




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			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["image_processor", "tokenizer"]
    snake_case__   = "BlipImageProcessor"
    snake_case__   = "AutoTokenizer"


    def __init__(			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			Dict:
         super().__init__(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         # add QFormer tokenizer
         lowerCAmelCase__            =					qformer_tokenizer


    def __call__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	ImageInput = None  ,      SCREAMING_SNAKE_CASE__	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[bool, str, PaddingStrategy] = False  ,      SCREAMING_SNAKE_CASE__	:	Union[bool, str, TruncationStrategy] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[int] = None  ,      SCREAMING_SNAKE_CASE__	:	int = 0  ,      SCREAMING_SNAKE_CASE__	:	Optional[int] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      **SCREAMING_SNAKE_CASE__	:	List[Any]  ,      ) ->			BatchFeature:
         if images is None and text is None:
              raise ValueError("You have to specify at least images or text."  )

         lowerCAmelCase__            =					BatchFeature()

         if text is not None:
              lowerCAmelCase__            =					self.tokenizer(
                  text=SCREAMING_SNAKE_CASE__  ,      add_special_tokens=SCREAMING_SNAKE_CASE__  ,      padding=SCREAMING_SNAKE_CASE__  ,      truncation=SCREAMING_SNAKE_CASE__  ,      max_length=SCREAMING_SNAKE_CASE__  ,      stride=SCREAMING_SNAKE_CASE__  ,      pad_to_multiple_of=SCREAMING_SNAKE_CASE__  ,      return_attention_mask=SCREAMING_SNAKE_CASE__  ,      return_overflowing_tokens=SCREAMING_SNAKE_CASE__  ,      return_special_tokens_mask=SCREAMING_SNAKE_CASE__  ,      return_offsets_mapping=SCREAMING_SNAKE_CASE__  ,      return_token_type_ids=SCREAMING_SNAKE_CASE__  ,      return_length=SCREAMING_SNAKE_CASE__  ,      verbose=SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  ,      )
              encoding.update(SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					self.qformer_tokenizer(
                  text=SCREAMING_SNAKE_CASE__  ,      add_special_tokens=SCREAMING_SNAKE_CASE__  ,      padding=SCREAMING_SNAKE_CASE__  ,      truncation=SCREAMING_SNAKE_CASE__  ,      max_length=SCREAMING_SNAKE_CASE__  ,      stride=SCREAMING_SNAKE_CASE__  ,      pad_to_multiple_of=SCREAMING_SNAKE_CASE__  ,      return_attention_mask=SCREAMING_SNAKE_CASE__  ,      return_overflowing_tokens=SCREAMING_SNAKE_CASE__  ,      return_special_tokens_mask=SCREAMING_SNAKE_CASE__  ,      return_offsets_mapping=SCREAMING_SNAKE_CASE__  ,      return_token_type_ids=SCREAMING_SNAKE_CASE__  ,      return_length=SCREAMING_SNAKE_CASE__  ,      verbose=SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  ,      )
              lowerCAmelCase__            =					qformer_text_encoding.pop("input_ids"  )
              lowerCAmelCase__            =					qformer_text_encoding.pop("attention_mask"  )

         if images is not None:
              lowerCAmelCase__            =					self.image_processor(SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  )
              encoding.update(SCREAMING_SNAKE_CASE__  )

         return encoding


    def   a (			self	:	Union[str, Any]  ,      *SCREAMING_SNAKE_CASE__	:	Tuple  ,      **SCREAMING_SNAKE_CASE__	:	Any  ) ->			Dict:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Any  ,      *SCREAMING_SNAKE_CASE__	:	int  ,      **SCREAMING_SNAKE_CASE__	:	int  ) ->			Union[str, Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def   a (			self	:	Dict  ) ->			List[Any]:
         lowerCAmelCase__            =					self.tokenizer.model_input_names
         lowerCAmelCase__            =					self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Any:
         if os.path.isfile(SCREAMING_SNAKE_CASE__  ):
              raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'  )
         os.makedirs(SCREAMING_SNAKE_CASE__  ,      exist_ok=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					os.path.join(SCREAMING_SNAKE_CASE__  ,      "qformer_tokenizer"  )
         self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__  )
         return super().save_pretrained(SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


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







__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
