






'''simple docstring'''





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:
							a_      = None

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

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

# fmt: off
a_      = '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 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):
		_UpperCAmelCase         =VOCAB_FILES_NAMES
		_UpperCAmelCase         =LlamaTokenizer
		_UpperCAmelCase         ='''left'''
		_UpperCAmelCase         =['''input_ids''', '''attention_mask''']



		def __init__(    self:    Any				,   a:    List[Any]=None				,   a:    List[str]=None				,   a:    Dict=False				,   a:    Dict="<unk>"				,   a:    Union[str, Any]="<s>"				,   a:    Tuple="</s>"				,   a:    Optional[Any]=True				,   a:    Any=False				,   **a:    Tuple				,   )  ->int:






								'''simple docstring'''


								super().__init__(
								    vocab_file=a				,   tokenizer_file=a				,   clean_up_tokenization_spaces=a				,   unk_token=a				,   bos_token=a				,   eos_token=a				,   **a				,   )
								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 _lowerCAmelCase     (    self:    Optional[int])  ->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=a				,   pair=a				,   special_tokens=a)



		@property
		def _lowerCAmelCase     (    self:    str)  ->Any:






								'''simple docstring'''


								return self._add_eos_token



		@property
		def _lowerCAmelCase     (    self:    Union[str, Any])  ->str:






								'''simple docstring'''


								return self._add_bos_token



		@add_eos_token.setter
		def _lowerCAmelCase     (    self:    Union[str, Any]				,   a:    Dict)  ->List[str]:






								'''simple docstring'''


								a_				     =   value
								self.update_post_processor()



		@add_bos_token.setter
		def _lowerCAmelCase     (    self:    str				,   a:    List[str])  ->str:






								'''simple docstring'''


								a_				     =   value
								self.update_post_processor()



		def _lowerCAmelCase     (    self:    List[Any]				,   a:    str				,   a:    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(a):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
														return
								a_				     =   os.path.join(
								    a				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

								return (out_vocab_file,)




		def _lowerCAmelCase     (    self:    List[Any]				,   a:    "Conversation")  ->Tuple:






								'''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(a) 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(a) 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=a)
								        + [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=a)
								return dialog_tokens











'''simple docstring'''





from __future__ import annotations





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    List[str]				,   a:    int = 0)  ->None:






								'''simple docstring'''


								a_				     =   value
								a_				     =   None
								a_				     =   None





def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				Node | None:

						'''simple docstring'''


						if treea is None:
												return treea
						if treea is None:
												return treea

						a_				     =   treea.value + treea.value
						a_				     =   merge_two_binary_trees(treea.left	,treea.left				)
						a_				     =   merge_two_binary_trees(treea.right	,treea.right				)
						return treea



def 			__UpperCAmelCase				(lowercase__				)		->				None:

						'''simple docstring'''


						if root:
												print(root.value				)
												print_preorder(root.left				)
												print_preorder(root.right				)


if __name__ == "__main__":
							a_      = Node(1)
							a_      = Node(2)
							a_      = Node(3)
							a_      = Node(4)

							a_      = Node(2)
							a_      = Node(4)
							a_      = Node(6)
							a_      = Node(9)
							a_      = Node(5)

							print('Tree1 is: ')
							print_preorder(treea)
							print('Tree2 is: ')
							print_preorder(treea)
							a_      = merge_two_binary_trees(treea, treea)
							print('Merged Tree is: ')
							print_preorder(merged_tree)



