






"""simple docstring"""



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_rag import save_json




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any      )		->Tuple:
						A__						:  List[str]		  =  filter(lambda UpperCAmelCase__      : p.requires_grad,						model.parameters()      )
						A__						:  Union[str, Any]		  =  sum([np.prod(p.size()      ) for p in model_parameters]      )
						return params


A_    =							logging.getLogger(__name__)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Dict      )		->str:
						if metric == "rouge2":
												A__						:  str		  =  """{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												A__						:  Union[str, Any]		  =  """{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "em":
												A__						:  Tuple		  =  """{val_avg_em:.4f}-{step_count}"""
						elif metric == "loss":
												A__						:  Dict		  =  """{val_avg_loss:.4f}-{step_count}"""
						else:
												raise NotImplementedError(
												    f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
												    """ function."""      )

						A__						:  Any		  =  ModelCheckpoint(
						    dirpath=UpperCAmelCase__,						filename=UpperCAmelCase__,						monitor=f'val_{metric}',						mode="""max""",						save_top_k=1,						every_n_epochs=1,						)

						return checkpoint_callback




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					str      )		->Optional[int]:
						return EarlyStopping(
						    monitor=f'val_{metric}',						mode="""min""" if """loss""" in metric else """max""",						patience=UpperCAmelCase__,						verbose=UpperCAmelCase__,						)






class 							__SCREAMING_SNAKE_CASE						(				pl.Callback		):


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  List[Any]		  =  {F'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
													pl_module.logger.log_metrics(snake_case							)


							@rank_zero_only
							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						pl.Trainer			,    snake_case      :						pl.LightningModule			,    snake_case      :						str			,    snake_case      :						int=True							):
													'''simple docstring'''





													logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'							)
													A__						:  Any		  =  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
													A__						:  Optional[Any]		  =  Path(pl_module.hparams.output_dir							)
													if type_path == "test":
																			A__						:  List[str]		  =  od / """test_results.txt"""
																			A__						:  Dict		  =  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.
																			A__						:  Optional[Any]		  =  od / F'{type_path}_results/{trainer.global_step:05d}.txt'
																			A__						:  Optional[int]		  =  od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
																			results_file.parent.mkdir(exist_ok=snake_case							)
																			generations_file.parent.mkdir(exist_ok=snake_case							)
													with open(snake_case			,    """a+"""							) as writer:
																			for key in sorted(snake_case							):
																									if key in ["log", "progress_bar", "preds"]:
																															continue
																									A__						:  List[Any]		  =  metrics[key]
																									if isinstance(snake_case			,    torch.Tensor							):
																															A__						:  Any		  =  val.item()
																									A__						:  Tuple		  =  F'{key}: {val:.6f}\n'
																									writer.write(snake_case							)

													if not save_generations:
																			return

													if "preds" in metrics:
																			A__						:  Optional[int]		  =  """\n""".join(metrics["""preds"""]							)
																			generations_file.open("""w+"""							).write(snake_case							)


							@rank_zero_only
							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Any			,    snake_case      :						Dict							):
													'''simple docstring'''





													try:
																			A__						:  Tuple		  =  pl_module.model.model.num_parameters()
													except AttributeError:
																			A__						:  Union[str, Any]		  =  pl_module.model.num_parameters()

													A__						:  Optional[int]		  =  count_trainable_parameters(snake_case							)
													# 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 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						pl.Trainer			,    snake_case      :						pl.LightningModule							):
													'''simple docstring'''





													save_json(pl_module.metrics			,    pl_module.metrics_save_path							)
													return self._write_logs(snake_case			,    snake_case			,    """test"""							)


							@rank_zero_only
							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						pl.Trainer			,    snake_case      :						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")














"""simple docstring"""



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->str:
						A__						:  Tuple		  =  {}
						A__						:  Union[str, Any]		  =  tokenizer(example["""content"""],						truncation=UpperCAmelCase__      )["""input_ids"""]
						A__						:  Any		  =  len(example["""content"""]      ) / len(output["""input_ids"""]      )
						return output


A_    =							HfArgumentParser(PretokenizationArguments)
A_    =							parser.parse_args()
if args.num_workers is None:
						A_    =							multiprocessing.cpu_count()
A_    =							AutoTokenizer.from_pretrained(args.tokenizer_dir)

A_    =							time.time()
A_    =							load_dataset(args.dataset_name, split='''train''')
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

A_    =							time.time()
A_    =							ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')

A_    =							time.time()
ds.push_to_hub(args.tokenized_data_repo)

print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')






