






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer




class 						UpperCAmelCase_  (		tf.keras.layers.Layer    ):


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = "longest"						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, **_SCREAMING_SNAKE_CASE						, )       -> List[Any]:
								super().__init__()
								if use_fast_bert_tokenizer:
										snake_case_ :							List[str]         =				FastBertTokenizer(
										    snake_case_						, token_out_type=tf.intaa						, lower_case_nfd_strip_accents=snake_case_						, **snake_case_ )
								else:
										snake_case_ :							List[str]         =				tf.lookup.StaticVocabularyTable(
										    tf.lookup.KeyValueTensorInitializer(
										        keys=snake_case_						, key_dtype=tf.string						, values=tf.range(tf.size(snake_case_						, out_type=tf.intaa )						, dtype=tf.intaa )						, value_dtype=tf.intaa						, )						, num_oov_buckets=1						, )
										snake_case_ :							Optional[int]         =				BertTokenizerLayer(
										    snake_case_						, token_out_type=tf.intaa						, lower_case=snake_case_						, **snake_case_ )

								snake_case_ :							Tuple         =				vocab_list
								snake_case_ :							int         =				do_lower_case
								snake_case_ :							int         =				cls_token_id or vocab_list.index("[CLS]" )
								snake_case_ :							List[Any]         =				sep_token_id or vocab_list.index("[SEP]" )
								snake_case_ :							List[Any]         =				pad_token_id or vocab_list.index("[PAD]" )
								snake_case_ :							Optional[int]         =				ShrinkLongestTrimmer(max_length - 3						, axis=1 )  # Allow room for special tokens
								snake_case_ :							Any         =				max_length
								snake_case_ :							str         =				padding
								snake_case_ :							Optional[int]         =				truncation
								snake_case_ :							Tuple         =				pad_to_multiple_of
								snake_case_ :							Any         =				return_token_type_ids
								snake_case_ :							Optional[int]         =				return_attention_mask






						@classmethod
						def 					_lowerCAmelCase				(		cls						, _SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> int:  # noqa: F821
								snake_case_ :							int         =				kwargs.pop("do_lower_case"						, snake_case_ )
								snake_case_ :							Optional[int]         =				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								snake_case_ :							str         =				kwargs.pop("cls_token_id"						, snake_case_ )
								snake_case_ :							str         =				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								snake_case_ :							Optional[int]         =				kwargs.pop("sep_token_id"						, snake_case_ )
								snake_case_ :							List[Any]         =				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								snake_case_ :							Tuple         =				kwargs.pop("pad_token_id"						, snake_case_ )
								snake_case_ :							Tuple         =				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								snake_case_ :							Tuple         =				tokenizer.get_vocab()
								snake_case_ :							List[Any]         =				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]						, key=lambda _SCREAMING_SNAKE_CASE : x[1] )
								snake_case_ :							Optional[Any]         =				[entry[0] for entry in vocab]
								return cls(
								    vocab_list=snake_case_						, do_lower_case=snake_case_						, cls_token_id=snake_case_						, sep_token_id=snake_case_						, pad_token_id=snake_case_						, **snake_case_						, )






						@classmethod
						def 					_lowerCAmelCase				(		cls						, _SCREAMING_SNAKE_CASE						, *_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> int:

								try:
										snake_case_ :							List[Any]         =				BertTokenizer.from_pretrained(snake_case_						, *snake_case_						, **snake_case_ )
								except:  # noqa: E722
										from .tokenization_bert_fast import BertTokenizerFast

										snake_case_ :							Dict         =				BertTokenizerFast.from_pretrained(snake_case_						, *snake_case_						, **snake_case_ )
								return cls.from_tokenizer(snake_case_						, **snake_case_ )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Any:

								if self.do_lower_case:
										snake_case_ :							List[Any]         =				case_fold_utfa(snake_case_ )
								snake_case_ :							Dict         =				self.tf_tokenizer.tokenize(snake_case_ )
								return tokens.merge_dims(1						, -1 )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, )       -> str:

								if padding is None:
										snake_case_ :							Any         =				self.padding
								if padding not in ("longest", "max_length"):
										raise ValueError("Padding must be either \'longest\' or \'max_length\'!" )
								if max_length is not None and text_pair is not None:
										# Because we have to instantiate a Trimmer to do it properly
										raise ValueError("max_length cannot be overridden at call time when truncating paired texts!" )
								if max_length is None:
										snake_case_ :							Optional[int]         =				self.max_length
								if truncation is None:
										snake_case_ :							Optional[Any]         =				self.truncation
								if pad_to_multiple_of is None:
										snake_case_ :							List[Any]         =				self.pad_to_multiple_of
								if return_token_type_ids is None:
										snake_case_ :							Optional[Any]         =				self.return_token_type_ids
								if return_attention_mask is None:
										snake_case_ :							Optional[int]         =				self.return_attention_mask
								if not isinstance(snake_case_						, tf.Tensor ):
										snake_case_ :							Dict         =				tf.convert_to_tensor(snake_case_ )
								if text_pair is not None and not isinstance(snake_case_						, tf.Tensor ):
										snake_case_ :							Union[str, Any]         =				tf.convert_to_tensor(snake_case_ )
								if text_pair is not None:
										if text.shape.rank > 1:
												raise ValueError("text argument should not be multidimensional when a text pair is supplied!" )
										if text_pair.shape.rank > 1:
												raise ValueError("text_pair should not be multidimensional!" )
								if text.shape.rank == 2:
										snake_case_ :							Dict         =				text[:, 0], text[:, 1]
								snake_case_ :							str         =				self.unpaired_tokenize(snake_case_ )
								if text_pair is None:  # Unpaired text
										if truncation:
												snake_case_ :							Any         =				text[:, : max_length - 2]  # Allow room for special tokens
										snake_case_ :							Optional[Any]         =				combine_segments(
										    (text,)						, start_of_sequence_id=self.cls_token_id						, end_of_segment_id=self.sep_token_id )
								else:  # Paired text
										snake_case_ :							Optional[int]         =				self.unpaired_tokenize(snake_case_ )
										if truncation:
												snake_case_ :							Optional[Any]         =				self.paired_trimmer.trim([text, text_pair] )
										snake_case_ :							List[str]         =				combine_segments(
										    (text, text_pair)						, start_of_sequence_id=self.cls_token_id						, end_of_segment_id=self.sep_token_id )
								if padding == "longest":
										snake_case_ :							str         =				input_ids.bounding_shape(axis=1 )
										if pad_to_multiple_of is not None:
												# No ceiling division in tensorflow, so we negate floordiv instead
												snake_case_ :							Tuple         =				pad_to_multiple_of * (-tf.math.floordiv(-pad_length						, snake_case_ ))
								else:
										snake_case_ :							Optional[int]         =				max_length

								snake_case_ :							Optional[int]         =				pad_model_inputs(snake_case_						, max_seq_length=snake_case_						, pad_value=self.pad_token_id )
								snake_case_ :							Dict         =				{'input_ids': input_ids}
								if return_attention_mask:
										snake_case_ :							Optional[int]         =				attention_mask
								if return_token_type_ids:
										snake_case_ :							Optional[int]         =				pad_model_inputs(
										    snake_case_						, max_seq_length=snake_case_						, pad_value=self.pad_token_id )
										snake_case_ :							List[Any]         =				token_type_ids
								return output






						def 					_lowerCAmelCase				(		self )       -> Dict:
								return {
								    "vocab_list": self.vocab_list,
								    "do_lower_case": self.do_lower_case,
								    "cls_token_id": self.cls_token_id,
								    "sep_token_id": self.sep_token_id,
								    "pad_token_id": self.pad_token_id,
								}

'''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


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


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      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=1000	,     help='Batch size during training.'	,     )
     parser.add_argument(
         '--vocab_size'	,     type=lowerCamelCase_	,     default=10048	,     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'	,     )

     UpperCAmelCase_							:   str   =      parser.parse_args()
     return args


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict   ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      datasets.load_dataset(args.dataset_name	,     args.dataset_config	,     split='train'   )

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

     # Prepare the trainer.
     UpperCAmelCase_							:   Tuple   =      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!'   )

     UpperCAmelCase_							:   List[str]   =      tokenizer.token_to_id('[CLS]'   )
     UpperCAmelCase_							:   Optional[int]   =      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_							:   Any   =      decoders.Metaspace()

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


if __name__ == "__main__":
 snake_case__     :			Optional[Any]      			=  parse_args()
 main(args)






