"""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:
       lowerCamelCase      : Any       =None

lowerCamelCase      : Tuple       =logging.get_logger(__name__)
lowerCamelCase      : List[Any]       ={'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     , lowerCamelCase      : Tuple       ='''[INST]''', '''[/INST]'''
lowerCamelCase     , lowerCamelCase      : Optional[Any]       ='''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
lowerCamelCase      : List[Any]       ='''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			VOCAB_FILES_NAMES
    _UpperCAmelCase       =			LlamaTokenizer
    _UpperCAmelCase       =			"left"
    _UpperCAmelCase       =			["input_ids", "attention_mask"]
    def __init__(  self					,       __lowerCamelCase=None					,       __lowerCamelCase=None					,       __lowerCamelCase=False					,       __lowerCamelCase="<unk>"					,       __lowerCamelCase="<s>"					,       __lowerCamelCase="</s>"					,       __lowerCamelCase=True					,       __lowerCamelCase=False					,       **__lowerCamelCase					,       ):




         '''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					,       )
         __A					:					Optional[int]								=		add_bos_token
         __A					:					List[str]								=		add_eos_token
         self.update_post_processor()

         __A					:					Optional[int]								=		vocab_file
         __A					:					str								=		False if not self.vocab_file else True
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		self.bos_token
         __A					:					int								=		self.bos_token_id

         __A					:					List[str]								=		self.eos_token
         __A					:					Union[str, Any]								=		self.eos_token_id

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

         __A					:					Any								=		[]
         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					:					Tuple								=		processors.TemplateProcessing(
             single=__lowerCamelCase					,       pair=__lowerCamelCase					,       special_tokens=__lowerCamelCase	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         return self._add_bos_token
    @add_eos_token.setter
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Tuple								=		value
         self.update_post_processor()
    @add_bos_token.setter
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		value
         self.update_post_processor()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = 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
         __A					:					List[str]								=		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 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Any								=		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					:					Tuple								=		[]
         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]:
                   __A					:					Tuple								=		(
                       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]:
              __A					:					Any								=		(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 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		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=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       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=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, Any]								=		[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()



