



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

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase					:				Optional[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
lowerCAmelCase					:				Optional[int]   	={
    '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					:				int   	=sorted(arg_to_scheduler.keys())
lowerCAmelCase					:				Union[str, Any]   	='{' + ', '.join(arg_to_scheduler_choices) + '}'






class 	_a			(      pl.LightningModule  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_=None    ,  lowercase_="base"    ,  lowercase_=None    ,  lowercase_=None    ,  lowercase_=None    ,  **lowercase_    ,  )	->					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(lowercase_ )
									lowerCAmelCase	:   Union[str, Any]					     =					0
									lowerCAmelCase	:   Any					     =					Path(self.hparams.output_dir )
									lowerCAmelCase	:   Dict					     =					self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
															lowerCAmelCase	:   Tuple					     =					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=lowercase_    ,  **lowercase_    ,  )
									else:
															lowerCAmelCase	:   PretrainedConfig					     =					config

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

									if tokenizer is None:
															lowerCAmelCase	:   List[Any]					     =					AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,  cache_dir=lowercase_    ,  )
									else:
															lowerCAmelCase	:   PreTrainedTokenizer					     =					tokenizer
									lowerCAmelCase	:   List[str]					     =					MODEL_MODES[mode]
									if model is None:
															lowerCAmelCase	:   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=lowercase_    ,  )
									else:
															lowerCAmelCase	:   List[str]					     =					model






			def 							_snake_case   (    self    ,  *lowercase_    ,  **lowercase_ )	->					Any:
									lowerCAmelCase	:   List[str]					     =					self.model_type.from_pretrained(*lowercase_    ,  **lowercase_ )






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






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Optional[Any]					     =					self.model
									lowerCAmelCase	:   Union[str, Any]					     =					["""bias""", """LayerNorm.weight"""]
									lowerCAmelCase	:   Optional[Any]					     =					[
									    {
									        """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	:   Tuple					     =					Adafactor(
															    lowercase_    ,  lr=self.hparams.learning_rate    ,  scale_parameter=lowercase_    ,  relative_step=lowercase_ )

									else:
															lowerCAmelCase	:   List[Any]					     =					AdamW(
															    lowercase_    ,  lr=self.hparams.learning_rate    ,  eps=self.hparams.adam_epsilon )
									lowerCAmelCase	:   str					     =					optimizer

									lowerCAmelCase	:   Dict					     =					self.get_lr_scheduler()

									return [optimizer], [scheduler]






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Optional[Any]:
									return self.validation_step(lowercase_    ,  lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_ )	->					Dict:
									return self.validation_end(lowercase_ )






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   List[Any]					     =					max(1    ,  self.hparams.gpus )  # TODO: consider num_tpu_cores
									lowerCAmelCase	:   Optional[int]					     =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






			def 							_snake_case   (    self    ,  lowercase_ )	->					Optional[int]:
									if mode == "test":
															lowerCAmelCase	:   List[Any]					     =					len(self.test_dataloader().dataset )
									else:
															lowerCAmelCase	:   Any					     =					self.get_dataloader("""train"""    ,  self.hparams.train_batch_size    ,  shuffle=lowercase_ )
															lowerCAmelCase	:   int					     =					len(self.train_dataloader().dataset )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_ = False )	->					List[Any]:
									raise NotImplementedError("""You must implement this for your task""" )






			def 							_snake_case   (    self )	->					Any:
									return self.train_loader






			def 							_snake_case   (    self )	->					List[Any]:
									return self.get_dataloader("""dev"""    ,  self.hparams.eval_batch_size    ,  shuffle=lowercase_ )






			def 							_snake_case   (    self )	->					int:
									return self.get_dataloader("""test"""    ,  self.hparams.eval_batch_size    ,  shuffle=lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_ )	->					List[Any]:
									return os.path.join(
									    self.hparams.data_dir    ,  """cached_{}_{}_{}""".format(
									        lowercase_    ,  list(filter(lowercase_    ,  self.hparams.model_name_or_path.split("""/""" ) ) ).pop()    ,  str(self.hparams.max_seq_length )    ,  )    ,  )






			@pl.utilities.rank_zero_only
			def 							_snake_case   (    self    ,  lowercase_ )	->					None:
									lowerCAmelCase	:   Dict					     =					self.output_dir.joinpath("""best_tfmr""" )
									lowerCAmelCase	:   Tuple					     =					self.step_count
									self.model.save_pretrained(lowercase_ )
									self.tokenizer.save_pretrained(lowercase_ )






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






class 	_a			(      pl.Callback  ):






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									lowerCAmelCase	:   Dict					     =					trainer.lr_schedulers[0]["""scheduler"""]
									lowerCAmelCase	:   Optional[int]					     =					{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
									pl_module.logger.log_metrics(lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Union[str, Any]:
									rank_zero_info("""***** Validation results *****""" )
									lowerCAmelCase	:   str					     =					trainer.callback_metrics
									# Log results
									for key in sorted(lowercase_ ):
															if key not in ["log", "progress_bar"]:
																					rank_zero_info("""{} = {}\n""".format(lowercase_    ,  str(metrics[key] ) ) )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					str:
									rank_zero_info("""***** Test results *****""" )
									lowerCAmelCase	:   Tuple					     =					trainer.callback_metrics
									# Log and save results to file
									lowerCAmelCase	:   int					     =					os.path.join(pl_module.hparams.output_dir    ,  """test_results.txt""" )
									with open(lowercase_    ,  """w""" ) as writer:
															for key in sorted(lowercase_ ):
																					if key not in ["log", "progress_bar"]:
																											rank_zero_info("""{} = {}\n""".format(lowercase_    ,  str(metrics[key] ) ) )
																											writer.write("""{} = {}\n""".format(lowercase_    ,  str(metrics[key] ) ) )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




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







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=None			,SCREAMING_SNAKE_CASE__=True			,SCREAMING_SNAKE_CASE__=[]			,SCREAMING_SNAKE_CASE__=None			,SCREAMING_SNAKE_CASE__=None			,**SCREAMING_SNAKE_CASE__			,):


						'''simple docstring'''




						pl.seed_everything(args.seed  )

						# init model
						lowerCAmelCase	:   List[str]					     =					Path(model.hparams.output_dir  )
						odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )

						# add custom checkpoints
						if checkpoint_callback is None:
												lowerCAmelCase	:   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(SCREAMING_SNAKE_CASE__  )
						if logging_callback is None:
												lowerCAmelCase	:   int					     =					LoggingCallback()

						lowerCAmelCase	:   Union[str, Any]					     =					{}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												lowerCAmelCase	:   Optional[int]					     =					1_6
												lowerCAmelCase	:   Optional[Any]					     =					args.fpaa_opt_level

						if args.gpus > 1:
												lowerCAmelCase	:   str					     =					"""ddp"""

						lowerCAmelCase	:   Union[str, Any]					     =					args.accumulate_grad_batches
						lowerCAmelCase	:   str					     =					extra_train_kwargs.get("""accelerator"""			,SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Any					     =					extra_train_kwargs.get("""profiler"""			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   List[str]					     =					pl.Trainer.from_argparse_args(
						    SCREAMING_SNAKE_CASE__			,weights_summary=SCREAMING_SNAKE_CASE__			,callbacks=[logging_callback] + extra_callbacks			,logger=SCREAMING_SNAKE_CASE__			,checkpoint_callback=SCREAMING_SNAKE_CASE__			,**SCREAMING_SNAKE_CASE__			,)

						if args.do_train:
												trainer.fit(SCREAMING_SNAKE_CASE__  )

						return trainer






def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					str(SCREAMING_SNAKE_CASE__  ).strip()
						if not bin_string:
												raise ValueError("""Empty string was passed to the function"""  )
						lowerCAmelCase	:   Optional[int]					     =					bin_string[0] == """-"""
						if is_negative:
												lowerCAmelCase	:   Tuple					     =					bin_string[1:]
						if not all(char in """01""" for char in bin_string  ):
												raise ValueError("""Non-binary value was passed to the function"""  )
						lowerCAmelCase	:   Union[str, Any]					     =					0
						for char in bin_string:
												lowerCAmelCase	:   int					     =					2 * decimal_number + int(SCREAMING_SNAKE_CASE__  )
						return -decimal_number if is_negative else decimal_number


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

		testmod()

