelCase_	)







	def   A			(		self    :  int	)    ->	List[str]:
								UpperCAmelCase				: Dict											=	arg_to_scheduler[self.hparams.lr_scheduler]
								UpperCAmelCase				: int											=	get_schedule_func(
								    self.opt	,    num_warmup_steps=self.hparams.warmup_steps	,    num_training_steps=self.total_steps()	)
								UpperCAmelCase				: int											=	{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
								return scheduler







	def   A			(		self    :  Optional[int]	)    ->	Union[str, Any]:
								UpperCAmelCase				: Union[str, Any]											=	self.model
								UpperCAmelCase				: int											=	["""bias""", """LayerNorm.weight"""]
								UpperCAmelCase				: List[str]											=	[
								    {
								        """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				: str											=	Adafactor(
															    lowerCamelCase_	,    lr=self.hparams.learning_rate	,    scale_parameter=lowerCamelCase_	,    relative_step=lowerCamelCase_	)

								else:
															UpperCAmelCase				: List[Any]											=	AdamW(
															    lowerCamelCase_	,    lr=self.hparams.learning_rate	,    eps=self.hparams.adam_epsilon	)
								UpperCAmelCase				: Dict											=	optimizer

								UpperCAmelCase				: Any											=	self.get_lr_scheduler()

								return [optimizer], [scheduler]







	def   A			(		self    :  Dict	,    __snake_case    :  Dict	,    __snake_case    :  List[str]	)    ->	Tuple:
								return self.validation_step(lowerCamelCase_	,    lowerCamelCase_	)







	def   A			(		self    :  Any	,    __snake_case    :  Optional[int]	)    ->	List[str]:
								return self.validation_end(lowerCamelCase_	)







	def   A			(		self    :  int	)    ->	List[Any]:
								UpperCAmelCase				: Tuple											=	max(1	,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
								UpperCAmelCase				: Optional[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   A			(		self    :  str	,    __snake_case    :  Optional[int]	)    ->	int:



								if stage == "test":
															UpperCAmelCase				: List[Any]											=	len(self.test_dataloader().dataset	)
								else:
															UpperCAmelCase				: Optional[Any]											=	self.get_dataloader('''train'''	,    self.hparams.train_batch_size	,    shuffle=lowerCamelCase_	)
															UpperCAmelCase				: List[Any]											=	len(self.train_dataloader().dataset	)







	def   A			(		self    :  Any	,    __snake_case    :  str	,    __snake_case    :  int	,    __snake_case    :  bool = False	)    ->	Union[str, Any]:
								raise NotImplementedError('''You must implement this for your task'''	)







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







	def   A			(		self    :  Any	)    ->	List[Any]:
								return self.get_dataloader('''dev'''	,    self.hparams.eval_batch_size	,    shuffle=lowerCamelCase_	)







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







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Any	)    ->	str:
								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    :  Dict	,    __snake_case    :  Dict[str, Any]	)    ->	int:
								UpperCAmelCase				: List[Any]											=	self.output_dir.joinpath('''best_tfmr'''	)
								UpperCAmelCase				: List[str]											=	self.step_count
								self.model.save_pretrained(lowerCamelCase_	)
								self.tokenizer.save_pretrained(lowerCamelCase_	)







	@staticmethod
	def   A			(		__snake_case    :  List[Any]	,    __snake_case    :  Optional[Any]	)    ->	Dict:
								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=str(Path(lowerCamelCase_	).parent / '''test_run''' / '''cache'''	)	,    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        SCREAMING_SNAKE_CASE(							pl.Callback    ):







	"""simple docstring"""






	def   A			(		self    :  Any	,    __snake_case    :  Any	,    __snake_case    :  int	)    ->	Dict:



								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        SCREAMING_SNAKE_CASE(							pl.Callback    ):







	"""simple docstring"""






	def   A			(		self    :  Dict	,    __snake_case    :  List[str]	,    __snake_case    :  List[str]	)    ->	int:



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

class        SCREAMING_SNAKE_CASE(							pl.Callback    ):







	"""simple docstring"""






	def   A			(		self    :  Any	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	)    ->	int:
								UpperCAmelCase				: Optional[Any]											=	trainer.lr_schedulers[0]["""scheduler"""]
								UpperCAmelCase				: Optional[int]											=	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
								pl_module.logger.log_metrics(lowerCamelCase_	)







	def   A			(		self    :  List[str]	,    __snake_case    :  pl.Trainer	,    __snake_case    :  pl.LightningModule	)    ->	List[Any]:
								rank_zero_info('''***** Validation results *****'''	)
								UpperCAmelCase				: Union[str, Any]											=	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    :  Tuple	,    __snake_case    :  pl.Trainer	,    __snake_case    :  pl.LightningModule	)    ->	Optional[Any]:
								rank_zero_info('''***** Test results *****'''	)
								UpperCAmelCase				: Tuple											=	trainer.callback_metrics
								# Log and save results to file
								UpperCAmelCase				: int											=	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 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     Union[str, Any]:

							'''simple docstring'''





							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 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		str=None     ,   _lowerCAmelCase						:		Any=True     ,   _lowerCAmelCase						:		Optional[Any]=[]     ,   _lowerCAmelCase						:		Union[str, Any]=None     ,   _lowerCAmelCase						:		str=None     ,   **_lowerCAmelCase						:		Any     ,   )   ->     Optional[int]:

							'''simple docstring'''





							pl.seed_everything(args.seed						)

							# init model
							UpperCAmelCase				: Tuple											=	Path(model.hparams.output_dir						)
							odir.mkdir(exist_ok=lowerCamelCase_						)

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

							UpperCAmelCase				: int											=	{}

							if args.fpaa:
														UpperCAmelCase				: List[Any]											=	16

							if args.gpus > 1:
														UpperCAmelCase				: List[Any]											=	"""auto"""
														UpperCAmelCase				: List[Any]											=	"""ddp"""

							UpperCAmelCase				: str											=	args.accumulate_grad_batches
							UpperCAmelCase				: Any											=	None
							UpperCAmelCase				: Any											=	"""auto"""

							UpperCAmelCase				: Tuple											=	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'''






UpperCamelCase__:     dict[tuple[int, int, int], int]										=    {}


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int						)   ->     int:

							# if we are absent twice, or late 3 consecutive days,
							# no further prize strings are possible
							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							UpperCAmelCase				: List[Any]											=	(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							UpperCAmelCase				: int											=	_calculate(days - 1     ,   _lowerCAmelCase     ,   late + 1						)

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							UpperCAmelCase				: Optional[Any]											=	_calculate(days - 1     ,   absent + 1     ,   0						)

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							UpperCAmelCase				: Tuple											=	_calculate(days - 1     ,   _lowerCAmelCase     ,   0						)

							UpperCAmelCase				: str											=	state_late + state_absent + state_ontime

							UpperCAmelCase				: List[Any]											=	prizestrings
							return prizestrings


def 				snake_case_  (      _lowerCAmelCase						:		int = 30						)   ->     int:
							return _calculate(_lowerCAmelCase     ,   absent=0     ,   late=0						)


if __name__ == "__main__":
						print(solution())





