





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_ (			lowercase_      ):
			A__      :			Dict       	=  ['image_processor', 'tokenizer']
			A__      :			Any       	=  'BlipImageProcessor'
			A__      :			List[str]       	=  ('BertTokenizer', 'BertTokenizerFast')

			def __init__(   self   :  List[Any]     , UpperCAmelCase__   :  List[str]     , UpperCAmelCase__   :  int						):






							"""simple docstring"""
							snake_case   :					Union[str, Any]    						=				False
							super().__init__(lowerCamelCase_     , lowerCamelCase_						)
							snake_case   :					Dict    						=				self.image_processor

			def __call__(   self   :  Union[str, Any]     , 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 either images or text.'''						)

							# Get only text
							if images is None:
											snake_case   :					Optional[int]    						=				self.tokenizer
											snake_case   :					Optional[Any]    						=				self.tokenizer(
											    text=lowerCamelCase_     , add_special_tokens=lowerCamelCase_     , padding=lowerCamelCase_     , truncation=lowerCamelCase_     , max_length=lowerCamelCase_     , stride=lowerCamelCase_     , pad_to_multiple_of=lowerCamelCase_     , return_attention_mask=lowerCamelCase_     , return_overflowing_tokens=lowerCamelCase_     , return_special_tokens_mask=lowerCamelCase_     , return_offsets_mapping=lowerCamelCase_     , return_token_type_ids=lowerCamelCase_     , return_length=lowerCamelCase_     , verbose=lowerCamelCase_     , return_tensors=lowerCamelCase_     , **lowerCamelCase_     , )
											return text_encoding

							# add pixel_values
							snake_case   :					Dict    						=				self.image_processor(lowerCamelCase_     , return_tensors=lowerCamelCase_						)

							if text is not None:
											snake_case   :					List[str]    						=				self.tokenizer(
											    text=lowerCamelCase_     , add_special_tokens=lowerCamelCase_     , padding=lowerCamelCase_     , truncation=lowerCamelCase_     , max_length=lowerCamelCase_     , stride=lowerCamelCase_     , pad_to_multiple_of=lowerCamelCase_     , return_attention_mask=lowerCamelCase_     , return_overflowing_tokens=lowerCamelCase_     , return_special_tokens_mask=lowerCamelCase_     , return_offsets_mapping=lowerCamelCase_     , return_token_type_ids=lowerCamelCase_     , return_length=lowerCamelCase_     , verbose=lowerCamelCase_     , return_tensors=lowerCamelCase_     , **lowerCamelCase_     , )
							else:
											snake_case   :					Any    						=				None

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

							return encoding_image_processor

			def      lowerCAmelCase(   self   :  List[Any]     , *UpperCAmelCase__   :  Dict     , **UpperCAmelCase__   :  int						):






							"""simple docstring"""
							return self.tokenizer.batch_decode(*lowerCamelCase_     , **lowerCamelCase_						)

			def      lowerCAmelCase(   self   :  Any     , *UpperCAmelCase__   :  str     , **UpperCAmelCase__   :  List[str]						):






							"""simple docstring"""
							return self.tokenizer.decode(*lowerCamelCase_     , **lowerCamelCase_						)



			@property
			def      lowerCAmelCase(   self   :  Optional[int]						):






							"""simple docstring"""
							snake_case   :					List[str]    						=				self.tokenizer.model_input_names
							snake_case   :					Any    						=				self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







'''simple docstring'''




from math import factorial



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		if n < k or k < 0:
				raise ValueError("""Please enter positive integers for n and k where n >= k"""	)
		return factorial(lowerCamelCase_	) // (factorial(lowerCamelCase_	) * factorial(n - k	))


if __name__ == "__main__":
							print(
							    """The number of five-card hands possible from a standard""",
							    f'''fifty-two card deck is: {combinations(52, 5)}\n''',
							)

							print(
							    """If a class of 40 students must be arranged into groups of""",
							    f'''4 for group projects, there are {combinations(40, 4)} ways''',
							    """to arrange them.\n""",
							)

							print(
							    """If 10 teams are competing in a Formula One race, there""",
							    f'''are {combinations(10, 3)} ways that first, second and''',
							    """third place can be awarded.""",
							)



