





import warnings
from typing import List, Optional, Union

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


class     __magic_name__     (							snake_case_							):





     """simple docstring"""



     __UpperCamelCase    			=   ['''image_processor''', '''tokenizer''']
     __UpperCamelCase    			=   '''FlavaImageProcessor'''
     __UpperCamelCase    			=   ('''BertTokenizer''', '''BertTokenizerFast''')



     def __init__(							self				:Optional[int]	,					snake_case				:int=None	,					snake_case				:int=None	,					**snake_case				:int     ):





        '''simple docstring'''




        A_    : int								=	None
        if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."	,					snake_case	,					)
           A_    : Optional[int]								=	kwargs.pop("feature_extractor"     )

        A_    : Optional[Any]								=	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__(snake_case	,					snake_case     )
        A_    : int								=	self.image_processor



     def __call__(							self				:Optional[int]	,					snake_case				:Optional[ImageInput] = None	,					snake_case				:Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None	,					snake_case				:bool = True	,					snake_case				:Union[bool, str, PaddingStrategy] = False	,					snake_case				:Union[bool, str, TruncationStrategy] = False	,					snake_case				:Optional[int] = None	,					snake_case				:int = 0	,					snake_case				:Optional[int] = None	,					snake_case				:Optional[bool] = None	,					snake_case				:Optional[bool] = None	,					snake_case				:Optional[bool] = None	,					snake_case				:Optional[bool] = None	,					snake_case				:bool = False	,					snake_case				:bool = False	,					snake_case				:bool = False	,					snake_case				:bool = False	,					snake_case				:bool = True	,					snake_case				:Optional[Union[str, TensorType]] = None	,					**snake_case				:Tuple	,					):





        '''simple docstring'''




        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:
           A_    : List[Any]								=	self.tokenizer(
               text=snake_case	,					add_special_tokens=snake_case	,					padding=snake_case	,					truncation=snake_case	,					max_length=snake_case	,					stride=snake_case	,					pad_to_multiple_of=snake_case	,					return_token_type_ids=snake_case	,					return_attention_mask=snake_case	,					return_overflowing_tokens=snake_case	,					return_special_tokens_mask=snake_case	,					return_offsets_mapping=snake_case	,					return_length=snake_case	,					verbose=snake_case	,					return_tensors=snake_case	,					**snake_case	,					)
        if images is not None:
           A_    : Dict								=	self.image_processor(
               snake_case	,					return_image_mask=snake_case	,					return_codebook_pixels=snake_case	,					return_tensors=snake_case	,					**snake_case	,					)

        if text is not None and images is not None:
           encoding.update(snake_case     )
           return encoding
        elif text is not None:
           return encoding
        else:
           return BatchEncoding(data=dict(**snake_case     )	,					tensor_type=snake_case     )



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]	,					*snake_case				:Optional[int]	,					**snake_case				:Any     ):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:str	,					*snake_case				:Optional[Any]	,					**snake_case				:List[Any]     ):





        '''simple docstring'''




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



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.tokenizer.model_input_names
        A_    : Any								=	self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




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





     @property
     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




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

import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



