






"""simple docstring"""





def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  ):
       """simple docstring"""





       lowercase__:							List[Any]         =     list(snake_case_						)
       lowercase__:							List[Any]         =     list(snake_case_						)
       lowercase__:							Union[str, Any]         =     len(snake_case_						)
       lowercase__:							Dict         =     len(snake_case_						)

       lowercase__:							Union[str, Any]         =     [
           [0 for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
       ]
       lowercase__:							List[str]         =     [
           ["0" for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
       ]

       for i in range(1		,  len_source_seq + 1						):
              lowercase__:							Dict         =     i * delete_cost
              lowercase__:							Optional[Any]         =     F"""D{source_seq[i - 1]:c}"""

       for i in range(1		,  len_destination_seq + 1						):
              lowercase__:							Any         =     i * insert_cost
              lowercase__:							Any         =     F"""I{destination_seq[i - 1]:c}"""

       for i in range(1		,  len_source_seq + 1						):
              for j in range(1		,  len_destination_seq + 1						):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            lowercase__:							List[Any]         =     costs[i - 1][j - 1] + copy_cost
                            lowercase__:							str         =     F"""C{source_seq[i - 1]:c}"""
                     else:
                            lowercase__:							Dict         =     costs[i - 1][j - 1] + replace_cost
                            lowercase__:							Optional[Any]         =     F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]						)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            lowercase__:							List[Any]         =     costs[i - 1][j] + delete_cost
                            lowercase__:							List[str]         =     F"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            lowercase__:							Optional[int]         =     costs[i][j - 1] + insert_cost
                            lowercase__:							str         =     F"""I{destination_seq[j - 1]:c}"""

       return costs, ops






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     lowercase__:							Dict         =     assemble_transformation(snake_case_		,  i - 1		,  j - 1						)
                     seq.append(ops[i][j]						)
                     return seq
              elif ops[i][j][0] == "D":
                     lowercase__:							int         =     assemble_transformation(snake_case_		,  i - 1		,  snake_case_						)
                     seq.append(ops[i][j]						)
                     return seq
              else:
                     lowercase__:							Optional[Any]         =     assemble_transformation(snake_case_		,  snake_case_		,  j - 1						)
                     seq.append(ops[i][j]						)
                     return seq


if __name__ == "__main__":
  UpperCamelCase,     UpperCamelCase					=     compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

  UpperCamelCase					=     len(operations)
  UpperCamelCase					=     len(operations[0])
  UpperCamelCase					=     assemble_transformation(operations, m - 1, n - 1)

  UpperCamelCase					=     list("""Python""")
  UpperCamelCase					=     0
  UpperCamelCase					=     0

  with open("""min_cost.txt""", """w""") as file:
    for op in sequence:
      print("""""".join(string))

      if op[0] == "C":
        file.write("""%-16s""" % """Copy %c""" % op[1])
        file.write("""\t\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost -= 1
      elif op[0] == "R":
        UpperCamelCase					=     op[2]

        file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
        file.write("""\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost += 1
      elif op[0] == "D":
        string.pop(i)

        file.write("""%-16s""" % """Delete %c""" % op[1])
        file.write("""\t\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost += 2
      else:
        string.insert(i, op[1])

        file.write("""%-16s""" % """Insert %c""" % op[1])
        file.write("""\t\t\t""" + """""".join(string))
        file.write("""\r\n""")

        cost += 2

      i += 1

    print("""""".join(string))
    print("""Cost: """, cost)

    file.write("""\r\nMinimum cost: """ + str(cost))














"""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


_UpperCamelCase :    List[str]      =			logging.getLogger(__name__)




def 					a			(  )	->		Union[str, Any]:
 __A						:							int      =						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'  ,						)

 __A						:							List[Any]      =						parser.parse_args()
 return args




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		Optional[Any]:
 __A						:							Union[str, Any]      =						datasets.load_dataset(args.dataset_name  ,						args.dataset_config  ,						split='train'		)

 if args.limit is not None:
  __A						:							Tuple      =						min(len(UpperCamelCase_		)  ,						args.limit		)
  __A						:							Optional[int]      =						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.
 __A						:							Any      =						Tokenizer(Unigram()		)
 __A						:							List[Any]      =						normalizers.Sequence([normalizers.Replace('``'  ,						'"'		), normalizers.Replace('\'\''  ,						'"'		)]		)
 __A						:							Optional[int]      =						pre_tokenizers.Metaspace()

 # Prepare the trainer.
 __A						:							int      =						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!'		)

 __A						:							Union[str, Any]      =						tokenizer.token_to_id('[CLS]'		)
 __A						:							str      =						tokenizer.token_to_id('[SEP]'		)
 __A						:							Tuple      =						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),
     ]  ,						)
 __A						:							List[str]      =						decoders.Metaspace()

 if args.export_to_hub:
  logger.info('Exporting the trained tokenzier to Hub.'		)
  __A						:							Union[str, Any]      =						AlbertTokenizerFast(tokenizer_object=UpperCamelCase_		)
  new_tokenizer.push_to_hub('unigram-tokenizer-dataset'		)


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





