


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__							:						int							     =   logging.get_logger(__name__)

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

lowerCamelCase__							:						Optional[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
lowerCamelCase__							:						str							     =   {
    """t5-small""": 5_1_2,
    """t5-base""": 5_1_2,
    """t5-large""": 5_1_2,
    """t5-3b""": 5_1_2,
    """t5-11b""": 5_1_2,
}

lowerCamelCase__							:						Union[str, Any]							     =   """▁"""
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							str	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							List[str]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowercase				:							Dict	   =       ['input_ids', 'attention_mask']

     def __init__(					self:int     ,		_a:Any     ,		_a:List[str]="</s>"     ,		_a:Union[str, Any]="<unk>"     ,		_a:List[Any]="<pad>"     ,		_a:Optional[Any]=1_00     ,		_a:List[str]=None     ,		_a:Optional[Dict[str, Any]] = None     ,		_a:int=True     ,		**_a:int     ,		):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         snake_case__						= [F"""<extra_id_{i}>""" for i in range(_a   )]
       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__						= len(set(filter(lambda _a   : bool('''extra_id''' in str(_a   )   )     ,		_a   )   )   )
         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__						= legacy
       snake_case__						= {} if sp_model_kwargs is None else sp_model_kwargs

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

       snake_case__						= vocab_file
       snake_case__						= extra_ids

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_a   )

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:Optional[int]     ,		_a:Union[str, Any]     ,		_a:str   ):
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
         snake_case__						= 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.'''     ,		_a     ,		)

       return max_model_length

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return self.sp_model.get_piece_size() + self._extra_ids

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_a     ,		token_ids_a=_a     ,		already_has_special_tokens=_a   )

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       return [self._convert_token_to_id(_a   ) for token in self.get_sentinel_tokens()]

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:List[int]   ):
       if len(_a   ) > 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:Tuple     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [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:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= self._add_eos_if_not_present(_a   )
       if token_ids_a is None:
         return token_ids_a
       else:
         snake_case__						= self._add_eos_if_not_present(_a   )
         return token_ids_a + token_ids_a

     def __getstate__(					self:Dict   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       return state

     def __setstate__(					self:Tuple     ,		_a:Union[str, Any]   ):
       snake_case__						= d

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:"TextInput"     ,		**_a:Tuple   ):
       # 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:
         snake_case__						= SPIECE_UNDERLINE + text.replace(_a     ,		''' '''   )
       return super().tokenize(_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]     ,		**_a:str   ):
       if not self.legacy:
         snake_case__						= text.startswith(_a   )
         if is_first:
           snake_case__						= text[1:]

       snake_case__						= self.sp_model.encode(_a     ,		out_type=_a   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int   ):
       if token.startswith('''<extra_id_'''   ):
         snake_case__						= re.match(r'''<extra_id_(\d+)>'''     ,		_a   )
         snake_case__						= int(match.group(1   )   )
         return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[Any]   ):
       if index < self.sp_model.get_piece_size():
         snake_case__						= self.sp_model.IdToPiece(_a   )
       else:
         snake_case__						= F"""<extra_id_{self.vocab_size - 1 - index}>"""
       return token

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Dict   ):
       snake_case__						= []
       snake_case__						= ''''''
       snake_case__						= 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(_a   ) + token
           snake_case__						= True
           snake_case__						= []
         else:
           current_sub_tokens.append(_a   )
           snake_case__						= False
       out_string += self.sp_model.decode(_a   )
       return out_string.strip()







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

       return (out_vocab_file,)


'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.batch_decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




