




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


__lowerCAmelCase		:    Tuple	      =		logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__lowerCAmelCase		:    List[str]	      =		{
    '''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
__lowerCAmelCase		:    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
}
__lowerCAmelCase		:    Dict	      =		sorted(arg_to_scheduler.keys())
__lowerCAmelCase		:    List[Any]	      =		'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class    snake_case__							(pl.LightningModule       ):
				"""simple docstring"""



				def __init__(				self    :					Optional[Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					List[Any]=None ,				__lowerCamelCase    :					List[Any]="base" ,				__lowerCamelCase    :					Optional[int]=None ,				__lowerCamelCase    :					Optional[Any]=None ,				__lowerCamelCase    :					List[str]=None ,				**__lowerCamelCase    :					Union[str, Any] ,				) ->					Optional[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(__SCREAMING_SNAKE_CASE							)
							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=__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE ,				)
							else:
										a   =      config

							a   =      ("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:
										a   =      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:
										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=__SCREAMING_SNAKE_CASE ,				)
							else:
										a   =      model





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Tuple:
							a   =      self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Any:
							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 							__UpperCAmelCase							(				self    :					Dict							) ->					Dict:
							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(
										    __SCREAMING_SNAKE_CASE ,				lr=self.hparams.learning_rate ,				scale_parameter=__SCREAMING_SNAKE_CASE ,				relative_step=__SCREAMING_SNAKE_CASE							)

							else:
										a   =      AdamW(
										    __SCREAMING_SNAKE_CASE ,				lr=self.hparams.learning_rate ,				eps=self.hparams.adam_epsilon							)
							a   =      optimizer

							a   =      self.get_lr_scheduler()

							return [optimizer], [scheduler]





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[Any]							) ->					Any:
							return self.validation_step(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Tuple							) ->					List[str]:
							return self.validation_end(__SCREAMING_SNAKE_CASE							)





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					int:
							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 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Dict							) ->					str:




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





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					int = False							) ->					str:
							raise NotImplementedError("You must implement this for your task"							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
							return self.train_loader





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[int]:
							return self.get_dataloader("dev" ,				self.hparams.eval_batch_size ,				shuffle=__SCREAMING_SNAKE_CASE							)





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





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Union[str, Any]							) ->					Optional[Any]:
							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    :					str ,				__lowerCamelCase    :					int							) ->					None:
							a   =      self.output_dir.joinpath("best_tfmr"							)
							a   =      self.step_count
							self.model.save_pretrained(__SCREAMING_SNAKE_CASE							)
							self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE							)





				@staticmethod
				def 							__UpperCAmelCase							(				__lowerCamelCase    :					int ,				__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							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="" ,				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    snake_case__							(pl.Callback       ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					List[Any]							) ->					Tuple:
							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(__SCREAMING_SNAKE_CASE							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					List[str]							) ->					Optional[int]:
							rank_zero_info("***** Validation results *****"							)
							a   =      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    :					List[Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[Any]							) ->					Optional[int]:
							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(__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       __magic_name__    (			A		: Optional[int],  A		: Optional[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       __magic_name__    (			A		: BaseTransformer,  A		: argparse.Namespace,  A		: Any=None,  A		: Union[str, Any]=True,  A		: Union[str, Any]=[],  A		: int=None,  A		: Any=None,  **A		: Tuple,  ):






			'''simple docstring'''







			pl.seed_everything(args.seed       )

			# init model
			a   =      Path(model.hparams.output_dir       )
			odir.mkdir(exist_ok=_lowerCamelCase       )

			# 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(_lowerCamelCase       )
			if logging_callback is None:
						a   =      LoggingCallback()

			a   =      {}

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

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

			a   =      args.accumulate_grad_batches
			a   =      extra_train_kwargs.get("accelerator",  _lowerCamelCase       )
			a   =      extra_train_kwargs.get("profiler",  _lowerCamelCase       )

			a   =      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












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






