





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'''
  a_							:       str      					=["""image_processor""", """tokenizer"""]
  a_							:       Union[str, Any]      					="""BlipImageProcessor"""
  a_							:       Dict      					="""AutoTokenizer"""







  def __init__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    super().__init__(UpperCamelCase      ,				UpperCamelCase							)

    # add QFormer tokenizer
    _snake_case   :				Optional[int]														=	qformer_tokenizer







  def __call__(      self      :					Dict      ,				UpperCamelCase      :					ImageInput = None      ,				UpperCamelCase      :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Union[bool, str, PaddingStrategy] = False      ,				UpperCamelCase      :					Union[bool, str, TruncationStrategy] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					int = 0      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[bool] = None      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Optional[Union[str, TensorType]] = None      ,				**UpperCamelCase      :					Optional[Any]      ,				):


    '''simple docstring'''





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

    _snake_case   :				List[Any]														=	BatchFeature()

    if text is not None:
      _snake_case   :				List[Any]														=	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							)
      _snake_case   :				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      ,				)
      _snake_case   :				List[str]														=	qformer_text_encoding.pop('input_ids'							)
      _snake_case   :				Optional[Any]														=	qformer_text_encoding.pop('attention_mask'							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase							)
      encoding.update(UpperCamelCase							)

    return encoding







  def UpperCamelCase_ (      self      :					Dict      ,				*UpperCamelCase      :					List[Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					List[Any]      ,				*UpperCamelCase      :					Optional[Any]      ,				**UpperCamelCase      :					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'''





    _snake_case   :				Dict														=	self.tokenizer.model_input_names
    _snake_case   :				Optional[int]														=	self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					str							):


    '''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							)
    _snake_case   :				Any														=	os.path.join(UpperCamelCase      ,				'qformer_tokenizer'							)
    self.qformer_tokenizer.save_pretrained(UpperCamelCase							)
    return super().save_pretrained(UpperCamelCase      ,				**UpperCamelCase							)







  @classmethod
  def UpperCamelCase_ (      cls      :					Tuple      ,				UpperCamelCase      :					List[str]      ,				**UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				int														=	AutoTokenizer.from_pretrained(UpperCamelCase      ,				subfolder='qformer_tokenizer'							)
    _snake_case   :				str														=	cls._get_arguments_from_pretrained(UpperCamelCase      ,				**UpperCamelCase							)
    args.append(UpperCamelCase							)
    return cls(*UpperCamelCase							)











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		int:

  if not isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Union[str, Any]														=	F"""Input value of [number={number}] must be an integer"""
    raise TypeError(lowerCAmelCase				)

  if number < 1:
    _snake_case   :				int														=	F"""Input value of [number={number}] must be > 0"""
    raise ValueError(lowerCAmelCase				)

  _snake_case   :				int														=	1

  for i in range(1    ,							lowerCAmelCase				):
    current_number *= 4 * i - 2
    current_number //= i + 1

  return current_number


if __name__ == "__main__":
      import doctest

      doctest.testmod()




