


from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			"MarkupLMFeatureExtractor"
					SCREAMING_SNAKE_CASE_		:       Union[str, Any]								=			("MarkupLMTokenizer", "MarkupLMTokenizerFast")
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			True
					def __call__(						self       :						Any	,      _lowerCAmelCase       :						Any=None	,      _lowerCAmelCase       :						str=None	,      _lowerCAmelCase       :						Tuple=None	,      _lowerCAmelCase       :						Union[str, Any]=None	,      _lowerCAmelCase       :						Union[str, Any]=None	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Union[bool, str, PaddingStrategy] = False	,      _lowerCAmelCase       :						Union[bool, str, TruncationStrategy] = None	,      _lowerCAmelCase       :						Optional[int] = None	,      _lowerCAmelCase       :						int = 0	,      _lowerCAmelCase       :						Optional[int] = None	,      _lowerCAmelCase       :						Optional[bool] = None	,      _lowerCAmelCase       :						Optional[bool] = None	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Optional[Union[str, TensorType]] = None	,      **_lowerCAmelCase       :						Tuple	,      )							->					BatchEncoding:



											'''simple docstring'''





											if self.parse_html:
																	if html_strings is None:
																							raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'  )

																	if nodes is not None or xpaths is not None or node_labels is not None:
																							raise ValueError(
																							    'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'  )

																	__magic_name__					=      self.feature_extractor(_lowerCAmelCase  )
																	__magic_name__					=      features['nodes']
																	__magic_name__					=      features['xpaths']
											else:
																	if html_strings is not None:
																							raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'  )
																	if nodes is None or xpaths is None:
																							raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'  )

        # # second, apply the tokenizer
											if questions is not None and self.parse_html:
																	if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																							__magic_name__					=      [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

											__magic_name__					=      self.tokenizer(
											    text=questions if questions is not None else nodes	,      text_pair=nodes if questions is not None else None	,      xpaths=_lowerCAmelCase	,      node_labels=_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase	,      padding=_lowerCAmelCase	,      truncation=_lowerCAmelCase	,      max_length=_lowerCAmelCase	,      stride=_lowerCAmelCase	,      pad_to_multiple_of=_lowerCAmelCase	,      return_token_type_ids=_lowerCAmelCase	,      return_attention_mask=_lowerCAmelCase	,      return_overflowing_tokens=_lowerCAmelCase	,      return_special_tokens_mask=_lowerCAmelCase	,      return_offsets_mapping=_lowerCAmelCase	,      return_length=_lowerCAmelCase	,      verbose=_lowerCAmelCase	,      return_tensors=_lowerCAmelCase	,      **_lowerCAmelCase	,      )

											return encoded_inputs
					def 	a			(						self       :						str	,      *_lowerCAmelCase       :						int	,      **_lowerCAmelCase       :						List[Any]  )							->					Dict:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase	,      **_lowerCAmelCase  )
					def 	a			(						self       :						Optional[int]	,      *_lowerCAmelCase       :						Optional[int]	,      **_lowerCAmelCase       :						int  )							->					Tuple:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase	,      **_lowerCAmelCase  )







					@property
					def 	a			(						self       :						Optional[Any]  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      self.tokenizer.model_input_names
											return tokenizer_input_names





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			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      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=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      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'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      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.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      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!'  )

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

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


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

