






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:
		UpperCamelCase												=      None

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

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

# fmt: off
UpperCamelCase												=      '''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						(__SCREAMING_SNAKE_CASE       ):
			__snake_case							: Tuple							=				VOCAB_FILES_NAMES
			__snake_case							: Optional[int]							=				LlamaTokenizer
			__snake_case							: str							=				"left"
			__snake_case							: int							=				["input_ids", "attention_mask"]







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







									'''simple docstring'''




									super().__init__(
									    vocab_file=lowerCamelCase_ ,      tokenizer_file=lowerCamelCase_ ,      clean_up_tokenization_spaces=lowerCamelCase_ ,      unk_token=lowerCamelCase_ ,      bos_token=lowerCamelCase_ ,      eos_token=lowerCamelCase_ ,      **lowerCamelCase_ ,      )
									_SCREAMING_SNAKE_CASE       =    add_bos_token
									_SCREAMING_SNAKE_CASE       =    add_eos_token
									self.update_post_processor()

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







			def 				UpperCamelCase (      self:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.bos_token
									_SCREAMING_SNAKE_CASE       =    self.bos_token_id

									_SCREAMING_SNAKE_CASE       =    self.eos_token
									_SCREAMING_SNAKE_CASE       =    self.eos_token_id

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

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







			@property
			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									return self._add_eos_token







			@property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									return self._add_bos_token







			@add_eos_token.setter
			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Optional[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    value
									self.update_post_processor()







			@add_bos_token.setter
			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    value
									self.update_post_processor()







			def 				UpperCamelCase (      self:    List[Any] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Optional[str] = None	):







									'''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(lowerCamelCase_	):
															logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
															return
									_SCREAMING_SNAKE_CASE       =    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 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    "Conversation"	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    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...)"""	)

									_SCREAMING_SNAKE_CASE       =    []
									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]:
																					_SCREAMING_SNAKE_CASE       =    (
																					    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]:
															_SCREAMING_SNAKE_CASE       =    (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





"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     ArgumentParser(
						    description=(
						        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
						    )			)

						# Optional arguments for the launch helper
						parser.add_argument('''--num_cores'''						,      type=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''						,      type=_UpperCamelCase						,      help=(
						        '''The full path to the single TPU training '''
						        '''program/script to be launched in parallel, '''
						        '''followed by all the arguments for the '''
						        '''training script'''
						    )						,      )

						# rest from the training program
						parser.add_argument('''training_script_args'''						,      nargs=_UpperCamelCase			)

						return parser.parse_args()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

						# Import training_script as a module.
						UpperCAmelCase__												=     Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						UpperCAmelCase__												=     script_fpath.stem
						UpperCAmelCase__												=     importlib.import_module(_UpperCamelCase			)

						# Patch sys.argv
						UpperCAmelCase__												=     [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores			)]

						xmp.spawn(mod._mp_fn						,      args=()						,      nprocs=args.num_cores			)


if __name__ == "__main__":
						main()