

"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{"vocab_file": "spiece.model"}

_lowerCAmelCase         =							{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCAmelCase         =							{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

_lowerCAmelCase         =							"▁"



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




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





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_="</s>"     ,     lowerCAmelCase_="<unk>"     ,     lowerCAmelCase_="<pad>"     ,     lowerCAmelCase_=100     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_=True     ,     **lowerCAmelCase_     ,     ):
		# Add extra_ids to the special token list
		if extra_ids > 0 and additional_special_tokens is None:
			A_		:						List[str]	    =   [f"""<extra_id_{i}>""" for i in range(lowerCAmelCase_							)]
		elif extra_ids > 0 and additional_special_tokens is not None:
			# Check that we have the right number of extra_id special tokens
			A_		:						Optional[Any]	    =   len(set(filter(lambda lowerCAmelCase_							: bool("""extra_id""" in str(lowerCAmelCase_							)							)     ,     lowerCAmelCase_							)							)							)
			if extra_tokens != extra_ids:
				raise ValueError(
				    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
				    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
				    """ tokens"""							)
		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_		:						Optional[int]	    =   legacy
		A_		:						Optional[int]	    =   {} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     extra_ids=lowerCAmelCase_     ,     additional_special_tokens=lowerCAmelCase_     ,     sp_model_kwargs=self.sp_model_kwargs     ,     legacy=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )

		A_		:						int	    =   vocab_file
		A_		:						int	    =   extra_ids

		A_		:						Tuple	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(lowerCAmelCase_							)





	@staticmethod
	def    lowerCamelCase(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
			A_		:						Optional[int]	    =   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
			if init_max_model_length is not None and init_max_model_length != max_model_length:
				return init_max_model_length
			elif init_max_model_length is None:
				warnings.warn(
				    """This tokenizer was incorrectly instantiated with a model max length of"""
				    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
				    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
				    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
				    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
				    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
				    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
				    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
				    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""     ,     lowerCAmelCase_     ,     )

		return max_model_length





	@property
	def    lowerCamelCase(self							):
		return self.sp_model.get_piece_size() + self._extra_ids





	def    lowerCamelCase(self							):
		A_		:						str	    =   {self.convert_ids_to_tokens(lowerCAmelCase_							): i for i in range(self.vocab_size							)}
		vocab.update(self.added_tokens_encoder							)
		return vocab





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = False							):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=lowerCAmelCase_     ,     token_ids_a=lowerCAmelCase_     ,     already_has_special_tokens=lowerCAmelCase_							)

		# normal case: some special tokens
		if token_ids_a is None:
			return ([0] * len(lowerCAmelCase_							)) + [1]
		return ([0] * len(lowerCAmelCase_							)) + [1] + ([0] * len(lowerCAmelCase_							)) + [1]





	def    lowerCamelCase(self							):
		return list(
		    set(filter(lambda lowerCAmelCase_							: bool(re.search(r"""<extra_id_\d+>"""     ,     lowerCAmelCase_							)							) is not None     ,     self.additional_special_tokens							)							)							)





	def    lowerCamelCase(self							):
		return [self._convert_token_to_id(lowerCAmelCase_							) for token in self.get_sentinel_tokens()]





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if len(lowerCAmelCase_							) > 0 and token_ids[-1] == self.eos_token_id:
			warnings.warn(
			    f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
			    """ eos tokens being added."""							)
			return token_ids
		else:
			return token_ids + [self.eos_token_id]





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						List[str]	    =   [self.eos_token_id]

		if token_ids_a is None:
			return len(token_ids_a + eos							) * [0]
		return len(token_ids_a + eos + token_ids_a + eos							) * [0]





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						List[Any]	    =   self._add_eos_if_not_present(lowerCAmelCase_							)
		if token_ids_a is None:
			return token_ids_a
		else:
			A_		:						Dict	    =   self._add_eos_if_not_present(lowerCAmelCase_							)
			return token_ids_a + token_ids_a





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





	def __setstate__(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   d

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

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		# 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:
			A_		:						int	    =   SPIECE_UNDERLINE + text.replace(lowerCAmelCase_     ,     """ """							)
		return super().tokenize(lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		if not self.legacy:
			A_		:						Tuple	    =   text.startswith(lowerCAmelCase_							)
			if is_first:
				A_		:						int	    =   text[1:]

		A_		:						Union[str, Any]	    =   self.sp_model.encode(lowerCAmelCase_     ,     out_type=lowerCAmelCase_							)

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if token.startswith("""<extra_id_"""							):
			A_		:						Union[str, Any]	    =   re.match(r"""<extra_id_(\d+)>"""     ,     lowerCAmelCase_							)
			A_		:						Tuple	    =   int(match.group(1							)							)
			return self.vocab_size - num - 1
		return self.sp_model.piece_to_id(lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if index < self.sp_model.get_piece_size():
			A_		:						Optional[int]	    =   self.sp_model.IdToPiece(lowerCAmelCase_							)
		else:
			A_		:						Tuple	    =   f"""<extra_id_{self.vocab_size - 1 - index}>"""
		return token





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Any	    =   []
		A_		:						str	    =   """"""
		A_		:						Dict	    =   False
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				if not prev_is_special:
					out_string += " "
				out_string += self.sp_model.decode(lowerCAmelCase_							) + token
				A_		:						Union[str, Any]	    =   True
				A_		:						str	    =   []
			else:
				current_sub_tokens.append(lowerCAmelCase_							)
				A_		:						Dict	    =   False
		out_string += self.sp_model.decode(lowerCAmelCase_							)
		return out_string.strip()





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

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

		return (out_vocab_file,)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =							{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase         =							[
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							_lowerCAmelCase         =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

