


"""simple docstring"""




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

import sentencepiece as spm

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


if is_torch_available():
			import torch


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_snake_case					:		str      =    logging.get_logger(__name__)
_snake_case					:		List[str]      =    {"vocab_file": "spiece.model"}

_snake_case					:		int      =    {
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_snake_case					:		List[str]      =    {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}

class     a		(a__				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							Any					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							int					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Optional[int]					  =  ["input_ids", "attention_mask"]

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  str=False   ,     lowerCamelCase	:  str=False   ,     lowerCamelCase	:  Dict=False   ,     lowerCamelCase	:  Optional[int]=None   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=None   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  Optional[int] = None   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   None:
														__snake_case		:      Optional[Any]										=       {} if sp_model_kwargs is None else sp_model_kwargs

														__snake_case		:      Dict										=       kwargs.get("name_or_path"    )
														if name_or_path is None:
																					logger.warning(
																					    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
																					    " you are testing the model, this can safely be ignored"    )
																					__snake_case		:      Tuple										=       'None'

														# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
														__snake_case		:      str										=       '<|endoftext|>' if eos_token is None else eos_token
														__snake_case		:      Tuple										=       '<unk>' if unk_token is None else unk_token
														if "gpt-sw3-7b" in name_or_path:
																					__snake_case		:      str										=       unk_token if pad_token is None else pad_token
																					__snake_case		:      List[str]										=       eos_token if bos_token is None else bos_token
														else:
																					__snake_case		:      List[Any]										=       '<pad>' if pad_token is None else pad_token
																					__snake_case		:      Dict										=       '<s>' if bos_token is None else bos_token

														super().__init__(
														    do_lower_case=SCREAMING_SNAKE_CASE_   ,     remove_space=SCREAMING_SNAKE_CASE_   ,     keep_accents=SCREAMING_SNAKE_CASE_   ,     bos_token=SCREAMING_SNAKE_CASE_   ,     eos_token=SCREAMING_SNAKE_CASE_   ,     unk_token=SCREAMING_SNAKE_CASE_   ,     pad_token=SCREAMING_SNAKE_CASE_   ,     sp_model_kwargs=self.sp_model_kwargs   ,     **SCREAMING_SNAKE_CASE_   ,     )

														__snake_case		:      List[str]										=       do_lower_case
														__snake_case		:      List[str]										=       remove_space
														__snake_case		:      List[Any]										=       keep_accents
														__snake_case		:      List[str]										=       vocab_file

														__snake_case		:      Union[str, Any]										=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
														self.sp_model.Load(SCREAMING_SNAKE_CASE_    )

														# Used for whitespace normalization in input texts
														# fmt : off
														__snake_case		:      Dict										=       {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
														# fmt : on

														# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
														__snake_case		:      List[Any]										=       re.compile(
														    F'[{"".join(map(SCREAMING_SNAKE_CASE_   ,     list(range(0   ,     9    )    ) + list(range(11   ,     32    )    ) + list(range(127   ,     160    )    ) + [160, 173, 8203]    )    )}]'    )

							def __getstate__(							self	:  Any    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.__dict__.copy()
														__snake_case		:      Optional[int]										=       None
														return state

							def __setstate__(							self	:  Dict   ,     lowerCamelCase	:  List[str]    )     ->   Any:
														__snake_case		:      Any										=       d

														# for backward compatibility
														if not hasattr(self   ,     "sp_model_kwargs"    ):
																					__snake_case		:      Optional[int]										=       {}

														__snake_case		:      int										=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
														self.sp_model.Load(self.vocab_file    )

							@property
							# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
							def    __snake_case (							self	:  str    )     ->   int:
														return len(self.sp_model    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Optional[Any]    )     ->   str:
														__snake_case		:      Dict										=       self.non_printing_characters_re.sub(""   ,     SCREAMING_SNAKE_CASE_    )

														# Normalize whitespaces
														__snake_case		:      Any										=       ''.join([char if char not in self.whitespaces else " " for char in text]    )

														# NFC Unicode normalization
														__snake_case		:      Dict										=       unicodedata.normalize("NFC"   ,     SCREAMING_SNAKE_CASE_    )
														return text

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]   ,     **lowerCamelCase	:  int    )     ->   List[str]:
														__snake_case		:      Any										=       self.preprocess_text(SCREAMING_SNAKE_CASE_    )
														return self.sp_model.encode(SCREAMING_SNAKE_CASE_   ,     out_type=SCREAMING_SNAKE_CASE_    )

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Optional[int]    )     ->   int:
														return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_    )

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Union[str, Any]    )     ->   str:
														return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_    )

							@staticmethod
							def    __snake_case (							lowerCamelCase	:  List[str]    )     ->   str:
														return out_string

							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  List[str]    )     ->   str:
														__snake_case		:      Optional[Any]										=       []
														__snake_case		:      List[Any]										=       ''
														__snake_case		:      str										=       False
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																												if not prev_is_special:
																																			out_string += " "

																												out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_    ) + token
																												__snake_case		:      Dict										=       True
																												__snake_case		:      Optional[Any]										=       []
																					else:
																												current_sub_tokens.append(SCREAMING_SNAKE_CASE_    )
																												__snake_case		:      str										=       False
														out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_    )

														return out_string

							def    __snake_case (							self	:  str    )     ->   Dict[str, int]:
														__snake_case		:      Tuple										=       {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    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  int = None    )     ->   Tuple[str]:
														if not os.path.isdir(SCREAMING_SNAKE_CASE_    ):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																					return
														__snake_case		:      List[str]										=       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:
																												__snake_case		:      Any										=       self.sp_model.serialized_model_proto()
																												fi.write(SCREAMING_SNAKE_CASE_    )

														return (out_vocab_file,)

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Any = False    )     ->   Union[List[int], List[List[int]], "torch.Tensor"]:

														if isinstance(SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_    ):
																					__snake_case		:      List[str]										=       self.preprocess_text(SCREAMING_SNAKE_CASE_    )
																					__snake_case		:      Optional[Any]										=       self.sp_model.encode(SCREAMING_SNAKE_CASE_    )
														else:
																					__snake_case		:      Union[str, Any]										=       [self.preprocess_text(SCREAMING_SNAKE_CASE_    ) for t in text]
																					__snake_case		:      Any										=       self.sp_model.encode(SCREAMING_SNAKE_CASE_    )

														if return_tensors is True or return_tensors == "pt":
																					__snake_case		:      List[Any]										=       torch.tensor(SCREAMING_SNAKE_CASE_    )

														return token_ids

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Dict    )     ->   str:
														return self.sp_model.decode(SCREAMING_SNAKE_CASE_    )






							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Optional[Any]    )     ->   List[int]:
														__snake_case		:      List[Any]										=       [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
														__snake_case		:      Optional[Any]										=       (
														    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(SCREAMING_SNAKE_CASE_    ) + F'{self.bos_token}Bot:'
														)
														return self.encode(text=SCREAMING_SNAKE_CASE_    )





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_snake_case					:		List[str]      =    namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (abs(source - target     ) / target) < 0.0_1







@pytest.mark.integration
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       _TestCommandArgs(dataset=__lowerCamelCase							,     all_configs=__lowerCamelCase							,     save_infos=__lowerCamelCase     )
							__snake_case		:      List[Any]										=       TestCommand(*__lowerCamelCase     )
							test_command.run()
							__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     "README.md"     )
							assert os.path.exists(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       DatasetInfosDict.from_directory(__lowerCamelCase     )
							__snake_case		:      List[str]										=       DatasetInfosDict(
							    {
							        "default": DatasetInfo(
							            features=Features(
							                {
							                    "tokens": Sequence(Value("string"     )     ),
							                    "ner_tags": Sequence(
							                        ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]     )     ),
							                    "langs": Sequence(Value("string"     )     ),
							                    "spans": Sequence(Value("string"     )     ),
							                }     )							,     splits=[
							                {
							                    "name": "train",
							                    "num_bytes": 2_3_5_1_5_6_3,
							                    "num_examples": 1_0_0_0_0,
							                },
							                {
							                    "name": "validation",
							                    "num_bytes": 2_3_8_4_1_8,
							                    "num_examples": 1_0_0_0,
							                },
							            ]							,     download_size=3_9_4_0_6_8_0							,     dataset_size=2_5_8_9_9_8_1							,     )
							    }     )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														__snake_case			,      __snake_case		:      Tuple										=       getattr(dataset_infos["default"]							,     __lowerCamelCase     ), getattr(expected_dataset_infos["default"]							,     __lowerCamelCase     )
														if key == "num_bytes":
																					assert is_apercent_close(__lowerCamelCase							,     __lowerCamelCase     )
														elif key == "splits":
																					assert list(__lowerCamelCase     ) == list(__lowerCamelCase     )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes							,     expected[split].num_bytes     )
														else:
																					result == expected

