





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_			(       snake_case_ ):





					"""simple docstring"""


					A__								=     VOCAB_FILES_NAMES
					A__								=     LlamaTokenizer
					A__								=     "left"
					A__								=     ["input_ids", "attention_mask"]





					def __init__(			self						,	_lowerCAmelCase=None						,	_lowerCAmelCase=None						,	_lowerCAmelCase=False						,	_lowerCAmelCase="<unk>"						,	_lowerCAmelCase="<s>"						,	_lowerCAmelCase="</s>"						,	_lowerCAmelCase=True						,	_lowerCAmelCase=False						,	**_lowerCAmelCase						,	):
										super().__init__(
										    vocab_file=_lowerCAmelCase						,	tokenizer_file=_lowerCAmelCase						,	clean_up_tokenization_spaces=_lowerCAmelCase						,	unk_token=_lowerCAmelCase						,	bos_token=_lowerCAmelCase						,	eos_token=_lowerCAmelCase						,	**_lowerCAmelCase						,	)
										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        __magic_name__			(			self						):
										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=_lowerCAmelCase						,	pair=_lowerCAmelCase						,	special_tokens=_lowerCAmelCase						)





					@property
					def        __magic_name__			(			self						):
										return self._add_eos_token





					@property
					def        __magic_name__			(			self						):
										return self._add_bos_token





					@add_eos_token.setter
					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							value
										self.update_post_processor()





					@add_bos_token.setter
					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							value
										self.update_post_processor()





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase = None						):






										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(_lowerCAmelCase						):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
															return
										lowerCamelCase__      =							os.path.join(
										    _lowerCAmelCase						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

										return (out_vocab_file,)






					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										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(_lowerCAmelCase						) 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(_lowerCAmelCase						) 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=_lowerCAmelCase						)
										        + [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=_lowerCAmelCase						)
										return dialog_tokens





from __future__ import annotations

from statistics import mean




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



	lowercase_			 = 2
	lowercase_			 = list(__lowerCamelCase  )
	lowercase_			 = [0] * len(__lowerCamelCase  )
	lowercase_			 = 0
	while True:
		lowercase_			 = True
		for i, burst_time in enumerate(__lowerCamelCase  ):
			if rem_burst_times[i] > 0:
				lowercase_			 = False
				if rem_burst_times[i] > quantum:
					t += quantum
					rem_burst_times[i] -= quantum
				else:
					t += rem_burst_times[i]
					lowercase_			 = t - burst_time
					lowercase_			 = 0
		if done is True:
			return waiting_times




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ,			__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



	return [burst + waiting for burst, waiting in zip(__lowerCamelCase  ,			__lowerCamelCase  )]


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				[3, 5, 7]
							SCREAMING_SNAKE_CASE__       			=				calculate_waiting_times(burst_times)
							SCREAMING_SNAKE_CASE__       			=				calculate_turn_around_times(burst_times, waiting_times)
							print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
							for i, burst_time in enumerate(burst_times):
														print(
														    f"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
														    f"""{turn_around_times[i]}"""
														)
							print(f"""\nAverage waiting time = {mean(waiting_times):.5f}""")
							print(f"""Average turn around time = {mean(turn_around_times):.5f}""")

