






import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
					from .tokenization_llama import LlamaTokenizer
else:
					lowerCAmelCase_     =			None

lowerCAmelCase_     =			logging.get_logger(__name__)
lowerCAmelCase_     =			{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_   ,				lowerCAmelCase_     =			'[INST]', '[/INST]'
lowerCAmelCase_   ,				lowerCAmelCase_     =			'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
lowerCAmelCase_     =			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = VOCAB_FILES_NAMES
		snake_case			 = LlamaTokenizer
		snake_case			 = "left"
		snake_case			 = ["input_ids", "attention_mask"]
		def __init__(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=None    ,_SCREAMING_SNAKE_CASE		:							Dict=None    ,_SCREAMING_SNAKE_CASE		:							int=False    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]="<unk>"    ,_SCREAMING_SNAKE_CASE		:							Optional[int]="<s>"    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]="</s>"    ,_SCREAMING_SNAKE_CASE		:							Tuple=True    ,_SCREAMING_SNAKE_CASE		:							int=False    ,**_SCREAMING_SNAKE_CASE		:							int    ,)     -> str:


						'''simple docstring'''
						super().__init__(
						    vocab_file=_SCREAMING_SNAKE_CASE    ,tokenizer_file=_SCREAMING_SNAKE_CASE    ,clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE    ,unk_token=_SCREAMING_SNAKE_CASE    ,bos_token=_SCREAMING_SNAKE_CASE    ,eos_token=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE    ,)
						A									=   add_bos_token
						A									=   add_eos_token
						self.update_post_processor()

						A									=   vocab_file
						A									=   False if not self.vocab_file else True
		def    A(	self		:							Any		)     -> Any:


						'''simple docstring'''
						A									=   self.bos_token
						A									=   self.bos_token_id

						A									=   self.eos_token
						A									=   self.eos_token_id

						A									=   f'{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'
						A									=   f'{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'

						A									=   []
						if self.add_bos_token:
										special_tokens.append((bos, bos_token_id)		)
						if self.add_eos_token:
										special_tokens.append((eos, eos_token_id)		)
						A									=   processors.TemplateProcessing(
						    single=_SCREAMING_SNAKE_CASE    ,pair=_SCREAMING_SNAKE_CASE    ,special_tokens=_SCREAMING_SNAKE_CASE		)
		@property
		def    A(	self		:							List[Any]		)     -> Optional[Any]:


						'''simple docstring'''
						return self._add_eos_token
		@property
		def    A(	self		:							int		)     -> str:


						'''simple docstring'''
						return self._add_bos_token
		@add_eos_token.setter
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							str		)     -> int:


						'''simple docstring'''
						A									=   value
						self.update_post_processor()
		@add_bos_token.setter
		def    A(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							int		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   value
						self.update_post_processor()
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							Optional[str] = None		)     -> Tuple[str]:


						'''simple docstring'''
						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
										    'tokenizer.'		)

						if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
						A									=   os.path.join(
						    _SCREAMING_SNAKE_CASE    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

						if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		):
										copyfile(self.vocab_file    ,_SCREAMING_SNAKE_CASE		)

						return (out_vocab_file,)







		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							"Conversation"		)     -> List[Any]:


						'''simple docstring'''
						A									=   list(conversation.iter_texts()		)
						if not all([is_user for is_user, msg in dialogue[::2]]		) or not all(
						    [not is_user for is_user, msg in dialogue[1::2]]		):
										raise ValueError(
										    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'		)

						A									=   []
						if len(conversation.past_user_inputs		) > 0:
										if not conversation.past_user_inputs[0].startswith(_SCREAMING_SNAKE_CASE		) or E_SYS not in conversation.past_user_inputs[0]:
														A									=   (
														    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
														)
						elif not dialogue[0][1].startswith(_SCREAMING_SNAKE_CASE		) or E_SYS not in dialogue[0][1]:
										A									=   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

						dialog_tokens += sum(
						    [
						        [self.bos_token_id]
						        + self.encode(
						            f'{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '    ,add_special_tokens=_SCREAMING_SNAKE_CASE		)
						        + [self.eos_token_id]
						        for prompt, answer in zip(dialogue[::2]    ,dialogue[1::2]		)
						    ]    ,[]    ,)
						if not (dialogue[-1][0]):
										raise ValueError(f'Last message must be from user, got {dialogue[-1]["role"]}'		)
						dialog_tokens += [self.bos_token_id] + self.encode(
						    f'{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'    ,add_special_tokens=_SCREAMING_SNAKE_CASE		)
						return dialog_tokens













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()





