

"""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    (    a__						):
  _UpperCAmelCase          =		["image_processor", "tokenizer"]
  _UpperCAmelCase          =		"BlipImageProcessor"
  _UpperCAmelCase          =		"AutoTokenizer"

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__(_A    ,_A      )

      # add QFormer tokenizer
      _lowerCAmelCase       :   str  =    qformer_tokenizer

  def __call__(		self    ,_A = None    ,_A = None    ,_A = True    ,_A = False    ,_A = None    ,_A = None    ,_A = 0    ,_A = None    ,_A = None    ,_A = False    ,_A = False    ,_A = False    ,_A = False    ,_A = False    ,_A = True    ,_A = None    ,**_A    ,):
      '''simple docstring'''

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

      _lowerCAmelCase       :   List[str]  =    BatchFeature()

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

      if images is not None:
          _lowerCAmelCase       :   Optional[Any]  =    self.image_processor(_A    ,return_tensors=_A      )
          encoding.update(_A      )

      return encoding

  def __lowerCamelCase		(		self    ,*_A    ,**_A      ):
      '''simple docstring'''

      return self.tokenizer.batch_decode(*_A    ,**_A      )

  def __lowerCamelCase		(		self    ,*_A    ,**_A      ):
      '''simple docstring'''

      return self.tokenizer.decode(*_A    ,**_A      )

  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.tokenizer.model_input_names
      _lowerCAmelCase       :   str  =    self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

  def __lowerCamelCase		(		self    ,_A    ,**_A      ):
      '''simple docstring'''

      if os.path.isfile(_A      ):
          raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
      os.makedirs(_A    ,exist_ok=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    os.path.join(_A    ,'qformer_tokenizer'      )
      self.qformer_tokenizer.save_pretrained(_A      )
      return super().save_pretrained(_A    ,**_A      )







  @classmethod
  def __lowerCamelCase		(		cls    ,_A    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    AutoTokenizer.from_pretrained(_A    ,subfolder='qformer_tokenizer'      )
      _lowerCAmelCase       :   Dict  =    cls._get_arguments_from_pretrained(_A    ,**_A      )
      args.append(_A      )
      return cls(*_A      )








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





