






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      (UpperCamelCase__      ):
      snake_case_	=				["""image_processor""", """tokenizer"""]
      snake_case_	=				"""BlipImageProcessor"""
      snake_case_	=				"""AutoTokenizer"""


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Any:
           super().__init__(__A       ,    __A						)

           # add QFormer tokenizer
           __lowerCAmelCase             =       qformer_tokenizer


      def __call__(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = 0       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						BatchFeature:



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

           __lowerCAmelCase             =       BatchFeature()

           if text is not None:
                __lowerCAmelCase             =       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             =       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             =       qformer_text_encoding.pop("input_ids"						)
                __lowerCAmelCase             =       qformer_text_encoding.pop("attention_mask"						)

           if images is not None:
                __lowerCAmelCase             =       self.image_processor(__A       ,    return_tensors=__A						)
                encoding.update(__A						)

           return encoding


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						List[str]:
           return self.tokenizer.batch_decode(*__A       ,    **__A						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						int:
           return self.tokenizer.decode(*__A       ,    **__A						)


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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						str:



           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             =       os.path.join(__A       ,    "qformer_tokenizer"						)
           self.qformer_tokenizer.save_pretrained(__A						)
           return super().save_pretrained(__A       ,    **__A						)


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       AutoTokenizer.from_pretrained(__A       ,    subfolder="qformer_tokenizer"						)
           __lowerCAmelCase             =       cls._get_arguments_from_pretrained(__A       ,    **__A						)
           args.append(__A						)
           return cls(*__A						)













def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int = 600_851_475_143     ):

		try:
				SCREAMING_SNAKE_CASE__								=   int(UpperCamelCase__     )
		except (TypeError, ValueError):
				raise TypeError("""Parameter n must be int or castable to int."""     )
		if n <= 0:
				raise ValueError("""Parameter n must be greater than or equal to one."""     )
		SCREAMING_SNAKE_CASE__								=   1
		SCREAMING_SNAKE_CASE__								=   2
		while i * i <= n:
				while n % i == 0:
						SCREAMING_SNAKE_CASE__								=   i
						n //= i
				i += 1
		if n > 1:
				SCREAMING_SNAKE_CASE__								=   n
		return int(UpperCamelCase__     )


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