

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       =					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_A       =					{
    "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       =					{
    "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       =					sorted(arg_to_scheduler.keys())
_A       =					"{" + ", ".join(arg_to_scheduler_choices) + "}"





class      _lowerCAmelCase (     pl.LightningModule	):




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase=None    ,    _UpperCamelCase="base"    ,    _UpperCamelCase=None    ,    _UpperCamelCase=None    ,    _UpperCamelCase=None    ,    **_UpperCamelCase    ,    )    ->				str:
							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 					__a						( self    ,    *_UpperCamelCase    ,    **_UpperCamelCase					)    ->				Tuple:
							lowerCAmelCase_      =     self.model_type.from_pretrained(*_UpperCamelCase    ,    **_UpperCamelCase					)




				def 					__a						( self					)    ->				int:
							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					)    ->				str:
							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					)],
							        "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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							return self.validation_step(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							return self.validation_end(_UpperCamelCase					)




				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    ,    _UpperCamelCase					)    ->				Tuple:
							if mode == "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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = False					)    ->				int:
							raise NotImplementedError("You must implement this for your task"					)




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




				def 					__a						( self					)    ->				Any:
							return self.get_dataloader("dev"    ,    self.hparams.eval_batch_size    ,    shuffle=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							return self.get_dataloader("test"    ,    self.hparams.eval_batch_size    ,    shuffle=_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Any:
							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 					__a						( self    ,    _UpperCamelCase					)    ->				None:
							lowerCAmelCase_      =     self.output_dir.joinpath("best_tfmr"					)
							lowerCAmelCase_      =     self.step_count
							self.model.save_pretrained(_UpperCamelCase					)
							self.tokenizer.save_pretrained(_UpperCamelCase					)




				@staticmethod
				def 					__a						( _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							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      _lowerCAmelCase (     pl.Callback	):




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Union[str, Any]:
							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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Union[str, 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(_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        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			parser.add_argument(
			    "--output_dir"   ,							default=__lowerCAmelCase   ,							type=__lowerCAmelCase   ,							required=__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=__lowerCAmelCase   ,							type=__lowerCAmelCase   ,							required=__lowerCAmelCase   ,							help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."   ,							)






def        lowerCamelCase__    (    __lowerCAmelCase  :  BaseTransformer   ,							__lowerCAmelCase  :  argparse.Namespace   ,							__lowerCAmelCase  :  Optional[int]=None   ,							__lowerCAmelCase  :  Optional[Any]=True   ,							__lowerCAmelCase  :  int=[]   ,							__lowerCAmelCase  :  List[Any]=None   ,							__lowerCAmelCase  :  Any=None   ,							**__lowerCAmelCase  :  Tuple   ,							):




			"""simple docstring"""


			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_      =     {}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						lowerCAmelCase_      =     16
						lowerCAmelCase_      =     args.fpaa_opt_level

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

			lowerCAmelCase_      =     args.accumulate_grad_batches
			lowerCAmelCase_      =     extra_train_kwargs.get("accelerator"   ,							__lowerCAmelCase    )
			lowerCAmelCase_      =     extra_train_kwargs.get("profiler"   ,							__lowerCAmelCase    )

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

			if args.do_train:
						trainer.fit(__lowerCAmelCase    )

			return trainer





from __future__ import annotations






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			assert n > 1 and a > 0 and greatest_common_divisor(__lowerCAmelCase   ,							__lowerCAmelCase    ) == 1
			((lowerCAmelCase_)     ,  (lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_gcd(__lowerCAmelCase   ,							__lowerCAmelCase    )  # Implemented below
			lowerCAmelCase_      =     (b * s) % n
			return x






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			((lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_euclid(__lowerCAmelCase   ,							__lowerCAmelCase    )  # Implemented below
			if b < 0:
						lowerCAmelCase_      =     (b % n + n) % n
			return b






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     invert_modulo(__lowerCAmelCase   ,							__lowerCAmelCase    )
			lowerCAmelCase_      =     (b * s) % n
			return x






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			assert a >= 0 and b >= 0

			if b == 0:
						lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     a, 1, 0
			else:
						((lowerCAmelCase_)     ,  (lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_gcd(__lowerCAmelCase   ,							a % b    )
						lowerCAmelCase_      =     q
						lowerCAmelCase_      =     p - q * (a // b)

			assert a % d == 0 and b % d == 0
			assert d == a * x + b * y

			return (d, x, y)






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if b == 0:
						return (1, 0)
			((lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_euclid(__lowerCAmelCase   ,							a % b    )
			lowerCAmelCase_      =     a // b
			return (y, x - k * y)






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if a < b:
						lowerCAmelCase_     ,  lowerCAmelCase_      =     b, a

			while a % b != 0:
						lowerCAmelCase_     ,  lowerCAmelCase_      =     b, a % b

			return b


if __name__ == "__main__":
			from doctest import testmod

			testmod(name="modular_division", verbose=True)
			testmod(name="modular_division2", verbose=True)
			testmod(name="invert_modulo", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="extended_euclid", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)



