






"""simple docstring"""







from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__a							:						Union[str, Any]   							=			logging.get_logger(__name__)

__a							:						Optional[int]   							=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__a							:						List[Any]   							=			{
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__a							:						Any   							=			{
    'openai-gpt': 512,
}







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE     =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE     =['input_ids', 'attention_mask']
							_SCREAMING_SNAKE_CASE     =OpenAIGPTTokenizer




							def __init__( self:  List[Any]					,		__A:  int=None					,		__A:  Tuple=None					,		__A:  Any=None					,		__A:  int="<unk>"					,		**__A:  Dict     ):

												'''simple docstring'''

												super().__init__(__A					,		__A					,		tokenizer_file=__A					,		unk_token=__A					,		**__A     )




							@property
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												return True





							def 			lowercase		( self:  Dict					,		__A:  str					,		__A:  Optional[str] = None     ):

												'''simple docstring'''

												a__ 							=	self._tokenizer.model.save(__A					,		name=__A     )
												return tuple(__A     )










"""simple docstring"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# 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(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


