


'''simple docstring'''

import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowercase__					  =		logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowercase__					  =		direct_transformers_import(Path(__file__).parent)


lowercase__					  =		{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}





class     __SCREAMING_SNAKE_CASE       (   a__			):
     UpperCAmelCase			       =					["""feature_extractor""", """tokenizer"""]
     # Names need to be attr_class for attr in attributes
     UpperCAmelCase			       =					None
     UpperCAmelCase			       =					None
     UpperCAmelCase			       =					None






     def __init__(      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     Optional[int]:





          for key in kwargs:
               if key not in self.attributes:
                    raise TypeError(f"Unexpected keyword argument {key}."		)
          for arg, attribute_name in zip(lowercase__  ,				self.attributes		):
               if attribute_name in kwargs:
                    raise TypeError(f"Got multiple values for argument {attribute_name}."		)
               else:
                    _a            =		arg

          if len(lowercase__		) != len(self.attributes		):
               raise ValueError(
                   f"This processor requires {len(self.attributes		)} arguments: {', '.join(self.attributes		)}. Got "
                   f"{len(lowercase__		)} arguments instead."		)

          # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
          for attribute_name, arg in kwargs.items():
               _a            =		getattr(self  ,				f"{attribute_name}_class"		)
               # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
               _a            =		AUTO_TO_BASE_CLASS_MAPPING.get(lowercase__  ,				lowercase__		)
               if isinstance(lowercase__  ,				lowercase__		):
                    _a            =		tuple(getattr(lowercase__  ,				lowercase__		) for n in class_name if n is not None		)
               else:
                    _a            =		getattr(lowercase__  ,				lowercase__		)

               if not isinstance(lowercase__  ,				lowercase__		):
                    raise ValueError(
                        f"Received a {type(lowercase__		).__name__} for argument {attribute_name}, but a {class_name} was expected."		)

               setattr(self  ,				lowercase__  ,				lowercase__		)






     def __repr__(      self		)							->     Optional[Any]:
          _a            =		[f"- {name}: {repr(getattr(self  ,				lowercase__		)		)}" for name in self.attributes]
          _a            =		'''\n'''.join(lowercase__		)
          return f"{self.__class__.__name__}:\n{attributes_repr}"






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase = False  ,				**__UpperCamelCase		)							->     List[str]:
          os.makedirs(lowercase__  ,				exist_ok=lowercase__		)

          if push_to_hub:
               _a            =		kwargs.pop("commit_message"  ,				lowercase__		)
               _a            =		kwargs.pop("repo_id"  ,				save_directory.split(os.path.sep		)[-1]		)
               _a            =		self._create_repo(lowercase__  ,				**lowercase__		)
               _a            =		self._get_files_timestamps(lowercase__		)
          # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
          # loaded from the Hub.
          if self._auto_class is not None:
               _a            =		[getattr(self  ,				lowercase__		) for attribute_name in self.attributes]
               _a            =		[(a.init_kwargs if isinstance(lowercase__  ,				lowercase__		) else a) for a in attrs]
               custom_object_save(self  ,				lowercase__  ,				config=lowercase__		)

          for attribute_name in self.attributes:
               _a            =		getattr(self  ,				lowercase__		)
               # Include the processor class in the attribute config so this processor can then be reloaded with the
               # `AutoProcessor` API.
               if hasattr(lowercase__  ,				"_set_processor_class"		):
                    attribute._set_processor_class(self.__class__.__name__		)
               attribute.save_pretrained(lowercase__		)

          if self._auto_class is not None:
               # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
               for attribute_name in self.attributes:
                    _a            =		getattr(self  ,				lowercase__		)
                    if isinstance(lowercase__  ,				lowercase__		):
                         del attribute.init_kwargs["auto_map"]

          if push_to_hub:
               self._upload_modified_files(
                   lowercase__  ,				lowercase__  ,				lowercase__  ,				commit_message=lowercase__  ,				token=kwargs.get("use_auth_token"		)  ,				)






     @classmethod
     def 			a_       (      cls  ,				__UpperCamelCase  ,				__UpperCamelCase = None  ,				__UpperCamelCase = False  ,				__UpperCamelCase = False  ,				__UpperCamelCase = None  ,				__UpperCamelCase = "main"  ,				**__UpperCamelCase  ,				)							->     Any:
          _a            =		cache_dir
          _a            =		force_download
          _a            =		local_files_only
          _a            =		revision

          _a            =		kwargs.pop("use_auth_token"  ,				lowercase__		)
          if use_auth_token is not None:
               warnings.warn(
                   "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."  ,				lowercase__		)
               if token is not None:
                    raise ValueError(
                        "`token` and `use_auth_token` are both specified. Please set only the argument `token`."		)
               _a            =		use_auth_token

          if token is not None:
               # change to `token` in a follow-up PR
               _a            =		token

          _a            =		cls._get_arguments_from_pretrained(lowercase__  ,				**lowercase__		)
          return cls(*lowercase__		)






     @classmethod
     def 			a_       (      cls  ,				__UpperCamelCase="AutoProcessor"		)							->     int:





          if not isinstance(lowercase__  ,				lowercase__		):
               _a            =		auto_class.__name__

          import transformers.models.auto as auto_module

          if not hasattr(lowercase__  ,				lowercase__		):
               raise ValueError(f"{auto_class} is not a valid auto class."		)

          _a            =		auto_class






     @classmethod
     def 			a_       (      cls  ,				__UpperCamelCase  ,				**__UpperCamelCase		)							->     Union[str, Any]:
          _a            =		[]
          for attribute_name in cls.attributes:
               _a            =		getattr(cls  ,				f"{attribute_name}_class"		)
               if isinstance(lowercase__  ,				lowercase__		):
                    _a            =		tuple(getattr(lowercase__  ,				lowercase__		) if n is not None else None for n in class_name		)
                    _a            =		kwargs.get("use_fast"  ,				lowercase__		)
                    if use_fast and classes[1] is not None:
                         _a            =		classes[1]
                    else:
                         _a            =		classes[0]
               else:
                    _a            =		getattr(lowercase__  ,				lowercase__		)

               args.append(attribute_class.from_pretrained(lowercase__  ,				**lowercase__		)		)
          return args






     @property
     def 			a_       (      self		)							->     Tuple:
          _a            =		getattr(self  ,				self.attributes[0]		)
          return getattr(lowercase__  ,				"model_input_names"  ,				lowercase__		)







lowercase__					  =		copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
   lowercase__					  =		ProcessorMixin.push_to_hub.__doc__.format(
       object="processor", object_class="AutoProcessor", object_files="processor files"
   )








'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      if counter == 0 or capacity == 0:
            return 0

      # If weight of the nth item is more than Knapsack of capacity,
      #   then this item cannot be included in the optimal solution,
      # else return the maximum of two cases:
      #   (1) nth item included
      #   (2) not included
      if weights[counter - 1] > capacity:
            return knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
      else:
            _snake_case		:						List[str]         =  capacity - weights[counter - 1]
            _snake_case		:						Tuple         =  values[counter - 1] + knapsack(
                lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            _snake_case		:						Any         =  knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            return max(lowerCAmelCase_      ,   lowerCAmelCase_						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


