
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      _lowerCAmelCase (     __a	):
				_lowercase      		=VOCAB_FILES_NAMES
				_lowercase      		=LlamaTokenizer
				_lowercase      		='''left'''
				_lowercase      		=['''input_ids''', '''attention_mask''']




				def __init__( self    ,    _UpperCamelCase=None    ,    _UpperCamelCase=None    ,    _UpperCamelCase=False    ,    _UpperCamelCase="<unk>"    ,    _UpperCamelCase="<s>"    ,    _UpperCamelCase="</s>"    ,    _UpperCamelCase=True    ,    _UpperCamelCase=False    ,    **_UpperCamelCase    ,    )    ->				Any:
							super().__init__(
							    vocab_file=_UpperCamelCase    ,    tokenizer_file=_UpperCamelCase    ,    clean_up_tokenization_spaces=_UpperCamelCase    ,    unk_token=_UpperCamelCase    ,    bos_token=_UpperCamelCase    ,    eos_token=_UpperCamelCase    ,    **_UpperCamelCase    ,    )
							lowerCAmelCase_      =     add_bos_token
							lowerCAmelCase_      =     add_eos_token
							self.update_post_processor()

							lowerCAmelCase_      =     vocab_file
							lowerCAmelCase_      =     False if not self.vocab_file else True




				def 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     self.bos_token
							lowerCAmelCase_      =     self.bos_token_id

							lowerCAmelCase_      =     self.eos_token
							lowerCAmelCase_      =     self.eos_token_id

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

							lowerCAmelCase_      =     []
							if self.add_bos_token:
										special_tokens.append((bos, bos_token_id)					)
							if self.add_eos_token:
										special_tokens.append((eos, eos_token_id)					)
							lowerCAmelCase_      =     processors.TemplateProcessing(
							    single=_UpperCamelCase    ,    pair=_UpperCamelCase    ,    special_tokens=_UpperCamelCase					)




				@property
				def 					__a						( self					)    ->				Dict:
							return self._add_eos_token




				@property
				def 					__a						( self					)    ->				Optional[int]:
							return self._add_bos_token




				@add_eos_token.setter
				def 					__a						( self    ,    _UpperCamelCase					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     value
							self.update_post_processor()




				@add_bos_token.setter
				def 					__a						( self    ,    _UpperCamelCase					)    ->				Any:
							lowerCAmelCase_      =     value
							self.update_post_processor()




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = None					)    ->				Tuple[str]:
							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(_UpperCamelCase					):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
										return
							lowerCAmelCase_      =     os.path.join(
							    _UpperCamelCase    ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

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

							return (out_vocab_file,)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							lowerCAmelCase_      =     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...)"					)

							lowerCAmelCase_      =     []
							if len(conversation.past_user_inputs					) > 0:
										if not conversation.past_user_inputs[0].startswith(_UpperCamelCase					) or E_SYS not in conversation.past_user_inputs[0]:
													lowerCAmelCase_      =     (
													    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
													)
							elif not dialogue[0][1].startswith(_UpperCamelCase					) or E_SYS not in dialogue[0][1]:
										lowerCAmelCase_      =     (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=_UpperCamelCase					)
							        + [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=_UpperCamelCase					)
							return dialog_tokens





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     key
							lowerCAmelCase_      =     freq




				def __str__( self					)    ->				Any:
							return f"""Node(key={self.key}, freq={self.freq})"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			if i > j or i < 0 or j > len(__lowerCAmelCase    ) - 1:
						return

			lowerCAmelCase_      =     root[i][j]
			if parent == -1:  # root does not have a parent
						print(F"""{key[node]} is the root of the binary search tree."""    )
			elif is_left:
						print(F"""{key[node]} is the left child of key {parent}."""    )
			else:
						print(F"""{key[node]} is the right child of key {parent}."""    )

			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							node - 1   ,							key[node]   ,							__lowerCAmelCase    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							node + 1   ,							__lowerCAmelCase   ,							key[node]   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			nodes.sort(key=lambda __lowerCAmelCase    : node.key    )

			lowerCAmelCase_      =     len(__lowerCAmelCase    )

			lowerCAmelCase_      =     [nodes[i].key for i in range(__lowerCAmelCase    )]
			lowerCAmelCase_      =     [nodes[i].freq for i in range(__lowerCAmelCase    )]

			# This 2D array stores the overall tree cost (which's as minimized as possible);
			# for a single key, cost is equal to frequency of the key.
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
			# array
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# stores tree roots that will be used later for constructing binary search tree
			lowerCAmelCase_      =     [[i if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]

			for interval_length in range(2   ,							n + 1    ):
						for i in range(n - interval_length + 1    ):
									lowerCAmelCase_      =     i + interval_length - 1

									lowerCAmelCase_      =     sys.maxsize  # set the value to "infinity"
									lowerCAmelCase_      =     total[i][j - 1] + freqs[j]

									# Apply Knuth's optimization
									# Loop without optimization: for r in range(i, j + 1):
									for r in range(root[i][j - 1]   ,							root[i + 1][j] + 1    ):  # r is a temporal root
												lowerCAmelCase_      =     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
												lowerCAmelCase_      =     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
												lowerCAmelCase_      =     left + total[i][j] + right

												if dp[i][j] > cost:
															lowerCAmelCase_      =     cost
															lowerCAmelCase_      =     r

			print("Binary search tree nodes:"    )
			for node in nodes:
						print(__lowerCAmelCase    )

			print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							0   ,							n - 1   ,							-1   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     [Node(__lowerCAmelCase   ,							randint(1   ,							50    )    ) for i in range(10   ,							0   ,							-1    )]
			find_optimal_binary_search_tree(__lowerCAmelCase    )


if __name__ == "__main__":
			main()



