

'''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_					  =	logging.getLogger(__name__)


def  __lowerCamelCase      (				)							->       Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      str					=	argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."   )
      parser.add_argument(
          "--dataset_name"  ,  type=__UpperCamelCase  ,  default="wikitext"  ,  help="Name of the training. Explore datasets at: hf.co/datasets."  ,  )
      parser.add_argument(
          "--dataset_config"  ,  type=__UpperCamelCase  ,  default="wikitext-103-raw-v1"  ,  help="Configuration name of the dataset."   )
      parser.add_argument(
          "--batch_size"  ,  type=__UpperCamelCase  ,  default=1_0_0_0  ,  help="Batch size during training."  ,  )
      parser.add_argument(
          "--vocab_size"  ,  type=__UpperCamelCase  ,  default=1_0_0_4_8  ,  help="Size of the desired vocabulary."  ,  )
      parser.add_argument(
          "--limit"  ,  default=__UpperCamelCase  ,  type=__UpperCamelCase  ,  help="Limit the number of shards (used for debugging)."  ,  )
      parser.add_argument(
          "--export_to_hub"  ,  action="store_true"  ,  )

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	parser.parse_args()
      return args


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	datasets.load_dataset(args.dataset_name  ,  args.dataset_config  ,  split="train"   )

      if args.limit is not None:
            SCREAMING_SNAKE_CASE_				:      Dict					=	min(len(__UpperCamelCase   )  ,  args.limit   )
            SCREAMING_SNAKE_CASE_				:      str					=	dataset.select(range(__UpperCamelCase   )   )
            logger.info(F"Limiting the dataset to {args.limit} entries."   )

      def batch_iterator():
            for i in range(0  ,  len(__UpperCamelCase   )  ,  args.batch_size   ):
                  yield dataset[i : i + args.batch_size]["text"]

            # Prepare the tokenizer.
      SCREAMING_SNAKE_CASE_				:      Any					=	Tokenizer(Unigram()   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	normalizers.Sequence([normalizers.Replace("``"  ,  "\""   ), normalizers.Replace("\'\'"  ,  "\""   )]   )
      SCREAMING_SNAKE_CASE_				:      List[str]					=	pre_tokenizers.Metaspace()

      # Prepare the trainer.
      SCREAMING_SNAKE_CASE_				:      List[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=__UpperCamelCase   )
      logger.info("Tokenizer training complete!"   )

      SCREAMING_SNAKE_CASE_				:      str					=	tokenizer.token_to_id("[CLS]"   )
      SCREAMING_SNAKE_CASE_				:      int					=	tokenizer.token_to_id("[SEP]"   )
      SCREAMING_SNAKE_CASE_				:      Union[str, 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),
          ]  ,  )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	decoders.Metaspace()

      if args.export_to_hub:
            logger.info("Exporting the trained tokenzier to Hub."   )
            SCREAMING_SNAKE_CASE_				:      List[str]					=	AlbertTokenizerFast(tokenizer_object=__UpperCamelCase   )
            new_tokenizer.push_to_hub("unigram-tokenizer-dataset"   )


if __name__ == "__main__":
 snake_case_					  =	parse_args()
 main(args)




'''simple docstring'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[(0, start)]  # cost from start node,end node
      SCREAMING_SNAKE_CASE_				:      str					=	set()
      while heap:
            ((SCREAMING_SNAKE_CASE_),						(SCREAMING_SNAKE_CASE_))				:      Optional[int]					=	heapq.heappop(SCREAMING_SNAKE_CASE_   )
            if u in visited:
                  continue
            visited.add(SCREAMING_SNAKE_CASE_   )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  SCREAMING_SNAKE_CASE_				:      int					=	cost + c
                  heapq.heappush(SCREAMING_SNAKE_CASE_  ,  (next_item, v)   )
      return -1


snake_case_					  =	{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

snake_case_					  =	dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()

