"""simple docstring"""







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  UpperCamelCase	(    lowercase_						):
   lowercase							=						['image_processor', 'tokenizer']
   lowercase							=						'BlipImageProcessor'
   lowercase							=						'AutoTokenizer'
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        super().__init__(__UpperCamelCase  ,__UpperCamelCase    )

        # add QFormer tokenizer
        lowercase_			:       List[str]									=			qformer_tokenizer
   def __call__(    self  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = True  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = 0  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   BatchFeature:


        '''simple docstring'''


        if images is None and text is None:
             raise ValueError('You have to specify at least images or text.'    )

        lowercase_			:       Tuple									=			BatchFeature()

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

        if images is not None:
             lowercase_			:       str									=			self.image_processor(__UpperCamelCase  ,return_tensors=__UpperCamelCase    )
             encoding.update(__UpperCamelCase    )

        return encoding
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        return self.tokenizer.batch_decode(*__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        return self.tokenizer.decode(*__UpperCamelCase  ,**__UpperCamelCase    )
   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			self.tokenizer.model_input_names
        lowercase_			:       Union[str, Any]									=			self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if os.path.isfile(__UpperCamelCase    ):
             raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''    )
        os.makedirs(__UpperCamelCase  ,exist_ok=__UpperCamelCase    )
        lowercase_			:       Tuple									=			os.path.join(__UpperCamelCase  ,'qformer_tokenizer'    )
        self.qformer_tokenizer.save_pretrained(__UpperCamelCase    )
        return super().save_pretrained(__UpperCamelCase  ,**__UpperCamelCase    )







   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Dict									=			AutoTokenizer.from_pretrained(__UpperCamelCase  ,subfolder='qformer_tokenizer'    )
        lowercase_			:       Tuple									=			cls._get_arguments_from_pretrained(__UpperCamelCase  ,**__UpperCamelCase    )
        args.append(__UpperCamelCase    )
        return cls(*__UpperCamelCase    )


"""simple docstring"""







import numpy as np



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.array   ):
     return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
       import doctest

       doctest.testmod()

