from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class  A_			(							__snake_case			):
  _SCREAMING_SNAKE_CASE     					=      ['image_processor', 'tokenizer']
  _SCREAMING_SNAKE_CASE     					=      'BlipImageProcessor'
  _SCREAMING_SNAKE_CASE     					=      'AutoTokenizer'

  def __init__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	False
   super().__init__(A_  ,				A_							)
   __a							=	self.image_processor

  def __call__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int = None  ,				__SCREAMING_SNAKE_CASE			:   Tuple = None  ,				__SCREAMING_SNAKE_CASE			:   List[str] = True  ,				__SCREAMING_SNAKE_CASE			:   Dict = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[int] = None  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any] = None  ,				__SCREAMING_SNAKE_CASE			:   int = 0  ,				__SCREAMING_SNAKE_CASE			:   Tuple = None  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any] = None  ,				__SCREAMING_SNAKE_CASE			:   List[Any] = False  ,				__SCREAMING_SNAKE_CASE			:   Tuple = False  ,				__SCREAMING_SNAKE_CASE			:   List[str] = False  ,				__SCREAMING_SNAKE_CASE			:   List[Any] = False  ,				__SCREAMING_SNAKE_CASE			:   int = False  ,				__SCREAMING_SNAKE_CASE			:   List[Any] = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int] = None  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):




   if images is None and text is None:
    raise ValueError("You have to specify either images or text."							)

   # Get only text
   if images is None:
    __a							=	self.tokenizer
    __a							=	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_  ,				)
    return text_encoding

   # add pixel_values
   __a							=	self.image_processor(A_  ,				return_tensors=A_							)

   if text is not None:
    __a							=	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_  ,				)
   else:
    __a							=	None

   if text_encoding is not None:
    encoding_image_processor.update(A_							)

   return encoding_image_processor

  def 		_UpperCAmelCase (		self			:   Any  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   return self.tokenizer.batch_decode(*A_  ,				**A_							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   str  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
   return self.tokenizer.decode(*A_  ,				**A_							)

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









def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  [1]
 for i in range(2				,  _lowerCAmelCase	):
  factorials.append(factorials[-1] * i	)
 assert 0 <= k < factorials[-1] * n, "k out of bounds"

 UpperCamelCase      :   Tuple			      =  []
 UpperCamelCase      :   str			      =  list(range(_lowerCAmelCase	)	)

 # Find permutation
 while factorials:
  UpperCamelCase      :   int			      =  factorials.pop()
  UpperCamelCase      ,       UpperCamelCase      :   int			      =  divmod(_lowerCAmelCase				,  _lowerCAmelCase	)
  permutation.append(elements[number]	)
  elements.remove(elements[number]	)
 permutation.append(elements[0]	)

 return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()
