
"""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 __snake_case		(				UpperCAmelCase_					):
    _a		:						List[str]=    ["""image_processor""", """tokenizer"""]
    _a		:						Any=    """BlipImageProcessor"""
    _a		:						int=    """AutoTokenizer"""
    def __init__(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(__lowercase		,__lowercase				)

           # add QFormer tokenizer
           lowercase							:					Optional[Any]	     =				qformer_tokenizer
    def __call__(  self		,snake_case = None		,snake_case = None		,snake_case = True		,snake_case = False		,snake_case = None		,snake_case = None		,snake_case = 0		,snake_case = None		,snake_case = None		,snake_case = False		,snake_case = False		,snake_case = False		,snake_case = False		,snake_case = False		,snake_case = True		,snake_case = None		,**snake_case		,):



           '''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							:					Tuple	     =				self.tokenizer(
                      text=__lowercase		,add_special_tokens=__lowercase		,padding=__lowercase		,truncation=__lowercase		,max_length=__lowercase		,stride=__lowercase		,pad_to_multiple_of=__lowercase		,return_attention_mask=__lowercase		,return_overflowing_tokens=__lowercase		,return_special_tokens_mask=__lowercase		,return_offsets_mapping=__lowercase		,return_token_type_ids=__lowercase		,return_length=__lowercase		,verbose=__lowercase		,return_tensors=__lowercase		,**__lowercase		,)
                  encoding.update(__lowercase				)
                  lowercase							:					Dict	     =				self.qformer_tokenizer(
                      text=__lowercase		,add_special_tokens=__lowercase		,padding=__lowercase		,truncation=__lowercase		,max_length=__lowercase		,stride=__lowercase		,pad_to_multiple_of=__lowercase		,return_attention_mask=__lowercase		,return_overflowing_tokens=__lowercase		,return_special_tokens_mask=__lowercase		,return_offsets_mapping=__lowercase		,return_token_type_ids=__lowercase		,return_length=__lowercase		,verbose=__lowercase		,return_tensors=__lowercase		,**__lowercase		,)
                  lowercase							:					List[str]	     =				qformer_text_encoding.pop("""input_ids"""				)
                  lowercase							:					List[Any]	     =				qformer_text_encoding.pop("""attention_mask"""				)

           if images is not None:
                  lowercase							:					List[Any]	     =				self.image_processor(__lowercase		,return_tensors=__lowercase				)
                  encoding.update(__lowercase				)

           return encoding
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*__lowercase		,**__lowercase				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*__lowercase		,**__lowercase				)
    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.tokenizer.model_input_names
           lowercase							:					Dict	     =				self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,**snake_case				):



           '''simple docstring'''







           if os.path.isfile(__lowercase				):
                  raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"				)
           os.makedirs(__lowercase		,exist_ok=__lowercase				)
           lowercase							:					int	     =				os.path.join(__lowercase		,"""qformer_tokenizer"""				)
           self.qformer_tokenizer.save_pretrained(__lowercase				)
           return super().save_pretrained(__lowercase		,**__lowercase				)
    @classmethod
    def 				_SCREAMING_SNAKE_CASE	(  cls		,snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				AutoTokenizer.from_pretrained(__lowercase		,subfolder="""qformer_tokenizer"""				)
           lowercase							:					List[Any]	     =				cls._get_arguments_from_pretrained(__lowercase		,**__lowercase				)
           args.append(__lowercase				)
           return cls(*__lowercase				)


from __future__ import annotations

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   tuple[np.ndarray, np.ndarray]:
       lowercase     ,      lowercase							:					Dict	     =				np.shape(SCREAMING_SNAKE_CASE__    )
       if rows != columns:
              lowercase							:					str	     =				(
                  """'table' has to be of square shaped array but got a """
                  f"{rows}x{columns} array:\n{table}"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Any	     =				np.zeros((rows, columns)    )
       lowercase							:					int	     =				np.zeros((rows, columns)    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Optional[int]	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     if upper[j][j] == 0:
                            raise ArithmeticError("""No LU decomposition exists"""    )
                     lowercase							:					str	     =				(table[i][j] - total) / upper[j][j]
              lowercase							:					Optional[Any]	     =				1
              for j in range(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Any	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					Tuple	     =				table[i][j] - total
       return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
