

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_ :				Dict						 =    logging.get_logger(__name__)
A_ :				Optional[Any]						 =    {'vocab_file': 'spiece.model'}

A_ :				List[str]						 =    {
    '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_ :				Optional[int]						 =    {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     int													= VOCAB_FILES_NAMES
					UpperCAmelCase__:     Optional[int]													= PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:     Optional[int]													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:     Union[str, Any]													= ['''input_ids''', '''attention_mask''']






					def __init__(				self					, A__					, A__=False					, A__=False					, A__=False					, A__=None					, A__=None					, A__=None					, A__=None					, A__ = None					, **A__					, ):
						A__							:					Optional[Any]       =	{} if sp_model_kwargs is None else sp_model_kwargs

						A__							:					str       =	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"""    )
							A__							:					Optional[Any]       =	"""None"""

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

						A__							:					Union[str, Any]       =	do_lower_case
						A__							:					int       =	remove_space
						A__							:					Any       =	keep_accents
						A__							:					Dict       =	vocab_file

						A__							:					List[Any]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
						self.sp_model.Load(A__    )

						# Used for whitespace normalization in input texts
						# fmt : off
						A__							:					Union[str, Any]       =	{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
						# fmt : on

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






					def __getstate__(				self    ):
						A__							:					Tuple       =	self.__dict__.copy()
						A__							:					Union[str, Any]       =	None
						return state






					def __setstate__(				self					, A__    ):
						A__							:					List[str]       =	d

						# for backward compatibility
						if not hasattr(self					, """sp_model_kwargs"""    ):
							A__							:					List[str]       =	{}

						A__							:					Union[str, Any]       =	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 						__A    (				self    ):
						return len(self.sp_model    )






					def 						__A    (				self					, A__    ):
						A__							:					Any       =	self.non_printing_characters_re.sub(""""""					, A__    )

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

						# NFC Unicode normalization
						A__							:					Tuple       =	unicodedata.normalize("""NFC"""					, A__    )
						return text






					def 						__A    (				self					, A__					, **A__    ):
						A__							:					Tuple       =	self.preprocess_text(A__    )
						return self.sp_model.encode(A__					, out_type=A__    )






					def 						__A    (				self					, A__    ):
						return self.sp_model.PieceToId(A__    )






					def 						__A    (				self					, A__    ):
						return self.sp_model.IdToPiece(A__    )






					@staticmethod
					def 						__A    (				A__    ):
						return out_string






					def 						__A    (				self					, A__    ):
						A__							:					int       =	[]
						A__							:					Dict       =	""""""
						A__							:					List[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(A__    ) + token
								A__							:					Optional[int]       =	True
								A__							:					Optional[Any]       =	[]
							else:
								current_sub_tokens.append(A__    )
								A__							:					List[Any]       =	False
						out_string += self.sp_model.decode(A__    )

						return out_string






					def 						__A    (				self    ):
						A__							:					List[Any]       =	{self.convert_ids_to_tokens(A__    ): i for i in range(self.vocab_size    )}
						vocab.update(self.added_tokens_encoder    )
						return vocab






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

						return (out_vocab_file,)






					def 						__A    (				self					, A__					, A__ = False    ):

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

						if return_tensors is True or return_tensors == "pt":
							A__							:					Union[str, Any]       =	torch.tensor(A__    )

						return token_ids






					def 						__A    (				self					, A__    ):
						return self.sp_model.decode(A__    )






					def 						__A    (				self					, A__    ):
						A__							:					List[Any]       =	[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
						A__							:					List[str]       =	(
						    F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(A__    ) + F"""{self.bos_token}Bot:"""
						)
						return self.encode(text=A__    )






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 		UpperCamelCase      (*lowercase_:				Optional[int]					,   lowercase_:				Optional[Union[Dict, Any]] = None					,   lowercase_:				Dict=True					,   lowercase_:				Tuple=2      )  ->   Dict:
	from .. import __version__

	A__							:					Dict       =	take_from
	A__							:					str       =	()
	if not isinstance(args[0]					,   lowercase_      ):
		A__							:					int       =	(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(lowercase_      ).base_version      ) >= version.parse(lowercase_      ):
			raise ValueError(
			    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
			    f""" version {__version__} is >= {version_name}"""      )

		A__							:					Any       =	None
		if isinstance(lowercase_					,   lowercase_      ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(lowercase_      ),)
			A__							:					List[str]       =	f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
		elif hasattr(lowercase_					,   lowercase_      ):
			values += (getattr(lowercase_					,   lowercase_      ),)
			A__							:					Optional[Any]       =	f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
		elif deprecated_kwargs is None:
			A__							:					int       =	f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

		if warning is not None:
			A__							:					int       =	warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message					,   lowercase_					,   stacklevel=lowercase_      )

	if isinstance(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
		A__							:					Union[str, Any]       =	inspect.getouterframes(inspect.currentframe()      )[1]
		A__							:					Optional[Any]       =	call_frame.filename
		A__							:					Optional[int]       =	call_frame.lineno
		A__							:					Any       =	call_frame.function
		A__     ,     A__							:					List[str]       =	next(iter(deprecated_kwargs.items()      )      )
		raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""      )

	if len(lowercase_      ) == 0:
		return
	elif len(lowercase_      ) == 1:
		return values[0]
	return values



