'''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__	:     Any      =    logging.getLogger(__name__)


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  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=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_				:  Tuple  = parser.parse_args()
     return args


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = datasets.load_dataset(args.dataset_name      ,	args.dataset_config      ,	split='train'  )

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

     # Prepare the trainer.
     UpperCAmelCase_				:  str  = 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_				:  Optional[Any]  = tokenizer.token_to_id('[CLS]'  )
     UpperCAmelCase_				:  List[str]  = tokenizer.token_to_id('[SEP]'  )
     UpperCAmelCase_				:  List[str]  = 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_				:  List[str]  = decoders.Metaspace()

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


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












'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



