





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 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	["""image_processor""", """tokenizer"""]
    lowerCamelCase__    :		Any				=	"""BridgeTowerImageProcessor"""
    lowerCamelCase__    :		Union[str, Any]				=	("""RobertaTokenizer""", """RobertaTokenizerFast""")



    def __init__(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        super().__init__(A_   ,				A_     )



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





        '''simple docstring'''

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

        return encoding



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

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




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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







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

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



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


    '''simple docstring'''







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



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return {self.text_column: "text", self.summary_column: "summary"}
