




'''simple docstring'''



import math
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list	,					__A    :			int = 0	,					__A    :			int = 0  )			->			list:
   _SCREAMING_SNAKE_CASE	=				end or len(__A  )
   for i in range(__A	,					__A  ):
      _SCREAMING_SNAKE_CASE	=				i
      _SCREAMING_SNAKE_CASE	=				array[i]
      while temp_index != start and temp_index_value < array[temp_index - 1]:
         _SCREAMING_SNAKE_CASE	=				array[temp_index - 1]
         temp_index -= 1
      _SCREAMING_SNAKE_CASE	=				temp_index_value
   return array
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list	,					__A    :			int	,					__A    :			int  )			->			None:  # Max Heap
   _SCREAMING_SNAKE_CASE	=				index
   _SCREAMING_SNAKE_CASE	=				2 * index + 1  # Left Node
   _SCREAMING_SNAKE_CASE	=				2 * index + 2  # Right Node

   if left_index < heap_size and array[largest] < array[left_index]:
      _SCREAMING_SNAKE_CASE	=				left_index

   if right_index < heap_size and array[largest] < array[right_index]:
      _SCREAMING_SNAKE_CASE	=				right_index

   if largest != index:
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				array[largest], array[index]
      heapify(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list  )			->			list:
   _SCREAMING_SNAKE_CASE	=				len(__A  )

   for i in range(n // 2	,					-1	,					-1  ):
      heapify(__A	,					__A	,					__A  )

   for i in range(n - 1	,					0	,					-1  ):
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				array[0], array[i]
      heapify(__A	,					0	,					__A  )

   return array
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list	,					__A    :			int	,					__A    :			int	,					__A    :			int  )			->			int:
   if (array[first_index] > array[middle_index]) != (
       array[first_index] > array[last_index]
   ):
      return array[first_index]
   elif (array[middle_index] > array[first_index]) != (
       array[middle_index] > array[last_index]
   ):
      return array[middle_index]
   else:
      return array[last_index]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list	,					__A    :			int	,					__A    :			int	,					__A    :			int  )			->			int:
   _SCREAMING_SNAKE_CASE	=				low
   _SCREAMING_SNAKE_CASE	=				high
   while True:
      while array[i] < pivot:
         i += 1
      j -= 1
      while pivot < array[j]:
         j -= 1
      if i >= j:
         return i
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				array[j], array[i]
      i += 1
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list  )			->			list:
   if len(__A  ) == 0:
      return array
   _SCREAMING_SNAKE_CASE	=				2 * math.ceil(math.loga(len(__A  )  )  )
   _SCREAMING_SNAKE_CASE	=				16
   return intro_sort(__A	,					0	,					len(__A  )	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			list	,					__A    :			int	,					__A    :			int	,					__A    :			int	,					__A    :			int  )			->			list:
   while end - start > size_threshold:
      if max_depth == 0:
         return heap_sort(__A  )
      max_depth -= 1
      _SCREAMING_SNAKE_CASE	=				median_of_a(__A	,					__A	,					start + ((end - start) // 2) + 1	,					end - 1  )
      _SCREAMING_SNAKE_CASE	=				partition(__A	,					__A	,					__A	,					__A  )
      intro_sort(__A	,					__A	,					__A	,					__A	,					__A  )
      _SCREAMING_SNAKE_CASE	=				p
   return insertion_sort(__A	,					__A	,					__A  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase_			=						input('Enter numbers separated by a comma : ').strip()
 lowerCamelCase_			=						[float(item) for item in user_input.split(',')]
 print(sort(unsorted))







'''simple docstring'''



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase_			=						logging.get_logger(__name__)


lowerCamelCase_			=						{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Tuple	,					__A    :			List[str]	,					__A    :			List[str]  )			->			List[Any]:
   if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
      raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()  )}."""  )

   if tokenizer_name is None:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES
   else:
      _SCREAMING_SNAKE_CASE	=				{tokenizer_name: getattr(__A	,					tokenizer_name + "Fast"  )}

   logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""  )

   for tokenizer_name in tokenizer_names:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES[tokenizer_name]

      _SCREAMING_SNAKE_CASE	=				True
      if checkpoint_name is None:
         _SCREAMING_SNAKE_CASE	=				list(tokenizer_class.max_model_input_sizes.keys()  )
      else:
         _SCREAMING_SNAKE_CASE	=				[checkpoint_name]

      logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""  )

      for checkpoint in checkpoint_names:
         logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""  )

         # Load tokenizer
         _SCREAMING_SNAKE_CASE	=				tokenizer_class.from_pretrained(__A	,					force_download=__A  )

         # Save fast tokenizer
         logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""  )

         # For organization names we create sub-directories
         if "/" in checkpoint:
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				checkpoint.split("/"  )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
         elif add_prefix:
            _SCREAMING_SNAKE_CASE	=				checkpoint
            _SCREAMING_SNAKE_CASE	=				dump_path
         else:
            _SCREAMING_SNAKE_CASE	=				None
            _SCREAMING_SNAKE_CASE	=				dump_path

         logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""  )

         if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()  )[0]:
            _SCREAMING_SNAKE_CASE	=				list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
            _SCREAMING_SNAKE_CASE	=				file_path.split(__A  )[-1][0]
            if next_char == "/":
               _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
               _SCREAMING_SNAKE_CASE	=				None

            logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""  )

         _SCREAMING_SNAKE_CASE	=				tokenizer.save_pretrained(
             __A	,					legacy_format=__A	,					filename_prefix=__A  )
         logger.info(f"""=> File names {file_names}"""  )

         for file_name in file_names:
            if not file_name.endswith("tokenizer.json"  ):
               os.remove(__A  )
               logger.info(f"""=> removing {file_name}"""  )


if __name__ == "__main__":
 lowerCamelCase_			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
 )
 parser.add_argument(
     '--tokenizer_name',
     default=None,
     type=str,
     help=(
         f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
         'download and convert all the checkpoints from AWS.'
     ),
 )
 parser.add_argument(
     '--checkpoint_name',
     default=None,
     type=str,
     help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
 )
 parser.add_argument(
     '--force_download',
     action='store_true',
     help='Re-download checkpoints.',
 )
 lowerCamelCase_			=						parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

