

"""simple docstring"""

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

a__ :	str						= logging.get_logger(__name__)

a__ :	List[Any]						= {"""vocab_file""": """tokenizer.model"""}

a__ :	Dict						= {
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
a__ :	Optional[int]						= {
    """hf-internal-testing/llama-tokenizer""": 2048,
}
a__ :	Any						= """▁"""

a__ :	int						= """[INST]""", """[/INST]"""
a__ :	List[str]						= """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
a__ :	List[str]						= """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     (						UpperCamelCase__       ):
      __snake_case 			=     VOCAB_FILES_NAMES
      __snake_case 			=     PRETRAINED_VOCAB_FILES_MAP
      __snake_case 			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case 			=     ["""input_ids""", """attention_mask"""]







      def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_="<unk>"     ,							UpperCAmelCase_="<s>"     ,							UpperCAmelCase_="</s>"     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=True     ,							**UpperCAmelCase_     ,							)			->						Optional[Any]:
           __lowercase												=			{} if sp_model_kwargs is None else sp_model_kwargs
           __lowercase												=			AddedToken(lowerCAmelCase__     ,							lstrip=lowerCAmelCase__     ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__     ,							lowerCAmelCase__							) else bos_token
           __lowercase												=			AddedToken(lowerCAmelCase__     ,							lstrip=lowerCAmelCase__     ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__     ,							lowerCAmelCase__							) else eos_token
           __lowercase												=			AddedToken(lowerCAmelCase__     ,							lstrip=lowerCAmelCase__     ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__     ,							lowerCAmelCase__							) else unk_token
           __lowercase												=			AddedToken(lowerCAmelCase__     ,							lstrip=lowerCAmelCase__     ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__     ,							lowerCAmelCase__							) else pad_token
           super().__init__(
               bos_token=lowerCAmelCase__     ,							eos_token=lowerCAmelCase__     ,							unk_token=lowerCAmelCase__     ,							pad_token=lowerCAmelCase__     ,							add_bos_token=lowerCAmelCase__     ,							add_eos_token=lowerCAmelCase__     ,							sp_model_kwargs=self.sp_model_kwargs     ,							clean_up_tokenization_spaces=lowerCAmelCase__     ,							legacy=lowerCAmelCase__     ,							**lowerCAmelCase__     ,							)
           if legacy:
                logger.warning_once(
                    f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''							)
           __lowercase												=			legacy
           __lowercase												=			vocab_file
           __lowercase												=			add_bos_token
           __lowercase												=			add_eos_token
           __lowercase												=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
           self.sp_model.Load(lowerCAmelCase__							)







      def __getstate__(self							)			->						int:
           __lowercase												=			self.__dict__.copy()
           __lowercase												=			None
           __lowercase												=			self.sp_model.serialized_model_proto()
           return state







      def __setstate__(self     ,							UpperCAmelCase_							)			->						Union[str, Any]:
           __lowercase												=			d
           __lowercase												=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)







      @property
      def        _UpperCamelCase    (self							)			->						str:
           return self.sp_model.get_piece_size()







      def        _UpperCamelCase    (self							)			->						Any:
           __lowercase												=			{self.convert_ids_to_tokens(lowerCAmelCase__							): i for i in range(self.vocab_size							)}
           vocab.update(self.added_tokens_encoder							)
           return vocab







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							**UpperCAmelCase_							)			->						List[str]:




           if not self.legacy:
                __lowercase												=			SPIECE_UNDERLINE + text.replace(lowerCAmelCase__     ,							''' '''							)
           return super().tokenize(lowerCAmelCase__     ,							**lowerCAmelCase__							)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						List[Any]:




           if not self.legacy:
                __lowercase												=			text.startswith(lowerCAmelCase__							)
                if is_first:
                     __lowercase												=			text[1:]

           __lowercase												=			self.sp_model.encode(lowerCAmelCase__     ,							out_type=lowerCAmelCase__							)

           if not self.legacy and not is_first and not text.startswith(''' '''							) and tokens[0].startswith(lowerCAmelCase__							):
                __lowercase												=			([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
           return tokens







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Optional[Any]:
           return self.sp_model.piece_to_id(lowerCAmelCase__							)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Optional[Any]:
           __lowercase												=			self.sp_model.IdToPiece(lowerCAmelCase__							)
           return token







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Union[str, Any]:
           __lowercase												=			[]
           __lowercase												=			""
           __lowercase												=			False
           for i, token in enumerate(lowerCAmelCase__							):
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     if not prev_is_special and i != 0:
                          out_string += " "
                     out_string += self.sp_model.decode(lowerCAmelCase__							) + token
                     __lowercase												=			True
                     __lowercase												=			[]
                else:
                     current_sub_tokens.append(lowerCAmelCase__							)
                     __lowercase												=			False
           out_string += self.sp_model.decode(lowerCAmelCase__							)
           return out_string







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						Tuple[str]:




           if not os.path.isdir(lowerCAmelCase__							):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                return
           __lowercase												=			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__							) and os.path.isfile(self.vocab_file							):
                copyfile(self.vocab_file     ,							lowerCAmelCase__							)
           elif not os.path.isfile(self.vocab_file							):
                with open(lowerCAmelCase__     ,							'''wb'''							) as fi:
                     __lowercase												=			self.sp_model.serialized_model_proto()
                     fi.write(lowerCAmelCase__							)

           return (out_vocab_file,)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=None							)			->						str:
           __lowercase												=			[self.bos_token_id] if self.add_bos_token else []
           __lowercase												=			[self.eos_token_id] if self.add_eos_token else []

           __lowercase												=			bos_token_id + token_ids_a + eos_token_id

           if token_ids_a is not None:
                __lowercase												=			output + bos_token_id + token_ids_a + eos_token_id

           return output







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = False							)			->						List[int]:




           if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=lowerCAmelCase__     ,							token_ids_a=lowerCAmelCase__     ,							already_has_special_tokens=lowerCAmelCase__							)

           __lowercase												=			[1] if self.add_bos_token else []
           __lowercase												=			[1] if self.add_eos_token else []

           if token_ids_a is None:
                return bos_token_id + ([0] * len(lowerCAmelCase__							)) + eos_token_id
           return (
               bos_token_id
               + ([0] * len(lowerCAmelCase__							))
               + eos_token_id
               + bos_token_id
               + ([0] * len(lowerCAmelCase__							))
               + eos_token_id
           )







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						List[int]:
           __lowercase												=			[self.bos_token_id] if self.add_bos_token else []
           __lowercase												=			[self.eos_token_id] if self.add_eos_token else []

           __lowercase												=			[0] * len(bos_token_id + token_ids_a + eos_token_id							)

           if token_ids_a is not None:
                output += [1] * len(bos_token_id + token_ids_a + eos_token_id							)

           return output







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						List[int]:
           __lowercase												=			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...)'''							)

           __lowercase												=			[]
           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]:
                     __lowercase												=			(
                         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]:
                __lowercase												=			(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 json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


