




lowercase_		=							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]





class 		A__				:






	def __init__(	self				,     lowerCamelCase						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							name
						__magic_name__					:							Optional[Any]		   =							[]
						__magic_name__					:							Dict		   =							[]






	def  lowercase      (	self				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						self.inbound.append(lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






						self.outbound.append(lowerCamelCase						)






	def __repr__(	self						)      ->   Optional[Any]:




						"""simple docstring"""






						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase=3,	UpperCAmelCase=0.85       )  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							{}
					for node in nodes:
										__magic_name__					:							int		   =							1

					__magic_name__					:							Optional[Any]		   =							{}
					for node in nodes:
										__magic_name__					:							Optional[int]		   =							len(node.outbound       )

					for i in range(UpperCAmelCase       ):
										print(F'''======= Iteration {i + 1} ======='''       )
										for _, node in enumerate(UpperCAmelCase       ):
															__magic_name__					:							Any		   =							(1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound       )
										print(UpperCAmelCase       )







def  lowerCAmelCase  (	)  ->Optional[int]:






					"""simple docstring"""






					__magic_name__					:							List[str]		   =							list(input('''Enter Names of the Nodes: '''       ).split()       )

					__magic_name__					:							Union[str, Any]		   =							[Node(UpperCAmelCase       ) for name in names]

					for ri, row in enumerate(UpperCAmelCase       ):
										for ci, col in enumerate(UpperCAmelCase       ):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]       )
																				nodes[ri].add_outbound(names[ci]       )

					print('''======= Nodes ======='''       )
					for node in nodes:
										print(UpperCAmelCase       )

					page_rank(UpperCAmelCase       )


if __name__ == "__main__":
				main()




'''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)






