






"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__					: Union[str, Any]=	{"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: Union[str, Any]=	["""TimmBackbone"""]


if TYPE_CHECKING:
  from .configuration_timm_backbone import TimmBackboneConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timm_backbone import TimmBackbone

else:
  import sys

  A__					: Optional[Any]=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












"""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


A__					: Dict=	logging.get_logger(__name__)

A__					: str=	{"""vocab_file""": """spiece.model"""}

A__					: Union[str, Any]=	{
    """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
A__					: Union[str, Any]=	{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

A__					: Optional[Any]=	"""▁"""
class    __lowerCamelCase    (  _a  ):
   a      :				Dict 						=VOCAB_FILES_NAMES
   a      :				str 						=PRETRAINED_VOCAB_FILES_MAP
   a      :				Union[str, Any] 						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a      :				List[str] 						=["""input_ids""", """attention_mask"""]

   def __init__(					self ,				snake_case_ ,				snake_case_="</s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_=100 ,				snake_case_=None ,				snake_case_ = None ,				snake_case_=True ,				**snake_case_ ,				)		->       None:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__         =    [F'<extra_id_{i}>' for i in range(snake_case_      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase__         =    len(set(filter(lambda snake_case_      : bool('extra_id' in str(snake_case_      )      ) ,				snake_case_      )      )      )
           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'      )

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

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

       UpperCamelCase__         =    vocab_file
       UpperCamelCase__         =    extra_ids

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__         =    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.' ,				snake_case_ ,				)

       return max_model_length

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return self.sp_model.get_piece_size() + self._extra_ids

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = False      )		->       List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_ ,				token_ids_a=snake_case_ ,				already_has_special_tokens=snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return list(
           set(filter(lambda snake_case_      : bool(re.search(r'<extra_id_\d+>' ,				snake_case_      )      ) is not None ,				self.additional_special_tokens      )      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[int]:
       if len(snake_case_      ) > 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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    [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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
       if token_ids_a is None:
           return token_ids_a
       else:
           UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
           return token_ids_a + token_ids_a

   def __getstate__(					self      )		->       str:
       UpperCamelCase__         =    self.__dict__.copy()
       UpperCamelCase__         =    None
       return state

   def __setstate__(					self ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    d

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

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       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:
           UpperCamelCase__         =    SPIECE_UNDERLINE + text.replace(snake_case_ ,				' '      )
       return super().tokenize(snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       if not self.legacy:
           UpperCamelCase__         =    text.startswith(snake_case_      )
           if is_first:
               UpperCamelCase__         =    text[1:]

       UpperCamelCase__         =    self.sp_model.encode(snake_case_ ,				out_type=snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       if token.startswith('<extra_id_'      ):
           UpperCamelCase__         =    re.match(r'<extra_id_(\d+)>' ,				snake_case_      )
           UpperCamelCase__         =    int(match.group(1      )      )
           return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       if index < self.sp_model.get_piece_size():
           UpperCamelCase__         =    self.sp_model.IdToPiece(snake_case_      )
       else:
           UpperCamelCase__         =    F'<extra_id_{self.vocab_size - 1 - index}>'
       return token

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    ''
       UpperCamelCase__         =    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(snake_case_      ) + token
               UpperCamelCase__         =    True
               UpperCamelCase__         =    []
           else:
               current_sub_tokens.append(snake_case_      )
               UpperCamelCase__         =    False
       out_string += self.sp_model.decode(snake_case_      )
       return out_string.strip()







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Tuple[str]:
       if not os.path.isdir(snake_case_      ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
           return
       UpperCamelCase__         =    os.path.join(
           snake_case_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

       return (out_vocab_file,)




