





"""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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__					=   {
    "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
SCREAMING_SNAKE_CASE__					=   {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

SCREAMING_SNAKE_CASE__					=   "▁"

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']





      def __init__(       self      ,						lowercase      ,						lowercase="</s>"      ,						lowercase="<unk>"      ,						lowercase="<pad>"      ,						lowercase=100      ,						lowercase=None      ,						lowercase = None      ,						lowercase=True      ,						**lowercase      ,						)		->     None:
            # Add extra_ids to the special token list
            if extra_ids > 0 and additional_special_tokens is None:
                  lowerCAmelCase  						=       [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
                  lowerCAmelCase  						=       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""" )

            lowerCAmelCase  						=       legacy
            lowerCAmelCase  						=       {} 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      ,						)

            lowerCAmelCase  						=       vocab_file
            lowerCAmelCase  						=       extra_ids

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )





      @staticmethod
      def     _snake_case			(       lowercase      ,						lowercase      ,						lowercase )		->     int:
            if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                  lowerCAmelCase  						=       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     _snake_case			(       self )		->     List[Any]:
            return self.sp_model.get_piece_size() + self._extra_ids





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = False )		->     List[int]:
            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     _snake_case			(       self )		->     Dict:
            return list(
                set(filter(lambda lowercase : bool(re.search(r"""<extra_id_\d+>"""      ,						lowercase ) ) is not None      ,						self.additional_special_tokens ) ) )





      def     _snake_case			(       self )		->     str:
            return [self._convert_token_to_id(lowercase ) for token in self.get_sentinel_tokens()]





      def     _snake_case			(       self      ,						lowercase )		->     List[int]:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[int]:
            lowerCAmelCase  						=       [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     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[int]:
            lowerCAmelCase  						=       self._add_eos_if_not_present(lowercase )
            if token_ids_a is None:
                  return token_ids_a
            else:
                  lowerCAmelCase  						=       self._add_eos_if_not_present(lowercase )
                  return token_ids_a + token_ids_a





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





      def __setstate__(       self      ,						lowercase )		->     int:
            lowerCAmelCase  						=       d

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

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





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     List[str]:
            # 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:
                  lowerCAmelCase  						=       SPIECE_UNDERLINE + text.replace(lowercase      ,						""" """ )
            return super().tokenize(lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     Any:
            if not self.legacy:
                  lowerCAmelCase  						=       text.startswith(lowercase )
                  if is_first:
                        lowerCAmelCase  						=       text[1:]

            lowerCAmelCase  						=       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 ):
                  lowerCAmelCase  						=       ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
            return tokens





      def     _snake_case			(       self      ,						lowercase )		->     Any:
            if token.startswith("""<extra_id_""" ):
                  lowerCAmelCase  						=       re.match(r"""<extra_id_(\d+)>"""      ,						lowercase )
                  lowerCAmelCase  						=       int(match.group(1 ) )
                  return self.vocab_size - num - 1
            return self.sp_model.piece_to_id(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            if index < self.sp_model.get_piece_size():
                  lowerCAmelCase  						=       self.sp_model.IdToPiece(lowercase )
            else:
                  lowerCAmelCase  						=       f'<extra_id_{self.vocab_size - 1 - index}>'
            return token





      def     _snake_case			(       self      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       """"""
            lowerCAmelCase  						=       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
                        lowerCAmelCase  						=       True
                        lowerCAmelCase  						=       []
                  else:
                        current_sub_tokens.append(lowercase )
                        lowerCAmelCase  						=       False
            out_string += self.sp_model.decode(lowercase )
            return out_string.strip()





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            if not os.path.isdir(lowercase ):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                  return
            lowerCAmelCase  						=       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:
                        lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
                        fi.write(lowercase )

            return (out_vocab_file,)













"""simple docstring"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					=   {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FocalNetForImageClassification",
           "FocalNetForMaskedImageModeling",
           "FocalNetBackbone",
           "FocalNetModel",
           "FocalNetPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

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






