



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

_lowercase:					List[Any]      =						logging.get_logger(__name__)

_lowercase:					str      =						{"vocab_file": "tokenizer.model"}

_lowercase:					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",
    },
}
_lowercase:					Any      =						{
    "hf-internal-testing/llama-tokenizer": 2048,
}
_lowercase:					List[Any]      =						"▁"

_lowercase     ,							_lowercase:					Any      =						"[INST]", "[/INST]"
_lowercase     ,							_lowercase:					Union[str, Any]      =						"<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
_lowercase:					Optional[Any]      =						"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       _lowercase				(  _lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				["input_ids", "attention_mask"]




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_="<unk>"						,			lowerCamelCase_="<s>"						,			lowerCamelCase_="</s>"						,			lowerCamelCase_=None						,			lowerCamelCase_ = None						,			lowerCamelCase_=True						,			lowerCamelCase_=False						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  {} if sp_model_kwargs is None else sp_model_kwargs
						a   	=  AddedToken(_UpperCAmelCase						,			lstrip=_UpperCAmelCase						,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase						,			_UpperCAmelCase						) else bos_token
						a   	=  AddedToken(_UpperCAmelCase						,			lstrip=_UpperCAmelCase						,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase						,			_UpperCAmelCase						) else eos_token
						a   	=  AddedToken(_UpperCAmelCase						,			lstrip=_UpperCAmelCase						,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase						,			_UpperCAmelCase						) else unk_token
						a   	=  AddedToken(_UpperCAmelCase						,			lstrip=_UpperCAmelCase						,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase						,			_UpperCAmelCase						) else pad_token
						super().__init__(
						    bos_token=_UpperCAmelCase						,			eos_token=_UpperCAmelCase						,			unk_token=_UpperCAmelCase						,			pad_token=_UpperCAmelCase						,			add_bos_token=_UpperCAmelCase						,			add_eos_token=_UpperCAmelCase						,			sp_model_kwargs=self.sp_model_kwargs						,			clean_up_tokenization_spaces=_UpperCAmelCase						,			legacy=_UpperCAmelCase						,			**_UpperCAmelCase						,			)
						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"						)
						a   	=  legacy
						a   	=  vocab_file
						a   	=  add_bos_token
						a   	=  add_eos_token
						a   	=  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
						self.sp_model.Load(_UpperCAmelCase						)




	def __getstate__(self						):
						"""simple docstring"""

						a   	=  self.__dict__.copy()
						a   	=  None
						a   	=  self.sp_model.serialized_model_proto()
						return state




	def __setstate__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  d
						a   	=  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.sp_model.get_piece_size()




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {self.convert_ids_to_tokens(_UpperCAmelCase						): i for i in range(self.vocab_size						)}
						vocab.update(self.added_tokens_encoder						)
						return vocab




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			**lowerCamelCase_						):
						"""simple docstring"""

						if not self.legacy:
											a   	=  SPIECE_UNDERLINE + text.replace(_UpperCAmelCase						,			" "						)
						return super().tokenize(_UpperCAmelCase						,			**_UpperCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if not self.legacy:
											a   	=  text.startswith(_UpperCAmelCase						)
											if is_first:
																a   	=  text[1:]

						a   	=  self.sp_model.encode(_UpperCAmelCase						,			out_type=_UpperCAmelCase						)

						if not self.legacy and not is_first and not text.startswith(" "						) and tokens[0].startswith(_UpperCAmelCase						):
											a   	=  ([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
						return tokens




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						return self.sp_model.piece_to_id(_UpperCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  self.sp_model.IdToPiece(_UpperCAmelCase						)
						return token




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  []
						a   	=  ''
						a   	=  False
						for i, token in enumerate(_UpperCAmelCase						):
											# 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(_UpperCAmelCase						) + token
																a   	=  True
																a   	=  []
											else:
																current_sub_tokens.append(_UpperCAmelCase						)
																a   	=  False
						out_string += self.sp_model.decode(_UpperCAmelCase						)
						return out_string




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

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

						return (out_vocab_file,)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_=None						):
						"""simple docstring"""

						a   	=  [self.bos_token_id] if self.add_bos_token else []
						a   	=  [self.eos_token_id] if self.add_eos_token else []

						a   	=  bos_token_id + token_ids_a + eos_token_id

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

						return output




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			lowerCamelCase_ = False						):
						"""simple docstring"""

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

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

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  [self.bos_token_id] if self.add_bos_token else []
						a   	=  [self.eos_token_id] if self.add_eos_token else []

						a   	=  [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						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  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   	=  []
						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]:
																a   	=  (
																    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]:
											a   	=  (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



from __future__ import annotations





def  _snake_case		(snake_case_					:     str  )							->		bool:
  __UpperCAmelCase	:  int
  __UpperCAmelCase	:  list[str]		=     list(snake_case_  )
  __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     _vector[:-1], int(_vector[-1]  )
  __UpperCAmelCase	:  list[int]		=     [int(snake_case_  ) for digit in __vector]

  vector.reverse()
  for i, digit in enumerate(snake_case_  ):
    if i & 1 == 0:
      __UpperCAmelCase	:  int		=     digit * 2
      if doubled > 9:
        doubled -= 9
      check_digit += doubled
    else:
      check_digit += digit

  return check_digit % 10 == 0


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      assert is_luhn('''79927398713''')
      assert not is_luhn('''79927398714''')





