
"""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


_lowercase											=			logging.getLogger(__name__)






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

	A  				=				parser.parse_args()
	return args






def  _snake_case			(						snake_case__				:       List[str]       ):
	A  				=				datasets.load_dataset(args.dataset_name		, args.dataset_config		, split='train'       )

	if args.limit is not None:
		A  				=				min(len(snake_case__       )		, args.limit       )
		A  				=				dataset.select(range(snake_case__       )       )
		logger.info(F'Limiting the dataset to {args.limit} entries.'       )

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

    # Prepare the tokenizer.
	A  				=				Tokenizer(Unigram()       )
	A  				=				normalizers.Sequence([normalizers.Replace('``'		, '"'       ), normalizers.Replace('\'\''		, '"'       )]       )
	A  				=				pre_tokenizers.Metaspace()

	# Prepare the trainer.
	A  				=				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=snake_case__       )
	logger.info('Tokenizer training complete!'       )

	A  				=				tokenizer.token_to_id('[CLS]'       )
	A  				=				tokenizer.token_to_id('[SEP]'       )
	A  				=				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),
	    ]		, )
	A  				=				decoders.Metaspace()

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


if __name__ == "__main__":
		_lowercase											=			parse_args()
		main(args)

"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .image_encoder import PaintByExampleImageEncoder
		from .pipeline_paint_by_example import PaintByExamplePipeline