



'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case(      _lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Any           =		MPNetTokenizer
						UpperCAmelCase							:    Any           =		MPNetTokenizerFast
						UpperCAmelCase							:    str           =		True
						UpperCAmelCase							:    int           =		True


						def 				__snake_case    (					self      )	->	int:
							super().setUp()

							lowerCAmelCase    =	[
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """[PAD]""",
							    """[MASK]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase    =	os.path.join(self.tmpdirname	,       VOCAB_FILES_NAMES["""vocab_file"""]      )
							with open(self.vocab_file	,       """w"""	,       encoding="""utf-8"""      ) as vocab_writer:
								vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )


						def 				__snake_case    (					self	,       A_      )	->	Optional[int]:
							lowerCAmelCase    =	"""UNwant\u00E9d,running"""
							lowerCAmelCase    =	"""unwanted, running"""
							return input_text, output_text


						def 				__snake_case    (					self      )	->	Dict:
							lowerCAmelCase    =	self.tokenizer_class(self.vocab_file      )

							lowerCAmelCase    =	tokenizer.tokenize("""UNwant\u00E9d,running"""      )
							self.assertListEqual(A_	,       ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]      )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_      )	,       [9, 6, 7, 12, 10, 11]      )




						@slow
						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""      )

							lowerCAmelCase    =	tokenizer.encode("""sequence builders"""	,       add_special_tokens=A_      )
							lowerCAmelCase    =	tokenizer.encode("""multi-sequence build"""	,       add_special_tokens=A_      )

							lowerCAmelCase    =	tokenizer.build_inputs_with_special_tokens(A_      )
							lowerCAmelCase    =	tokenizer.build_inputs_with_special_tokens(A_	,       A_      )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]




'''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			=    logging.get_logger(__name__)

UpperCAmelCase			=    {'vocab_file': 'tokenizer.model'}

UpperCAmelCase			=    {
    '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			=    {
    'hf-internal-testing/llama-tokenizer': 2048,
}
UpperCAmelCase			=    '▁'

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 __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    str           =		VOCAB_FILES_NAMES
						UpperCAmelCase							:    Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:    Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:    Tuple           =		["input_ids", "attention_mask"]


						def __init__(					self	,       A_	,       A_="<unk>"	,       A_="<s>"	,       A_="</s>"	,       A_=None	,       A_ = None	,       A_=True	,       A_=False	,       A_=False	,       A_=True	,       **A_	,       )	->	Dict:
							lowerCAmelCase    =	{} if sp_model_kwargs is None else sp_model_kwargs
							lowerCAmelCase    =	AddedToken(A_	,       lstrip=A_	,       rstrip=A_      ) if isinstance(A_	,       A_      ) else bos_token
							lowerCAmelCase    =	AddedToken(A_	,       lstrip=A_	,       rstrip=A_      ) if isinstance(A_	,       A_      ) else eos_token
							lowerCAmelCase    =	AddedToken(A_	,       lstrip=A_	,       rstrip=A_      ) if isinstance(A_	,       A_      ) else unk_token
							lowerCAmelCase    =	AddedToken(A_	,       lstrip=A_	,       rstrip=A_      ) if isinstance(A_	,       A_      ) else pad_token
							super().__init__(
							    bos_token=A_	,       eos_token=A_	,       unk_token=A_	,       pad_token=A_	,       add_bos_token=A_	,       add_eos_token=A_	,       sp_model_kwargs=self.sp_model_kwargs	,       clean_up_tokenization_spaces=A_	,       legacy=A_	,       **A_	,       )
							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"""      )
							lowerCAmelCase    =	legacy
							lowerCAmelCase    =	vocab_file
							lowerCAmelCase    =	add_bos_token
							lowerCAmelCase    =	add_eos_token
							lowerCAmelCase    =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(A_      )


						def __getstate__(					self      )	->	List[str]:
							lowerCAmelCase    =	self.__dict__.copy()
							lowerCAmelCase    =	None
							lowerCAmelCase    =	self.sp_model.serialized_model_proto()
							return state


						def __setstate__(					self	,       A_      )	->	List[Any]:
							lowerCAmelCase    =	d
							lowerCAmelCase    =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )


						@property
						def 				__snake_case    (					self      )	->	Optional[int]:
							return self.sp_model.get_piece_size()


						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	{self.convert_ids_to_tokens(A_      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab


						def 				__snake_case    (					self	,       A_	,       **A_      )	->	List[str]:
							# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
							# the beginning of the text
							if not self.legacy:
								lowerCAmelCase    =	SPIECE_UNDERLINE + text.replace(A_	,       """ """      )
							return super().tokenize(A_	,       **A_      )


						def 				__snake_case    (					self	,       A_      )	->	Tuple:
							if not self.legacy:
								lowerCAmelCase    =	text.startswith(A_      )
								if is_first:
									lowerCAmelCase    =	text[1:]

							lowerCAmelCase    =	self.sp_model.encode(A_	,       out_type=A_      )

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


						def 				__snake_case    (					self	,       A_      )	->	List[str]:
							return self.sp_model.piece_to_id(A_      )


						def 				__snake_case    (					self	,       A_      )	->	str:
							lowerCAmelCase    =	self.sp_model.IdToPiece(A_      )
							return token


						def 				__snake_case    (					self	,       A_      )	->	Dict:
							lowerCAmelCase    =	[]
							lowerCAmelCase    =	""""""
							lowerCAmelCase    =	False
							for i, token in enumerate(A_      ):
								# 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(A_      ) + token
									lowerCAmelCase    =	True
									lowerCAmelCase    =	[]
								else:
									current_sub_tokens.append(A_      )
									lowerCAmelCase    =	False
							out_string += self.sp_model.decode(A_      )
							return out_string


						def 				__snake_case    (					self	,       A_	,       A_ = None      )	->	Tuple[str]:
							if not os.path.isdir(A_      ):
								logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
								return
							lowerCAmelCase    =	os.path.join(
							    A_	,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

							if os.path.abspath(self.vocab_file      ) != os.path.abspath(A_      ) and os.path.isfile(self.vocab_file      ):
								copyfile(self.vocab_file	,       A_      )
							elif not os.path.isfile(self.vocab_file      ):
								with open(A_	,       """wb"""      ) as fi:
									lowerCAmelCase    =	self.sp_model.serialized_model_proto()
									fi.write(A_      )

							return (out_vocab_file,)


						def 				__snake_case    (					self	,       A_	,       A_=None      )	->	Any:
							lowerCAmelCase    =	[self.bos_token_id] if self.add_bos_token else []
							lowerCAmelCase    =	[self.eos_token_id] if self.add_eos_token else []

							lowerCAmelCase    =	bos_token_id + token_ids_a + eos_token_id

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

							return output


						def 				__snake_case    (					self	,       A_	,       A_ = None	,       A_ = False      )	->	List[int]:
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,       token_ids_a=A_	,       already_has_special_tokens=A_      )

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

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


						def 				__snake_case    (					self	,       A_	,       A_ = None      )	->	List[int]:
							lowerCAmelCase    =	[self.bos_token_id] if self.add_bos_token else []
							lowerCAmelCase    =	[self.eos_token_id] if self.add_eos_token else []

							lowerCAmelCase    =	[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 				__snake_case    (					self	,       A_      )	->	List[int]:
							lowerCAmelCase    =	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...)"""      )

							lowerCAmelCase    =	[]
							if len(conversation.past_user_inputs      ) > 0:
								if not conversation.past_user_inputs[0].startswith(A_      ) or E_SYS not in conversation.past_user_inputs[0]:
									lowerCAmelCase    =	(
									    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
									)
							elif not dialogue[0][1].startswith(A_      ) or E_SYS not in dialogue[0][1]:
								lowerCAmelCase    =	(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=A_      )
							        + [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=A_      )
							return dialog_tokens