






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


lowerCAmelCase_     =			logging.get_logger(__name__)

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


lowerCAmelCase_     =			{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = ["feature_extractor", "tokenizer"]
		# Names need to be attr_class for attr in attributes
		snake_case			 = None
		snake_case			 = None
		snake_case			 = None
		def __init__(	self		:							Any    ,*_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,**_SCREAMING_SNAKE_CASE		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						# Sanitize args and kwargs
						for key in kwargs:
										if key not in self.attributes:
														raise TypeError(f'Unexpected keyword argument {key}.'		)
						for arg, attribute_name in zip(_SCREAMING_SNAKE_CASE    ,self.attributes		):
										if attribute_name in kwargs:
														raise TypeError(f'Got multiple values for argument {attribute_name}.'		)
										else:
														A									=   arg

						if len(_SCREAMING_SNAKE_CASE		) != len(self.attributes		):
										raise ValueError(
										    f'This processor requires {len(self.attributes		)} arguments: {", ".join(self.attributes		)}. Got '
										    f'{len(_SCREAMING_SNAKE_CASE		)} 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(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		):
														A									=   tuple(getattr(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) for n in class_name if n is not None		)
										else:
														A									=   getattr(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)

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

										setattr(self    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
		def __repr__(	self		:							Dict		)     -> str:


						'''simple docstring'''
						A									=   [f'- {name}: {repr(getattr(self    ,_SCREAMING_SNAKE_CASE		)		)}' for name in self.attributes]
						A									=   '\n'.join(_SCREAMING_SNAKE_CASE		)
						return f'{self.__class__.__name__}:\n{attributes_repr}'
		def    A(	self		:							Dict    ,_SCREAMING_SNAKE_CASE		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,**_SCREAMING_SNAKE_CASE		:							Dict		)     -> str:


						'''simple docstring'''
						os.makedirs(_SCREAMING_SNAKE_CASE    ,exist_ok=_SCREAMING_SNAKE_CASE		)

						if push_to_hub:
										A									=   kwargs.pop('commit_message'    ,_SCREAMING_SNAKE_CASE		)
										A									=   kwargs.pop('repo_id'    ,save_directory.split(os.path.sep		)[-1]		)
										A									=   self._create_repo(_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
										A									=   self._get_files_timestamps(_SCREAMING_SNAKE_CASE		)
						# 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    ,_SCREAMING_SNAKE_CASE		) for attribute_name in self.attributes]
										A									=   [(a.init_kwargs if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else a) for a in attrs]
										custom_object_save(self    ,_SCREAMING_SNAKE_CASE    ,config=_SCREAMING_SNAKE_CASE		)

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

						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    ,_SCREAMING_SNAKE_CASE		)
														if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		):
																		del attribute.init_kwargs["auto_map"]

						if push_to_hub:
										self._upload_modified_files(
										    _SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,commit_message=_SCREAMING_SNAKE_CASE    ,token=kwargs.get('use_auth_token'		)    ,)
		@classmethod
		def    A(	cls		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							Union[str, os.PathLike]    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, os.PathLike]] = None    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, bool]] = None    ,_SCREAMING_SNAKE_CASE		:							str = "main"    ,**_SCREAMING_SNAKE_CASE		:							Optional[int]    ,)     -> List[Any]:


						'''simple docstring'''
						A									=   cache_dir
						A									=   force_download
						A									=   local_files_only
						A									=   revision

						A									=   kwargs.pop('use_auth_token'    ,_SCREAMING_SNAKE_CASE		)
						if use_auth_token is not None:
										warnings.warn(
										    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'    ,_SCREAMING_SNAKE_CASE		)
										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(_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
						return cls(*_SCREAMING_SNAKE_CASE		)
		@classmethod
		def    A(	cls		:							str    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]="AutoProcessor"		)     -> List[Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		):
										A									=   auto_class.__name__

						import transformers.models.auto as auto_module

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

						A									=   auto_class
		@classmethod
		def    A(	cls		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]    ,**_SCREAMING_SNAKE_CASE		:							str		)     -> Tuple:


						'''simple docstring'''
						A									=   []
						for attribute_name in cls.attributes:
										A									=   getattr(cls    ,f'{attribute_name}_class'		)
										if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		):
														A									=   tuple(getattr(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) if n is not None else None for n in class_name		)
														A									=   kwargs.get('use_fast'    ,_SCREAMING_SNAKE_CASE		)
														if use_fast and classes[1] is not None:
																		A									=   classes[1]
														else:
																		A									=   classes[0]
										else:
														A									=   getattr(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)

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







		@property
		def    A(	self		:							str		)     -> int:


						'''simple docstring'''
						A									=   getattr(self    ,self.attributes[0]		)
						return getattr(_SCREAMING_SNAKE_CASE    ,'model_input_names'    ,_SCREAMING_SNAKE_CASE		)


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













from __future__ import annotations







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							list[int],    UpperCAmelCase						:							list[int],    UpperCAmelCase						:							int ):

				# Base Case
				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(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
				else:
								A									=   capacity - weights[counter - 1]
								A									=   values[counter - 1] + knapsack(
								    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
								A									=   knapsack(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
								return max(UpperCAmelCase,    UpperCAmelCase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





