






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 :    List[Any]								=				{
    "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 :    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
}
__a :    Tuple								=				sorted(arg_to_scheduler.keys())
__a :    Tuple								=				"{" + ", ".join(arg_to_scheduler_choices) + "}"


class  __lowercase (		pl.LightningModule					):







     '''simple docstring'''




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     argparse.Namespace	,		UpperCamelCase_    :     List[Any]=None	,		UpperCamelCase_    :     Dict="base"	,		UpperCamelCase_    :     str=None	,		UpperCamelCase_    :     List[str]=None	,		UpperCamelCase_    :     List[str]=None	,		**UpperCamelCase_    :     Optional[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(UpperCamelCase_			)
         __A             =		0
         __A             =		Path(self.hparams.output_dir			)
         __A             =		self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
             __A             =		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:
             __A             =		config

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




     def 						lowerCAmelCase_       (			self    :     int	,		*UpperCamelCase_    :     Any	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         __A             =		self.model_type.from_pretrained(*UpperCamelCase_	,		**UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         __A             =		self.model
         __A             =		["""bias""", """LayerNorm.weight"""]
         __A             =		[
             {
                 """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:
             __A             =		Adafactor(
                 UpperCamelCase_	,		lr=self.hparams.learning_rate	,		scale_parameter=UpperCamelCase_	,		relative_step=UpperCamelCase_			)

         else:
             __A             =		AdamW(
                 UpperCamelCase_	,		lr=self.hparams.learning_rate	,		eps=self.hparams.adam_epsilon			)
         __A             =		optimizer

         __A             =		self.get_lr_scheduler()

         return [optimizer], [scheduler]




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     str			):


         """simple docstring"""



         return self.validation_step(UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[int]	,		UpperCamelCase_    :     str			):


         """simple docstring"""



         return self.validation_end(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     str			):


         """simple docstring"""



         if mode == "test":
             __A             =		len(self.test_dataloader().dataset			)
         else:
             __A             =		self.get_dataloader("""train"""	,		self.hparams.train_batch_size	,		shuffle=UpperCamelCase_			)
             __A             =		len(self.train_dataloader().dataset			)




     def 						lowerCAmelCase_       (			self    :     List[Any]	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         return self.train_loader




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         return self.get_dataloader("""dev"""	,		self.hparams.eval_batch_size	,		shuffle=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         return self.get_dataloader("""test"""	,		self.hparams.eval_batch_size	,		shuffle=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     Optional[int]			):


         """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 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Dict[str, Any]			):


         """simple docstring"""



         __A             =		self.output_dir.joinpath("""best_tfmr"""			)
         __A             =		self.step_count
         self.model.save_pretrained(UpperCamelCase_			)
         self.tokenizer.save_pretrained(UpperCamelCase_			)





     @staticmethod
     def 						lowerCAmelCase_       (			UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     Optional[int]			):


         """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=""""""	,		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  __lowercase (		pl.Callback					):







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int			):


         """simple docstring"""



         __A             =		trainer.lr_schedulers[0]["""scheduler"""]
         __A             =		{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
         pl_module.logger.log_metrics(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     pl.Trainer	,		UpperCamelCase_    :     pl.LightningModule			):


         """simple docstring"""



         rank_zero_info("""***** Validation results *****"""			)
         __A             =		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 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     pl.Trainer	,		UpperCamelCase_    :     pl.LightningModule			):


         """simple docstring"""



         rank_zero_info("""***** Test results *****"""			)
         __A             =		trainer.callback_metrics
         # Log and save results to file
         __A             =		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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     Optional[Any]      )  -> None:





    """simple docstring"""

    parser.add_argument(
        """--output_dir"""     ,			default=__lowercase     ,			type=__lowercase     ,			required=__lowercase     ,			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=__lowercase     ,			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=__lowercase      )
    parser.add_argument("""--max_grad_norm"""     ,			dest="""gradient_clip_val"""     ,			default=1.0     ,			type=__lowercase     ,			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=__lowercase     ,			default=1     ,			help="""Number of updates steps to accumulate before performing a backward/update pass."""     ,			)
    parser.add_argument("""--seed"""     ,			type=__lowercase     ,			default=4_2     ,			help="""random seed for initialization"""      )
    parser.add_argument(
        """--data_dir"""     ,			default=__lowercase     ,			type=__lowercase     ,			required=__lowercase     ,			help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""     ,			)






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     BaseTransformer     ,			__lowercase				:     argparse.Namespace     ,			__lowercase				:     Optional[Any]=None     ,			__lowercase				:     int=True     ,			__lowercase				:     Dict=[]     ,			__lowercase				:     List[Any]=None     ,			__lowercase				:     Optional[Any]=None     ,			**__lowercase				:     Optional[Any]     ,			)  -> List[str]:





    """simple docstring"""

    pl.seed_everything(args.seed      )

    # init model
    __A             =		Path(model.hparams.output_dir      )
    odir.mkdir(exist_ok=__lowercase      )

    # add custom checkpoints
    if checkpoint_callback is None:
        __A             =		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(__lowercase      )
    if logging_callback is None:
        __A             =		LoggingCallback()

    __A             =		{}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        __A             =		1_6
        __A             =		args.fpaa_opt_level

    if args.gpus > 1:
        __A             =		"""ddp"""

    __A             =		args.accumulate_grad_batches
    __A             =		extra_train_kwargs.get("""accelerator"""     ,			__lowercase      )
    __A             =		extra_train_kwargs.get("""profiler"""     ,			__lowercase      )

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

    if args.do_train:
        trainer.fit(__lowercase      )

    return trainer












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     int      )  -> int:





    """simple docstring"""

    __A             =		1.0
    for i in range(1     ,			r + 1      ):
        ret *= (n + 1 - i) / i
    return round(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return choose(8 + n     ,			__lowercase      ) + choose(9 + n     ,			__lowercase      ) - 1_0



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return sum(non_bouncy_exact(__lowercase      ) for i in range(1     ,			n + 1      )      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int = 1_0_0      )  -> int:





    """simple docstring"""

    return non_bouncy_upto(__lowercase      )


if __name__ == "__main__":
  print(f"""{solution() = }""")




