'''simple docstring'''


import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


snake_case__	:     Any      =    logging.getLogger(__name__)


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
     parser.add_argument(
         '--dataset_name'      ,	type=lowerCamelCase_      ,	default='wikitext'      ,	help='Name of the training. Explore datasets at: hf.co/datasets.'      ,	)
     parser.add_argument(
         '--dataset_config'      ,	type=lowerCamelCase_      ,	default='wikitext-103-raw-v1'      ,	help='Configuration name of the dataset.'  )
     parser.add_argument(
         '--batch_size'      ,	type=lowerCamelCase_      ,	default=1000      ,	help='Batch size during training.'      ,	)
     parser.add_argument(
         '--vocab_size'      ,	type=lowerCamelCase_      ,	default=10048      ,	help='Size of the desired vocabulary.'      ,	)
     parser.add_argument(
         '--limit'      ,	default=lowerCamelCase_      ,	type=lowerCamelCase_      ,	help='Limit the number of shards (used for debugging).'      ,	)
     parser.add_argument(
         '--export_to_hub'      ,	action='store_true'      ,	)

     UpperCAmelCase_				:  Tuple  = parser.parse_args()
     return args


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = datasets.load_dataset(args.dataset_name      ,	args.dataset_config      ,	split='train'  )

     if args.limit is not None:
          UpperCAmelCase_				:  Dict  = min(len(lowerCamelCase_  )      ,	args.limit  )
          UpperCAmelCase_				:  Optional[Any]  = dataset.select(range(lowerCamelCase_  )  )
          logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

     def batch_iterator():
          for i in range(0      ,	len(lowerCamelCase_  )      ,	args.batch_size  ):
               yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
     UpperCAmelCase_				:  Optional[int]  = Tokenizer(Unigram()  )
     UpperCAmelCase_				:  Optional[int]  = normalizers.Sequence([normalizers.Replace('``'      ,	'"'  ), normalizers.Replace('\'\''      ,	'"'  )]  )
     UpperCAmelCase_				:  Union[str, Any]  = pre_tokenizers.Metaspace()

     # Prepare the trainer.
     UpperCAmelCase_				:  str  = UnigramTrainer(
         unk_token='<unk>'      ,	special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']      ,	vocab_size=args.vocab_size      ,	)

     logger.info('Training the tokenizer.'  )
     tokenizer.train_from_iterator(batch_iterator()      ,	trainer=lowerCamelCase_  )
     logger.info('Tokenizer training complete!'  )

     UpperCAmelCase_				:  Optional[Any]  = tokenizer.token_to_id('[CLS]'  )
     UpperCAmelCase_				:  List[str]  = tokenizer.token_to_id('[SEP]'  )
     UpperCAmelCase_				:  List[str]  = processors.TemplateProcessing(
         single='[CLS]:0 $A:0 [SEP]:0'      ,	pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'      ,	special_tokens=[
             ('[CLS]', cls_token_id),
             ('[SEP]', sep_token_id),
         ]      ,	)
     UpperCAmelCase_				:  List[str]  = decoders.Metaspace()

     if args.export_to_hub:
          logger.info('Exporting the trained tokenzier to Hub.'  )
          UpperCAmelCase_				:  Tuple  = AlbertTokenizerFast(tokenizer_object=lowerCamelCase_  )
          new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
 snake_case__	:     Optional[int]      =    parse_args()
 main(args)








import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





