"""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


__lowerCamelCase :     Optional[int]          =  logging.getLogger(__name__)




def 				_snake_case     (       ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		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=1_0_0_0       ,			help="Batch size during training."       ,			)
		parser.add_argument(
		    "--vocab_size"       ,			type=lowerCAmelCase       ,			default=1_0_0_4_8       ,			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"       ,			)

		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   parser.parse_args()
		return args




def 				_snake_case     (       lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]    				=   datasets.load_dataset(args.dataset_name       ,			args.dataset_config       ,			split="train"     )

		if args.limit is not None:
				SCREAMING_SNAKE_CASE_  :		Any    				=   min(len(lowerCAmelCase     )       ,			args.limit     )
				SCREAMING_SNAKE_CASE_  :		str    				=   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.
		SCREAMING_SNAKE_CASE_  :		Any    				=   Tokenizer(Unigram()     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   normalizers.Sequence([normalizers.Replace("``"       ,			"\""     ), normalizers.Replace("''"       ,			"\""     )]     )
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   pre_tokenizers.Metaspace()

		# Prepare the trainer.
		SCREAMING_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=lowerCAmelCase     )
		logger.info("Tokenizer training complete!"     )

		SCREAMING_SNAKE_CASE_  :		List[str]    				=   tokenizer.token_to_id("[CLS]"     )
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   tokenizer.token_to_id("[SEP]"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   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),
		    ]       ,			)
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   decoders.Metaspace()

		if args.export_to_hub:
				logger.info("Exporting the trained tokenzier to Hub."     )
				SCREAMING_SNAKE_CASE_  :		str    				=   AlbertTokenizerFast(tokenizer_object=lowerCAmelCase     )
				new_tokenizer.push_to_hub("unigram-tokenizer-dataset"     )


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

from __future__ import annotations






class 		a__						:

					def __init__(   self				:	Union[str, Any],_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   value
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None







class 		a__						:
					A     	=							42

					def __init__(   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   0

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	Node | None,_A				:	int   ):







							"""simple docstring"""






							if node is None:
									return

							if path_sum == self.target:
									self.paths += 1

							if node.left:
									self.depth_first_search(node.left,path_sum + node.left.value   )
							if node.right:
									self.depth_first_search(node.right,path_sum + node.right.value   )






					def     __UpperCamelCase       (   self				:	List[str],_A				:	Node | None,_A				:	int | None = None   ):







							"""simple docstring"""






							if node is None:
									return 0
							if target is not None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   target

							self.depth_first_search(_A,node.value   )
							self.path_sum(node.left   )
							self.path_sum(node.right   )

							return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
