






"""simple docstring"""







import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  _UpperCamelCase	     =    None


_UpperCamelCase	     =    logging.get_logger(__name__)

_UpperCamelCase	     =    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_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""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# 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,
}







class    lowerCamelCase__   (			lowerCAmelCase__					):
    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''']
    SCREAMING_SNAKE_CASE						=     TaTokenizer

    SCREAMING_SNAKE_CASE						=     []






    def __init__(					self      ,A=None      ,A=None      ,A="</s>"      ,A="<unk>"      ,A="<pad>"      ,A=100      ,A=None      ,**A      ,):


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

          super().__init__(
              __lowerCAmelCase      ,tokenizer_file=__lowerCAmelCase      ,eos_token=__lowerCAmelCase      ,unk_token=__lowerCAmelCase      ,pad_token=__lowerCAmelCase      ,extra_ids=__lowerCAmelCase      ,additional_special_tokens=__lowerCAmelCase      ,**__lowerCAmelCase      ,)

          UpperCAmelCase							     =			vocab_file
          UpperCAmelCase							     =			False if not self.vocab_file else True
          UpperCAmelCase							     =			extra_ids






    @staticmethod
    def 							_UpperCamelCase				(					A      ,A      ,A   ):


          if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                UpperCAmelCase							     =			TaTokenizerFast.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."""      ,__lowerCAmelCase      ,)

          return max_model_length






    def 							_UpperCamelCase				(					self      ,A      ,A = None   ):


          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer."""   )

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

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowerCAmelCase   ):
                copyfile(self.vocab_file      ,__lowerCAmelCase   )
                logger.info(F'''Copy vocab file to {out_vocab_file}'''   )

          return (out_vocab_file,)






    def 							_UpperCamelCase				(					self      ,A      ,A = None   ):
          UpperCAmelCase							     =			token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                return self.prefix_tokens + token_ids_a
          else:
                UpperCAmelCase							     =			token_ids_a + [self.eos_token_id]
                return self.prefix_tokens + token_ids_a + token_ids_a






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






    def 							_UpperCamelCase				(					self   ):
          return [self.convert_tokens_to_ids(__lowerCAmelCase   ) for token in self.get_sentinel_tokens()]




'''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




