


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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							List[Any]					  =  "BlipImageProcessor"
							__UpperCAmelCase   :							Union[str, Any]					  =  "AutoTokenizer"

							def __init__(							self	:  int   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Optional[Any]    )     ->   Optional[Any]:
														__snake_case		:      List[Any]										=       False
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )
														__snake_case		:      int										=       self.image_processor

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  ImageInput = None   ,     lowerCamelCase	:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[bool, str, PaddingStrategy] = False   ,     lowerCamelCase	:  Union[bool, str, TruncationStrategy] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  int = 0   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[bool] = None   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     **lowerCamelCase	:  List[str]   ,     )     ->   BatchEncoding:
														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		:      str										=       self.tokenizer
																					__snake_case		:      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		:      Any										=       self.image_processor(lowerCamelCase   ,     return_tensors=lowerCamelCase    )

														if text is not None:
																					__snake_case		:      Union[str, 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   ,     )
														else:
																					__snake_case		:      str										=       None

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

														return encoding_image_processor

							def    __snake_case (							self	:  List[Any]   ,     *lowerCamelCase	:  int   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   List[str]:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Tuple   ,     *lowerCamelCase	:  Optional[Any]   ,     **lowerCamelCase	:  Any    )     ->   int:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )






							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def    __snake_case (							self	:  str    )     ->   Tuple:
														__snake_case		:      Union[str, Any]										=       self.tokenizer.model_input_names
														__snake_case		:      Union[str, Any]										=       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )





def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return x if y == 0 else greatest_common_divisor(__lowerCamelCase							,     x % y     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (x * y) // greatest_common_divisor(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase = 2_0     ):
							__snake_case		:      Optional[Any]										=       1
							for i in range(1							,     n + 1     ):
														__snake_case		:      Any										=       lcm(__lowerCamelCase							,     __lowerCamelCase     )
							return g


if __name__ == "__main__":
			print(f'''{solution() = }''')

