





"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__							:Dict	     =					"CLIPImageProcessor"
  SCREAMING_SNAKE_CASE__							:List[str]	     =					("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")

  def __init__(		self   :       Union[str, Any]					,   __a   :       Any=None					,   __a   :       Dict=None					,   **__a   :       Dict	)		->				Optional[Any]:
        _UpperCamelCase      :	Any						=       None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."					,   __a					,   )
              _UpperCamelCase      :	Dict						=       kwargs.pop("feature_extractor"	)

        _UpperCamelCase      :	int						=       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__(__a					,   __a	)

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

        if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none."	)

        if text is not None:
              _UpperCamelCase      :	Optional[int]						=       self.tokenizer(__a					,   return_tensors=__a					,   **__a	)

        if images is not None:
              _UpperCamelCase      :	str						=       self.image_processor(__a					,   return_tensors=__a					,   **__a	)

        if text is not None and images is not None:
              _UpperCamelCase      :	Tuple						=       image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a	)					,   tensor_type=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   *__a   :       Tuple					,   **__a   :       Tuple	)		->				Any:
        return self.tokenizer.batch_decode(*__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   *__a   :       List[Any]					,   **__a   :       Dict	)		->				Dict:
        return self.tokenizer.decode(*__a					,   **__a	)


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Dict:
        _UpperCamelCase      :	Tuple						=       self.tokenizer.model_input_names
        _UpperCamelCase      :	Optional[Any]						=       self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(default="summarization"		,  metadata={"include_in_asdict_even_if_is_default": True}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"text": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"summary": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:str	     =					"text"
  SCREAMING_SNAKE_CASE__							:str	     =					"summary"

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict[str, str]:
        return {self.text_column: "text", self.summary_column: "summary"}
