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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Any						=      ["""image_processor""", """tokenizer"""]
    a__	:						Optional[Any]						=      """ViltImageProcessor"""
    a__	:						Optional[int]						=      ("""BertTokenizer""", """BertTokenizerFast""")







    def __init__(	self , __lowercase=None , __lowercase=None , **__lowercase)       ->	List[str]:
        __UpperCamelCase     :Union[str, 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.''' , __lowercase , )
            __UpperCamelCase     :Tuple			  =		kwargs.pop('''feature_extractor''')

        __UpperCamelCase     :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__(__lowercase , __lowercase)
        __UpperCamelCase     :Optional[Any]			  =		self.image_processor







    def __call__(	self , __lowercase , __lowercase = None , __lowercase = True , __lowercase = False , __lowercase = None , __lowercase = None , __lowercase = 0 , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = False , __lowercase = False , __lowercase = False , __lowercase = False , __lowercase = True , __lowercase = None , **__lowercase , )       ->	BatchEncoding:
        __UpperCamelCase     :Tuple			  =		self.tokenizer(
            text=__lowercase , add_special_tokens=__lowercase , padding=__lowercase , truncation=__lowercase , max_length=__lowercase , stride=__lowercase , pad_to_multiple_of=__lowercase , return_token_type_ids=__lowercase , return_attention_mask=__lowercase , return_overflowing_tokens=__lowercase , return_special_tokens_mask=__lowercase , return_offsets_mapping=__lowercase , return_length=__lowercase , verbose=__lowercase , return_tensors=__lowercase , **__lowercase , )
        # add pixel_values + pixel_mask
        __UpperCamelCase     :str			  =		self.image_processor(__lowercase , return_tensors=__lowercase)
        encoding.update(__lowercase)

        return encoding







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	str:
        return self.tokenizer.batch_decode(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	Optional[int]:
        return self.tokenizer.decode(*__lowercase , **__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :int			  =		self.tokenizer.model_input_names
        __UpperCamelCase     :int			  =		self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







    @property
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __lowercase , )
        return self.image_processor_class







    @property
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __lowercase , )
        return self.image_processor






import argparse
import json

from tqdm import tqdm



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        '''--src_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''biencoder-nq-dev.json'''  ,			help='''Path to raw DPR training data'''  ,			)
    parser.add_argument(
        '''--evaluation_set'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''where to store parsed evaluation_set file'''  ,			)
    parser.add_argument(
        '''--gold_data_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''where to store parsed gold_data_path file'''  ,			)
    __UpperCamelCase     :str			  =		parser.parse_args()

    with open(args.src_path  ,			'''r'''	) as src_file, open(args.evaluation_set  ,			'''w'''	) as eval_file, open(
        args.gold_data_path  ,			'''w'''	) as gold_file:
        __UpperCamelCase     :List[str]			  =		json.load(SCREAMING_SNAKE_CASE	)
        for dpr_record in tqdm(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :List[str]			  =		dpr_record['''question''']
            __UpperCamelCase     :Tuple			  =		[context['''title'''] for context in dpr_record['''positive_ctxs''']]
            eval_file.write(question + '''\n'''	)
            gold_file.write('''\t'''.join(SCREAMING_SNAKE_CASE	) + '''\n'''	)


if __name__ == "__main__":
    main()





