
"""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

lowercase		:    Optional[Any]			=       logging.get_logger(__name__)

lowercase		:    Union[str, Any]			=       {'vocab_file': 'tokenizer.model'}

lowercase		:    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',
    },
}
lowercase		:    Optional[int]			=       {
    'hf-internal-testing/llama-tokenizer': 20_48,
}
lowercase		:    int			=       '▁'

lowercase,lowercase		:    List[str]			=       '[INST]', '[/INST]'
lowercase,lowercase		:    List[Any]			=       '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
lowercase		:    List[str]			=       '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	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE		  =    PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE		  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE		  =    ['''input_ids''', '''attention_mask''']






							def __init__(       self    :      List[Any]    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      Any="<unk>"    ,			snake_case__    :      Dict="<s>"    ,			snake_case__    :      Dict="</s>"    ,			snake_case__    :      Optional[Any]=None    ,			snake_case__    :      Optional[Dict[str, Any]] = None    ,			snake_case__    :      List[Any]=True    ,			snake_case__    :      str=False    ,			snake_case__    :      Union[str, Any]=False    ,			snake_case__    :      int=True    ,			**snake_case__    :      List[str]    ,			):






												"""simple docstring"""



												__a:    List[Any]		     =       {} if sp_model_kwargs is None else sp_model_kwargs
												__a:    Any		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else bos_token
												__a:    Any		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else eos_token
												__a:    Union[str, Any]		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else unk_token
												__a:    Tuple		     =       AddedToken(snake_case__    ,			lstrip=snake_case__    ,			rstrip=snake_case__     ) if isinstance(snake_case__    ,			snake_case__     ) else pad_token
												super().__init__(
												    bos_token=snake_case__    ,			eos_token=snake_case__    ,			unk_token=snake_case__    ,			pad_token=snake_case__    ,			add_bos_token=snake_case__    ,			add_eos_token=snake_case__    ,			sp_model_kwargs=self.sp_model_kwargs    ,			clean_up_tokenization_spaces=snake_case__    ,			legacy=snake_case__    ,			**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"""     )
												__a:    Union[str, Any]		     =       legacy
												__a:    Tuple		     =       vocab_file
												__a:    Optional[Any]		     =       add_bos_token
												__a:    List[Any]		     =       add_eos_token
												__a:    Optional[Any]		     =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
												self.sp_model.Load(snake_case__     )






							def __getstate__(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Tuple		     =       self.__dict__.copy()
												__a:    Dict		     =       None
												__a:    List[Any]		     =       self.sp_model.serialized_model_proto()
												return state






							def __setstate__(       self    :      Any    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    List[str]		     =       d
												__a:    Any		     =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return self.sp_model.get_piece_size()






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												__a:    Any		     =       {self.convert_ids_to_tokens(snake_case__     ): i for i in range(self.vocab_size     )}
												vocab.update(self.added_tokens_encoder     )
												return vocab






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      Tuple    ,			**snake_case__    :      Dict     ):






												"""simple docstring"""



												if not self.legacy:
																	__a:    Optional[int]		     =       SPIECE_UNDERLINE + text.replace(snake_case__    ,			""" """     )
												return super().tokenize(snake_case__    ,			**snake_case__     )






							def 				__lowercase	(       self    :      Any    ,			snake_case__    :      Dict     ):






												"""simple docstring"""



												if not self.legacy:
																	__a:    int		     =       text.startswith(snake_case__     )
																	if is_first:
																						__a:    Union[str, Any]		     =       text[1:]

												__a:    List[str]		     =       self.sp_model.encode(snake_case__    ,			out_type=snake_case__     )

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






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Any     ):






												"""simple docstring"""



												return self.sp_model.piece_to_id(snake_case__     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      Any     ):






												"""simple docstring"""



												__a:    int		     =       self.sp_model.IdToPiece(snake_case__     )
												return token






							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       []
												__a:    Tuple		     =       """"""
												__a:    Tuple		     =       False
												for i, token in enumerate(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(snake_case__     ) + token
																						__a:    Optional[Any]		     =       True
																						__a:    List[Any]		     =       []
																	else:
																						current_sub_tokens.append(snake_case__     )
																						__a:    Union[str, Any]		     =       False
												out_string += self.sp_model.decode(snake_case__     )
												return out_string






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      List[str]    ,			snake_case__    :      Optional[str] = None     ):






												"""simple docstring"""



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

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

												return (out_vocab_file,)






							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      Dict    ,			snake_case__    :      int=None     ):






												"""simple docstring"""



												__a:    int		     =       [self.bos_token_id] if self.add_bos_token else []
												__a:    Optional[Any]		     =       [self.eos_token_id] if self.add_eos_token else []

												__a:    Dict		     =       bos_token_id + token_ids_a + eos_token_id

												if token_ids_a is not None:
																	__a:    str		     =       output + bos_token_id + token_ids_a + eos_token_id

												return output






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      List[int]    ,			snake_case__    :      Optional[List[int]] = None    ,			snake_case__    :      bool = False     ):






												"""simple docstring"""



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

												__a:    Dict		     =       [1] if self.add_bos_token else []
												__a:    Optional[int]		     =       [1] if self.add_eos_token else []

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






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      List[int]    ,			snake_case__    :      Optional[List[int]] = None     ):






												"""simple docstring"""



												__a:    Any		     =       [self.bos_token_id] if self.add_bos_token else []
												__a:    Union[str, Any]		     =       [self.eos_token_id] if self.add_eos_token else []

												__a:    Union[str, Any]		     =       [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 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      "Conversation"     ):






												"""simple docstring"""



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


"""simple docstring"""
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property




class  _snake_case	(    unittest.TestCase							):






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.tokenizer_ab
												__a:    Union[str, Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Optional[Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.rust_tokenizer_ab
												__a:    Optional[Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Union[str, Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												assert self.tokenizer_ab.add_prefix_space
												assert self.tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												assert self.rust_tokenizer_ab.add_prefix_space
												assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]
