




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


__A						:							str							=  logging.get_logger(__name__)
__A						:							Optional[int]							=  {'vocab_file': 'spiece.model'}

__A						:							Any							=  {
    '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',
    }
}

__A						:							str							=  {
    'AI-Sweden/gpt-sw3-126m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-350m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-1.6b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-6.7b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-20b': 2_0_4_8,
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     ['''input_ids''', '''attention_mask''']





							def __init__(					self       :	Dict       ,     a       :	List[str]       ,     a       :	Any=False       ,     a       :	Tuple=False       ,     a       :	Dict=False       ,     a       :	str=None       ,     a       :	List[str]=None       ,     a       :	Any=None       ,     a       :	str=None       ,     a       :	Optional[Dict[str, Any]] = None       ,     **a       :	str       ,     )			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					{} if sp_model_kwargs is None else sp_model_kwargs

										SCREAMING_SNAKE_CASE           =					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"""			)
													SCREAMING_SNAKE_CASE           =					"None"

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

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

										SCREAMING_SNAKE_CASE           =					do_lower_case
										SCREAMING_SNAKE_CASE           =					remove_space
										SCREAMING_SNAKE_CASE           =					keep_accents
										SCREAMING_SNAKE_CASE           =					vocab_file

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(__A			)

										# Used for whitespace normalization in input texts
										# fmt : off
										SCREAMING_SNAKE_CASE           =					{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
										# fmt : on

										# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
										SCREAMING_SNAKE_CASE           =					re.compile(
										    f"""[{"".join(map(__A       ,     list(range(0       ,     9			)			) + list(range(11       ,     32			)			) + list(range(127       ,     160			)			) + [160, 173, 8_203]			)			)}]"""			)





							def __getstate__(					self       :	Dict			)			->					Any:
										SCREAMING_SNAKE_CASE           =					self.__dict__.copy()
										SCREAMING_SNAKE_CASE           =					None
										return state





							def __setstate__(					self       :	Tuple       ,     a       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					d

										# for backward compatibility
										if not hasattr(self       ,     """sp_model_kwargs"""			):
													SCREAMING_SNAKE_CASE           =					{}

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	int			)			->					List[Any]:
										return len(self.sp_model			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str			)			->					Any:
										SCREAMING_SNAKE_CASE           =					self.non_printing_characters_re.sub(""""""       ,     __A			)

										# Normalize whitespaces
										SCREAMING_SNAKE_CASE           =					"".join([char if char not in self.whitespaces else """ """ for char in text]			)

										# NFC Unicode normalization
										SCREAMING_SNAKE_CASE           =					unicodedata.normalize("""NFC"""       ,     __A			)
										return text





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     **a       :	Optional[int]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.preprocess_text(__A			)
										return self.sp_model.encode(__A       ,     out_type=__A			)





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	str			)			->					Tuple:
										return self.sp_model.PieceToId(__A			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	int			)			->					List[str]:
										return self.sp_model.IdToPiece(__A			)





							@staticmethod
							def    _UpperCAmelCase     (					a       :	str			)			->					Optional[int]:
										return out_string





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					""
										SCREAMING_SNAKE_CASE           =					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(__A			) + token
																SCREAMING_SNAKE_CASE           =					True
																SCREAMING_SNAKE_CASE           =					[]
													else:
																current_sub_tokens.append(__A			)
																SCREAMING_SNAKE_CASE           =					False
										out_string += self.sp_model.decode(__A			)

										return out_string





							def    _UpperCAmelCase     (					self       :	Any			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					{self.convert_ids_to_tokens(__A			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					int:
										if not os.path.isdir(__A			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
													return
										SCREAMING_SNAKE_CASE           =					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:
																SCREAMING_SNAKE_CASE           =					self.sp_model.serialized_model_proto()
																fi.write(__A			)

										return (out_vocab_file,)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	Union[str, List[str]]       ,     a       :	Union[str, bool] = False			)			->					Optional[Any]:

										if isinstance(__A       ,     __A			):
													SCREAMING_SNAKE_CASE           =					self.preprocess_text(__A			)
													SCREAMING_SNAKE_CASE           =					self.sp_model.encode(__A			)
										else:
													SCREAMING_SNAKE_CASE           =					[self.preprocess_text(__A			) for t in text]
													SCREAMING_SNAKE_CASE           =					self.sp_model.encode(__A			)

										if return_tensors is True or return_tensors == "pt":
													SCREAMING_SNAKE_CASE           =					torch.tensor(__A			)

										return token_ids





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Union[int, List[int]]			)			->					Dict:
										return self.sp_model.decode(__A			)







							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	"Conversation"			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					[f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
										SCREAMING_SNAKE_CASE           =					(
										    f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__A			) + f"""{self.bos_token}Bot:"""
										)
										return self.encode(text=__A			)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






