

'''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:
      __lowerCamelCase										=			None


__lowerCamelCase										=			logging.get_logger(__name__)

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

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


class     A__					(					SCREAMING_SNAKE_CASE__	):
    lowercase		    =						VOCAB_FILES_NAMES
    lowercase		    =						PRETRAINED_VOCAB_FILES_MAP
    lowercase		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase		    =						["input_ids", "attention_mask"]
    lowercase		    =						TaTokenizer

    lowercase		    =						[]



    def __init__(    self						, UpperCamelCase__=None						, UpperCamelCase__=None						, UpperCamelCase__="</s>"						, UpperCamelCase__="<unk>"						, UpperCamelCase__="<pad>"						, UpperCamelCase__=100						, UpperCamelCase__=None						, **UpperCamelCase__						, )  ->					List[str]:





           '''simple docstring'''
           # Add extra_ids to the special token list
           if extra_ids > 0 and additional_special_tokens is None:
                  A_		    =					[f'''<extra_id_{i}>''' for i in range(UpperCamelCase__				)]
           elif extra_ids > 0 and additional_special_tokens is not None:
                  # Check that we have the right number of extra special tokens
                  A_		    =					len(set(filter(lambda UpperCamelCase__				: bool("""extra_id_""" in str(UpperCamelCase__				)				)						, UpperCamelCase__				)				)				)
                  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__(
               UpperCamelCase__						, tokenizer_file=UpperCamelCase__						, eos_token=UpperCamelCase__						, unk_token=UpperCamelCase__						, pad_token=UpperCamelCase__						, extra_ids=UpperCamelCase__						, additional_special_tokens=UpperCamelCase__						, **UpperCamelCase__						, )

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



    @staticmethod
    def     snake_case_	(    UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__				)  ->					List[str]:





           '''simple docstring'''
           if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                  A_		    =					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."""						, UpperCamelCase__						, )

           return max_model_length



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__ = None				)  ->					Tuple[str]:





           '''simple docstring'''
           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(UpperCamelCase__				):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
                  return
           A_		    =					os.path.join(
               UpperCamelCase__						, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

           return (out_vocab_file,)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__ = None				)  ->					List[int]:





           '''simple docstring'''
           A_		    =					token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                  return self.prefix_tokens + token_ids_a
           else:
                  A_		    =					token_ids_a + [self.eos_token_id]
                  return self.prefix_tokens + token_ids_a + token_ids_a



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__ = None				)  ->					List[int]:





           '''simple docstring'''
           A_		    =					[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				)  ->					List[str]:





           '''simple docstring'''
           return list(
               set(filter(lambda UpperCamelCase__				: bool(re.search(R"""<extra_id_\d+>"""						, UpperCamelCase__				)				) is not None						, self.additional_special_tokens				)				)				)







    def     snake_case_	(    self				)  ->					Any:





           '''simple docstring'''
           return [self.convert_tokens_to_ids(UpperCamelCase__				) for token in self.get_sentinel_tokens()]

from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







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

        if images is None and audio is None:
              raise ValueError("You need to specify either an `images` or `audio` input to process."      )

        SCREAMING_SNAKE_CASE_      =     None
        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   mask_pixel=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if images_mixed is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   is_mixed=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if audio is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor(
                  __magic_name__     ,   *__magic_name__     ,   sampling_rate=__magic_name__     ,   mask_audio=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.image_processor.model_input_names
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )


