





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    ['image_processor', 'tokenizer']
       __snake_case 			=    'BlipImageProcessor'
       __snake_case 			=    'AutoTokenizer'




       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     List[Any]:
            super().__init__(__lowerCamelCase    ,						__lowerCamelCase							)

            # add QFormer tokenizer
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							qformer_tokenizer




       def __call__(      self    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = True    ,						__lowerCamelCase = False    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     BatchFeature:
            if images is None and text is None:
                 raise ValueError("You have to specify at least images or text."							)

            _SCREAMING_SNAKE_CASE       :	Any						    =							BatchFeature()

            if text is not None:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.tokenizer(
                     text=__lowerCamelCase    ,						add_special_tokens=__lowerCamelCase    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						stride=__lowerCamelCase    ,						pad_to_multiple_of=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_overflowing_tokens=__lowerCamelCase    ,						return_special_tokens_mask=__lowerCamelCase    ,						return_offsets_mapping=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						return_length=__lowerCamelCase    ,						verbose=__lowerCamelCase    ,						return_tensors=__lowerCamelCase    ,						**__lowerCamelCase    ,						)
                 encoding.update(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.qformer_tokenizer(
                     text=__lowerCamelCase    ,						add_special_tokens=__lowerCamelCase    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						stride=__lowerCamelCase    ,						pad_to_multiple_of=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_overflowing_tokens=__lowerCamelCase    ,						return_special_tokens_mask=__lowerCamelCase    ,						return_offsets_mapping=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						return_length=__lowerCamelCase    ,						verbose=__lowerCamelCase    ,						return_tensors=__lowerCamelCase    ,						**__lowerCamelCase    ,						)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							qformer_text_encoding.pop("input_ids"							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							qformer_text_encoding.pop("attention_mask"							)

            if images is not None:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.image_processor(__lowerCamelCase    ,						return_tensors=__lowerCamelCase							)
                 encoding.update(__lowerCamelCase							)

            return encoding




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     str:
            return self.tokenizer.batch_decode(*__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[Any]:
            return self.tokenizer.decode(*__lowerCamelCase    ,						**__lowerCamelCase							)




       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.tokenizer.model_input_names
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     Tuple:
            if os.path.isfile(__lowerCamelCase							):
                 raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""							)
            os.makedirs(__lowerCamelCase    ,						exist_ok=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							os.path.join(__lowerCamelCase    ,						"qformer_tokenizer"							)
            self.qformer_tokenizer.save_pretrained(__lowerCamelCase							)
            return super().save_pretrained(__lowerCamelCase    ,						**__lowerCamelCase							)







       @classmethod
       def     UpperCamelCase_			(      cls    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							AutoTokenizer.from_pretrained(__lowerCamelCase    ,						subfolder="qformer_tokenizer"							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							cls._get_arguments_from_pretrained(__lowerCamelCase    ,						**__lowerCamelCase							)
            args.append(__lowerCamelCase							)
            return cls(*__lowerCamelCase							)






from __future__ import annotations

import requests






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
     return requests.get(__lowerCamelCase	).json()






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							requests.get(__lowerCamelCase	).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase	) for story_id in story_ids]






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							hackernews_top_stories(__lowerCamelCase	)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase	) for story in stories	)


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())