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


lowerCAmelCase__					:       Dict		=       logging.getLogger(__name__)
def 			UpperCamelCase__						(    )       -> Tuple:
				snake_case__ :     Tuple 							=				argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'   )
				parser.add_argument(
				    '--dataset_name'      ,			type=A__      ,			default='wikitext'      ,			help='Name of the training. Explore datasets at: hf.co/datasets.'      ,			)
				parser.add_argument(
				    '--dataset_config'      ,			type=A__      ,			default='wikitext-103-raw-v1'      ,			help='Configuration name of the dataset.'   )
				parser.add_argument(
				    '--batch_size'      ,			type=A__      ,			default=1000      ,			help='Batch size during training.'      ,			)
				parser.add_argument(
				    '--vocab_size'      ,			type=A__      ,			default=1_0048      ,			help='Size of the desired vocabulary.'      ,			)
				parser.add_argument(
				    '--limit'      ,			default=A__      ,			type=A__      ,			help='Limit the number of shards (used for debugging).'      ,			)
				parser.add_argument(
				    '--export_to_hub'      ,			action='store_true'      ,			)

				snake_case__ :     List[Any] 							=				parser.parse_args()
				return args
def 			UpperCamelCase__						(    A__   )       -> List[str]:
				snake_case__ :     List[Any] 							=				datasets.load_dataset(args.dataset_name      ,			args.dataset_config      ,			split='train'   )

				if args.limit is not None:
								snake_case__ :     Optional[Any] 							=				min(len(A__   )      ,			args.limit   )
								snake_case__ :     str 							=				dataset.select(range(A__   )   )
								logger.info(F"""Limiting the dataset to {args.limit} entries."""   )

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

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

				# Prepare the trainer.
				snake_case__ :     Union[str, Any] 							=				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=A__   )
				logger.info('Tokenizer training complete!'   )

				snake_case__ :     str 							=				tokenizer.token_to_id('[CLS]'   )
				snake_case__ :     Optional[int] 							=				tokenizer.token_to_id('[SEP]'   )
				snake_case__ :     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),
				    ]      ,			)
				snake_case__ :     int 							=				decoders.Metaspace()

				if args.export_to_hub:
								logger.info('Exporting the trained tokenzier to Hub.'   )
								snake_case__ :     List[Any] 							=				AlbertTokenizerFast(tokenizer_object=A__   )
								new_tokenizer.push_to_hub('unigram-tokenizer-dataset'   )


if __name__ == "__main__":
				lowerCAmelCase__					:       int		=       parse_args()
				main(args)



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment





class 			__snake_case  (      unittest.TestCase     ):




					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				inspect.getfile(accelerate.test_utils		)
									snake_case__ :     Tuple 							=				os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ['scripts', 'test_sync.py']		)




					@require_cpu
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									debug_launcher(test_sync.main   ,	num_processes=1		)




					@require_cpu
					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									debug_launcher(test_sync.main		)




					@require_single_gpu
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									test_sync.main()




					@require_multi_gpu
					def       __a      (       self		)					->  str:



									'''simple docstring'''







									print(F"""Found {torch.cuda.device_count()} devices."""		)
									snake_case__ :     Any 							=				['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
									with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__UpperCamelCase   ,	env=os.environ.copy()		)


