





'''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


__UpperCamelCase					    =	logging.get_logger(__name__)

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

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

__UpperCamelCase					    =	"▁"




class  _A (					__lowercase		):
		lowercase__:   Tuple				     =   VOCAB_FILES_NAMES
		lowercase__:   List[str]				     =   PRETRAINED_VOCAB_FILES_MAP
		lowercase__:   Union[str, Any]				     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase__:   Optional[int]				     =   ['''input_ids''', '''attention_mask''']

		def __init__(   self					:   List[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[int]="</s>"  ,      __magic_name__					:   Optional[int]="<unk>"  ,      __magic_name__					:   Any="<pad>"  ,      __magic_name__					:   Any=1_00  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   Optional[Dict[str, Any]] = None  ,      __magic_name__					:   List[Any]=True  ,      **__magic_name__					:   str  ,      )					->    None:


					"""simple docstring"""

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

					__snake_case					:      str							      =     legacy
					__snake_case					:      Tuple							      =     {} if sp_model_kwargs is None else sp_model_kwargs

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

					__snake_case					:      Dict							      =     vocab_file
					__snake_case					:      List[Any]							      =     extra_ids

					__snake_case					:      Any							      =     spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(__magic_name__			)

		@staticmethod
		def 	lowercase__     (   __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Dict			)					->    Dict:


					"""simple docstring"""

					if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
								__snake_case					:      Optional[Any]							      =     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."""  ,      __magic_name__  ,      )

					return max_model_length

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    List[Any]:


					"""simple docstring"""

					return self.sp_model.get_piece_size() + self._extra_ids

		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     {self.convert_ids_to_tokens(__magic_name__			): i for i in range(self.vocab_size			)}
					vocab.update(self.added_tokens_encoder			)
					return vocab

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   List[int]  ,      __magic_name__					:   Optional[List[int]] = None  ,      __magic_name__					:   bool = False			)					->    List[int]:


					"""simple docstring"""

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

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

		def 	lowercase__     (   self					:   int			)					->    Any:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Optional[Any]			)					->    List[str]:


					"""simple docstring"""

					return [self._convert_token_to_id(__magic_name__			) for token in self.get_sentinel_tokens()]

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   List[int]			)					->    List[int]:


					"""simple docstring"""

					if len(__magic_name__			) > 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 	lowercase__     (   self					:   str  ,      __magic_name__					:   List[int]  ,      __magic_name__					:   Optional[List[int]] = None			)					->    List[int]:


					"""simple docstring"""

					__snake_case					:      int							      =     [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 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   List[int]  ,      __magic_name__					:   Optional[List[int]] = None			)					->    List[int]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self._add_eos_if_not_present(__magic_name__			)
					if token_ids_a is None:
								return token_ids_a
					else:
								__snake_case					:      int							      =     self._add_eos_if_not_present(__magic_name__			)
								return token_ids_a + token_ids_a

		def __getstate__(   self					:   str			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.__dict__.copy()
					__snake_case					:      Dict							      =     None
					return state

		def __setstate__(   self					:   int  ,      __magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     d

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

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

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   "TextInput"  ,      **__magic_name__					:   Tuple			)					->    List[str]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   Union[str, Any]  ,      **__magic_name__					:   int			)					->    Dict:


					"""simple docstring"""

					if not self.legacy:
								__snake_case					:      Dict							      =     text.startswith(__magic_name__			)
								if is_first:
											__snake_case					:      Union[str, Any]							      =     text[1:]

					__snake_case					:      List[str]							      =     self.sp_model.encode(__magic_name__  ,      out_type=__magic_name__			)

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

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   Optional[Any]			)					->    Optional[int]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   List[Any]			)					->    Any:


					"""simple docstring"""

					if index < self.sp_model.get_piece_size():
								__snake_case					:      List[str]							      =     self.sp_model.IdToPiece(__magic_name__			)
					else:
								__snake_case					:      Any							      =     f'''<extra_id_{self.vocab_size - 1 - index}>'''
					return token

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     []
					__snake_case					:      List[Any]							      =     """"""
					__snake_case					:      str							      =     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(__magic_name__			) + token
											__snake_case					:      Optional[int]							      =     True
											__snake_case					:      Union[str, Any]							      =     []
								else:
											current_sub_tokens.append(__magic_name__			)
											__snake_case					:      Optional[Any]							      =     False
					out_string += self.sp_model.decode(__magic_name__			)
					return out_string.strip()

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   Optional[str] = None			)					->    Tuple[str]:


					"""simple docstring"""

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

					if os.path.abspath(self.vocab_file			) != os.path.abspath(__magic_name__			) and os.path.isfile(self.vocab_file			):
								copyfile(self.vocab_file  ,      __magic_name__			)
					elif not os.path.isfile(self.vocab_file			):
								with open(__magic_name__  ,      """wb"""			) as fi:
											__snake_case					:      Optional[int]							      =     self.sp_model.serialized_model_proto()
											fi.write(__magic_name__			)

					return (out_vocab_file,)









'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

					warnings.warn(
					    """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use OwlViTImageProcessor instead."""  ,      __magic_name__  ,      )
					super().__init__(*__magic_name__  ,      **__magic_name__			)


