


#
# Sort large text files in a minimum amount of memory
#
import argparse
import os



class 							lowercase__:






      '''simple docstring'''







      snake_case__       =					'''block_{0}.dat'''




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=filename
         UpperCamelCase__ :			Any 						=[]




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.BLOCK_FILENAME_FORMAT.format(__SCREAMING_SNAKE_CASE)
         with open(__SCREAMING_SNAKE_CASE   ,			"w") as file:
            file.write(__SCREAMING_SNAKE_CASE)
         self.block_filenames.append(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         return self.block_filenames




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=0
         with open(self.filename) as file:
            while True:
               UpperCamelCase__ :			Tuple 						=file.readlines(__SCREAMING_SNAKE_CASE)

               if lines == []:
                  break

               if sort_key is None:
                  lines.sort()
               else:
                  lines.sort(key=__SCREAMING_SNAKE_CASE)

               self.write_block("".join(__SCREAMING_SNAKE_CASE)   ,			__SCREAMING_SNAKE_CASE)
               i += 1




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         map(os.remove   ,			self.block_filenames)




class 							lowercase__:






      '''simple docstring'''







      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=-1
         UpperCamelCase__ :			List[Any] 						=None

         for i in range(len(__SCREAMING_SNAKE_CASE)):
            if min_str is None or choices[i] < min_str:
               UpperCamelCase__ :			Union[str, Any] 						=i

         return min_index




class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=files
         UpperCamelCase__ :			Union[str, Any] 						=set()
         UpperCamelCase__ :			Any 						=len(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			int 						={i: None for i in range(self.num_buffers)}




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







         """simple docstring"""







         return {
             i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty
         }




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         for i in range(self.num_buffers):
            if self.buffers[i] is None and i not in self.empty:
               UpperCamelCase__ :			str 						=self.files[i].readline()

               if self.buffers[i] == "":
                  self.empty.add(__SCREAMING_SNAKE_CASE)
                  self.files[i].close()

         if len(self.empty) == self.num_buffers:
            return False

         return True




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.buffers[index]
         UpperCamelCase__ :			str 						=None

         return value




class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=merge_strategy




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			int 						=FilesArray(self.get_file_handles(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE))
         with open(__SCREAMING_SNAKE_CASE   ,			"w"   ,			__SCREAMING_SNAKE_CASE) as outfile:
            while buffers.refresh():
               UpperCamelCase__ :			Dict 						=self.merge_strategy.select(buffers.get_dict())
               outfile.write(buffers.unshift(__SCREAMING_SNAKE_CASE))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						={}

         for i in range(len(__SCREAMING_SNAKE_CASE)):
            UpperCamelCase__ :			Dict 						=open(filenames[i]   ,			"r"   ,			__SCREAMING_SNAKE_CASE)  # noqa: UP015

         return files




class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=block_size




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=self.get_number_blocks(__SCREAMING_SNAKE_CASE   ,			self.block_size)
         UpperCamelCase__ :			Union[str, Any] 						=FileSplitter(__SCREAMING_SNAKE_CASE)
         splitter.split(self.block_size   ,			__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			str 						=FileMerger(NWayMerge())
         UpperCamelCase__ :			int 						=self.block_size / (num_blocks + 1)
         merger.merge(splitter.get_block_filenames()   ,			filename + ".out"   ,			__SCREAMING_SNAKE_CASE)

         splitter.cleanup()




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         return (os.stat(__SCREAMING_SNAKE_CASE).st_size / block_size) + 1




def 	_lowerCamelCase  (		A_  :	Tuple      )			->			Any:

   '''simple docstring'''






   if string[-1].lower() == "k":
      return int(string[:-1]      ) * 1_0_2_4
   elif string[-1].lower() == "m":
      return int(string[:-1]      ) * 1_0_2_4 * 1_0_2_4
   elif string[-1].lower() == "g":
      return int(string[:-1]      ) * 1_0_2_4 * 1_0_2_4 * 1_0_2_4
   else:
      return int(A_      )
def 	_lowerCamelCase  (		)			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=argparse.ArgumentParser()
   parser.add_argument(
       "-m"							,  "--mem"							,  help="amount of memory to use for sorting"							,  default="100M"      )
   parser.add_argument(
       "filename"							,  metavar="<filename>"							,  nargs=1							,  help="name of file to sort"      )
   UpperCamelCase__ :			Optional[int] 						=parser.parse_args()

   UpperCamelCase__ :			Optional[Any] 						=ExternalSort(parse_memory(args.mem      )      )
   sorter.sort(args.filename[0]      )


if __name__ == "__main__":
     main()





from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

