






'''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


_UpperCamelCase		:		Dict								=			logging.getLogger(__name__)
def __UpperCAmelCase (			)  -> Dict:
			UpperCAmelCase_     :   Dict       =   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=1_0_0_0	,   help='''Batch size during training.'''	,   )
			parser.add_argument(
			    '''--vocab_size'''	,   type=A	,   default=1_0_0_4_8	,   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'''	,   )

			UpperCAmelCase_     :   Optional[Any]       =   parser.parse_args()
			return args
def __UpperCAmelCase (			A			:	str						)  -> Dict:
			UpperCAmelCase_     :   List[str]       =   datasets.load_dataset(args.dataset_name	,   args.dataset_config	,   split='''train'''						)

			if args.limit is not None:
						UpperCAmelCase_     :   List[Any]       =   min(len(A						)	,   args.limit						)
						UpperCAmelCase_     :   Tuple       =   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.
			UpperCAmelCase_     :   Any       =   Tokenizer(Unigram()						)
			UpperCAmelCase_     :   Dict       =   normalizers.Sequence([normalizers.Replace('''``'''	,   '''"'''						), normalizers.Replace('''\'\''''	,   '''"'''						)]						)
			UpperCAmelCase_     :   Any       =   pre_tokenizers.Metaspace()

			# Prepare the trainer.
			UpperCAmelCase_     :   List[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!'''						)

			UpperCAmelCase_     :   int       =   tokenizer.token_to_id('''[CLS]'''						)
			UpperCAmelCase_     :   Optional[Any]       =   tokenizer.token_to_id('''[SEP]'''						)
			UpperCAmelCase_     :   Optional[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),
			    ]	,   )
			UpperCAmelCase_     :   Dict       =   decoders.Metaspace()

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


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










'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								if isinstance(image[0]							,				PIL.Image.Image     ):
											return np.stack([np.array(_A     ) for i in image]							,				axis=0     )
								elif isinstance(_A							,				torch.Tensor     ):
											return image.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								return image




					def 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								UpperCAmelCase_     :   List[Any]       =   image_processor.numpy_to_pil(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6





					def 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6


