


'''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()

lowercase__				     =		logging.get_logger(__name__)


lowercase__				     =		{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Optional[int]				)      ->     Any:







     '''simple docstring'''






     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:
          _a						=		TOKENIZER_CLASSES
     else:
          _a						=		{tokenizer_name: getattr(__lowerCamelCase      ,  tokenizer_name + "Fast"				)}

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

     for tokenizer_name in tokenizer_names:
          _a						=		TOKENIZER_CLASSES[tokenizer_name]

          _a						=		True
          if checkpoint_name is None:
               _a						=		list(tokenizer_class.max_model_input_sizes.keys()				)
          else:
               _a						=		[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
               _a						=		tokenizer_class.from_pretrained(__lowerCamelCase      ,  force_download=__lowerCamelCase				)

               # 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:
                    _a			,			_a						=		checkpoint.split("/"				)
                    _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
               elif add_prefix:
                    _a						=		checkpoint
                    _a						=		dump_path
               else:
                    _a						=		None
                    _a						=		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]:
                    _a						=		list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                    _a						=		file_path.split(__lowerCamelCase				)[-1][0]
                    if next_char == "/":
                         _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
                         _a						=		None

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

               _a						=		tokenizer.save_pretrained(
                   __lowerCamelCase      ,  legacy_format=__lowerCamelCase      ,  filename_prefix=__lowerCamelCase				)
               logger.info(F"=> File names {file_names}"				)

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


if __name__ == "__main__":
   lowercase__				     =		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.",
   )
   lowercase__				     =		parser.parse_args()

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





'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          # we need a list not a string, so do something to change the type
          _a						=		arr.split(","		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		[int(self.array[0]		)] * len(self.array		)
          _a						=		[int(self.array[0]		)] * len(self.array		)
          for i in range(1     ,							len(self.array		)		):
               _a						=		max(
                   int(self.array[i]		) + sum_value[i - 1]     ,							int(self.array[i]		)		)
               _a						=		max(sum_value[i]     ,							rear[i - 1]		)
          return rear[len(self.array		) - 1]


if __name__ == "__main__":
   lowercase__				     =		input("please input some numbers:")
   lowercase__				     =		SubArray(whole_array)
   lowercase__				     =		array.solve_sub_array()
   print(("the results is:", re))

