


"""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

UpperCamelCase__							:Union[str, Any]       =  logging.get_logger(__name__)

UpperCamelCase__							:Union[str, Any]       =  {"""vocab_file""": """tokenizer.model"""}

UpperCamelCase__							:List[str]       =  {
    """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""",
    },
}
UpperCamelCase__							:int       =  {
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCamelCase__							:Dict       =  """▁"""

UpperCamelCase__      , UpperCamelCase__							:List[Any]       =  """[INST]""", """[/INST]"""
UpperCamelCase__      , UpperCamelCase__							:Optional[Any]       =  """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCamelCase__							: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     A(     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				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__="<unk>"				,							SCREAMING_SNAKE_CASE__="<s>"				,							SCREAMING_SNAKE_CASE__="</s>"				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=True				,							**SCREAMING_SNAKE_CASE__				,							)			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  {} if sp_model_kwargs is None else sp_model_kwargs
					_UpperCamelCase				:Dict				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else bos_token
					_UpperCamelCase				:Tuple				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else eos_token
					_UpperCamelCase				:Optional[int]				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else unk_token
					_UpperCamelCase				:Optional[Any]				    =  AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__      ) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ) else pad_token
					super().__init__(
					    bos_token=SCREAMING_SNAKE_CASE__				,							eos_token=SCREAMING_SNAKE_CASE__				,							unk_token=SCREAMING_SNAKE_CASE__				,							pad_token=SCREAMING_SNAKE_CASE__				,							add_bos_token=SCREAMING_SNAKE_CASE__				,							add_eos_token=SCREAMING_SNAKE_CASE__				,							sp_model_kwargs=self.sp_model_kwargs				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__				,							legacy=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__				,							)
					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'''      )
					_UpperCamelCase				:List[Any]				    =  legacy
					_UpperCamelCase				:Optional[Any]				    =  vocab_file
					_UpperCamelCase				:Optional[int]				    =  add_bos_token
					_UpperCamelCase				:List[str]				    =  add_eos_token
					_UpperCamelCase				:List[str]				    =  spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(SCREAMING_SNAKE_CASE__      )
		def __getstate__(   self      )			->  Tuple:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.__dict__.copy()
					_UpperCamelCase				:Union[str, Any]				    =  None
					_UpperCamelCase				:List[str]				    =  self.sp_model.serialized_model_proto()
					return state
		def __setstate__(   self				,							SCREAMING_SNAKE_CASE__      )			->  Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  d
					_UpperCamelCase				:Any				    =  spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )
		@property
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					return self.sp_model.get_piece_size()
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      ): i for i in range(self.vocab_size      )}
					vocab.update(self.added_tokens_encoder      )
					return vocab
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )			->  List[str]:

					"""simple docstring"""



					if not self.legacy:
								_UpperCamelCase				:Any				    =  SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE__				,							''' '''      )
					return super().tokenize(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					if not self.legacy:
								_UpperCamelCase				:Any				    =  text.startswith(SCREAMING_SNAKE_CASE__      )
								if is_first:
											_UpperCamelCase				:List[str]				    =  text[1:]

					_UpperCamelCase				:Tuple				    =  self.sp_model.encode(SCREAMING_SNAKE_CASE__				,							out_type=SCREAMING_SNAKE_CASE__      )

					if not self.legacy and not is_first and not text.startswith(''' '''      ) and tokens[0].startswith(SCREAMING_SNAKE_CASE__      ):
								_UpperCamelCase				:Tuple				    =  ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
					return tokens
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__      )
					return token
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  []
					_UpperCamelCase				:List[str]				    =  ''''''
					_UpperCamelCase				:str				    =  False
					for i, token in enumerate(SCREAMING_SNAKE_CASE__      ):
								# 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(SCREAMING_SNAKE_CASE__      ) + token
											_UpperCamelCase				:Union[str, Any]				    =  True
											_UpperCamelCase				:Any				    =  []
								else:
											current_sub_tokens.append(SCREAMING_SNAKE_CASE__      )
											_UpperCamelCase				:List[Any]				    =  False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__      )
					return out_string
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  Tuple[str]:

					"""simple docstring"""



					if not os.path.isdir(SCREAMING_SNAKE_CASE__      ):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
								return
					_UpperCamelCase				:Tuple				    =  os.path.join(
					    SCREAMING_SNAKE_CASE__				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

					if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE__      ) and os.path.isfile(self.vocab_file      ):
								copyfile(self.vocab_file				,							SCREAMING_SNAKE_CASE__      )
					elif not os.path.isfile(self.vocab_file      ):
								with open(SCREAMING_SNAKE_CASE__				,							'''wb'''      ) as fi:
											_UpperCamelCase				:List[Any]				    =  self.sp_model.serialized_model_proto()
											fi.write(SCREAMING_SNAKE_CASE__      )

					return (out_vocab_file,)
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=None      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  [self.bos_token_id] if self.add_bos_token else []
					_UpperCamelCase				:str				    =  [self.eos_token_id] if self.add_eos_token else []

					_UpperCamelCase				:Optional[int]				    =  bos_token_id + token_ids_a + eos_token_id

					if token_ids_a is not None:
								_UpperCamelCase				:List[str]				    =  output + bos_token_id + token_ids_a + eos_token_id

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

					"""simple docstring"""



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

					_UpperCamelCase				:Tuple				    =  [1] if self.add_bos_token else []
					_UpperCamelCase				:List[Any]				    =  [1] if self.add_eos_token else []

					if token_ids_a is None:
								return bos_token_id + ([0] * len(SCREAMING_SNAKE_CASE__      )) + eos_token_id
					return (
					    bos_token_id
					    + ([0] * len(SCREAMING_SNAKE_CASE__      ))
					    + eos_token_id
					    + bos_token_id
					    + ([0] * len(SCREAMING_SNAKE_CASE__      ))
					    + eos_token_id
					)
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__ = None      )			->  List[int]:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  [self.bos_token_id] if self.add_bos_token else []
					_UpperCamelCase				:Optional[int]				    =  [self.eos_token_id] if self.add_eos_token else []

					_UpperCamelCase				:Tuple				    =  [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				,							SCREAMING_SNAKE_CASE__      )			->  List[int]:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  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...)'''      )

					_UpperCamelCase				:List[int]				    =  []
					if len(conversation.past_user_inputs      ) > 0:
								if not conversation.past_user_inputs[0].startswith(SCREAMING_SNAKE_CASE__      ) or E_SYS not in conversation.past_user_inputs[0]:
											_UpperCamelCase				:List[Any]				    =  (
											    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
											)
					elif not dialogue[0][1].startswith(SCREAMING_SNAKE_CASE__      ) or E_SYS not in dialogue[0][1]:
								_UpperCamelCase				:List[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=SCREAMING_SNAKE_CASE__      )
					        + [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=SCREAMING_SNAKE_CASE__      )
					return dialog_tokens





"""simple docstring"""






def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:Tuple				    =  word.upper()
			_UpperCamelCase				:Dict				    =  0
			_UpperCamelCase				:Union[str, Any]				    =  {}
			_UpperCamelCase				:Optional[int]				    =  []

			for letter in word:
						if letter not in letter_nums:
									_UpperCamelCase				:Dict				    =  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
					import pprint
					import time

					UpperCamelCase__							:Dict       =  time.time()
					with open("""dictionary.txt""") as in_file:
										UpperCamelCase__							:str       =  in_file.read().splitlines()

					UpperCamelCase__							:dict       =  {}
					for word in word_list:
										UpperCamelCase__							:List[Any]       =  get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															UpperCamelCase__							:Tuple       =  [word]

					with open("""word_patterns.txt""", """w""") as out_file:
										out_file.write(pprint.pformat(all_patterns))

					UpperCamelCase__							:Dict       =  round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.

