



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


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {'vocab_file': 'spiece.model'}

lowercase_								=     {
    '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
lowercase_								=     {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

lowercase_								=     '▁'
class 				_snake_case			(					a__):
    UpperCamelCase__				:					Optional[int]     		=VOCAB_FILES_NAMES
    UpperCamelCase__				:					List[str]     		=PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase__				:					int     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase__				:					Optional[Any]     		=["""input_ids""", """attention_mask"""]




    def __init__(						self					:						List[str],					__lowercase					:						int,					__lowercase					:						Any="</s>",					__lowercase					:						Tuple="<unk>",					__lowercase					:						Optional[int]="<pad>",					__lowercase					:						Optional[Any]=100,					__lowercase					:						Union[str, Any]=None,					__lowercase					:						List[Any] = None,					__lowercase					:						Any=True,					**__lowercase					:						Dict,					):





       if extra_ids > 0 and additional_special_tokens is None:
          lowercase__					  =				[F'''<extra_id_{i}>''' for i in range(lowercase__						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
          # Check that we have the right number of extra_id special tokens
          lowercase__					  =				len(set(filter(lambda __lowercase						: bool("extra_id" in str(lowercase__						)						),					lowercase__						)						)						)
          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"						)

       lowercase__					  =				legacy
       lowercase__					  =				{} if sp_model_kwargs is None else sp_model_kwargs

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

       lowercase__					  =				vocab_file
       lowercase__					  =				extra_ids

       lowercase__					  =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(lowercase__						)




    @staticmethod
    def 							A__      (						__lowercase					:						int,					__lowercase					:						str,					__lowercase					:						Optional[int]						):





       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
          lowercase__					  =				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.",					lowercase__,					)

       return max_model_length




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




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




    def 							A__      (						self					:						str,					__lowercase					:						Tuple,					__lowercase					:						Optional[int] = None,					__lowercase					:						List[Any] = False						):





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

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




    def 							A__      (						self					:						Optional[Any]						):
       return list(
           set(filter(lambda __lowercase						: bool(re.search(R"<extra_id_\d+>",					lowercase__						)						) is not None,					self.additional_special_tokens						)						)						)




    def 							A__      (						self					:						List[str]						):
       return [self._convert_token_to_id(lowercase__						) for token in self.get_sentinel_tokens()]




    def 							A__      (						self					:						Dict,					__lowercase					:						str						):





       if len(lowercase__						) > 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 							A__      (						self					:						Any,					__lowercase					:						str,					__lowercase					:						Union[str, Any] = None						):
       lowercase__					  =				[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 							A__      (						self					:						Optional[Any],					__lowercase					:						List[Any],					__lowercase					:						str = None						):
       lowercase__					  =				self._add_eos_if_not_present(lowercase__						)
       if token_ids_a is None:
          return token_ids_a
       else:
          lowercase__					  =				self._add_eos_if_not_present(lowercase__						)
          return token_ids_a + token_ids_a




    def __getstate__(						self					:						str						):
       lowercase__					  =				self.__dict__.copy()
       lowercase__					  =				None
       return state




    def __setstate__(						self					:						int,					__lowercase					:						List[str]						):
       lowercase__					  =				d

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

       lowercase__					  =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)




    def 							A__      (						self					:						Tuple,					__lowercase					:						Union[str, Any],					**__lowercase					:						Optional[int]						):





       if not self.legacy:
          lowercase__					  =				SPIECE_UNDERLINE + text.replace(lowercase__,					" "						)
       return super().tokenize(lowercase__,					**lowercase__						)




    def 							A__      (						self					:						Optional[Any],					__lowercase					:						Tuple,					**__lowercase					:						Dict						):





       if not self.legacy:
          lowercase__					  =				text.startswith(lowercase__						)
          if is_first:
             lowercase__					  =				text[1:]

       lowercase__					  =				self.sp_model.encode(lowercase__,					out_type=lowercase__						)

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




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





       if token.startswith("<extra_id_"						):
          lowercase__					  =				re.match(R"<extra_id_(\d+)>",					lowercase__						)
          lowercase__					  =				int(match.group(1						)						)
          return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(lowercase__						)




    def 							A__      (						self					:						Optional[Any],					__lowercase					:						Tuple						):





       if index < self.sp_model.get_piece_size():
          lowercase__					  =				self.sp_model.IdToPiece(lowercase__						)
       else:
          lowercase__					  =				F'''<extra_id_{self.vocab_size - 1 - index}>'''
       return token




    def 							A__      (						self					:						Dict,					__lowercase					:						str						):
       lowercase__					  =				[]
       lowercase__					  =				''''''
       lowercase__					  =				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(lowercase__						) + token
             lowercase__					  =				True
             lowercase__					  =				[]
          else:
             current_sub_tokens.append(lowercase__						)
             lowercase__					  =				False
       out_string += self.sp_model.decode(lowercase__						)
       return out_string.strip()




    def 							A__      (						self					:						Optional[int],					__lowercase					:						str,					__lowercase					:						Union[str, Any] = None						):





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

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

       return (out_vocab_file,)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

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




