"""simple docstring"""





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  _snake_case     (					a__		):
      snake_case__			  =			["image_processor", "tokenizer"]
      snake_case__			  =			"BlipImageProcessor"
      snake_case__			  =			"AutoTokenizer"






      def __init__(       self      :			Any							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tuple    ):
             super().__init__(UpperCAmelCase							,					UpperCAmelCase    )

             # add QFormer tokenizer
             __lowerCamelCase					:		str		   =     qformer_tokenizer






      def __call__(       self      :			Optional[int]							,					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      :			str							,					):
             if images is None and text is None:
                    raise ValueError("You have to specify at least images or text."    )

             __lowerCamelCase					:		Any		   =     BatchFeature()

             if text is not None:
                    __lowerCamelCase					:		Optional[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    )
                    __lowerCamelCase					:		List[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							,					)
                    __lowerCamelCase					:		Any		   =     qformer_text_encoding.pop("input_ids"    )
                    __lowerCamelCase					:		Dict		   =     qformer_text_encoding.pop("attention_mask"    )

             if images is not None:
                    __lowerCamelCase					:		int		   =     self.image_processor(UpperCAmelCase							,					return_tensors=UpperCAmelCase    )
                    encoding.update(UpperCAmelCase    )

             return encoding






      def       lowerCamelCase__     (       self      :			str							,					*UpperCAmelCase      :			Optional[int]							,					**UpperCAmelCase      :			str    ):
             return self.tokenizer.batch_decode(*UpperCAmelCase							,					**UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Optional[Any]							,					*UpperCAmelCase      :			List[str]							,					**UpperCAmelCase      :			Tuple    ):
             return self.tokenizer.decode(*UpperCAmelCase							,					**UpperCAmelCase    )






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






      def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			Tuple							,					**UpperCAmelCase      :			Optional[Any]    ):
             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    )
             __lowerCamelCase					:		List[str]		   =     os.path.join(UpperCAmelCase							,					"qformer_tokenizer"    )
             self.qformer_tokenizer.save_pretrained(UpperCAmelCase    )
             return super().save_pretrained(UpperCAmelCase							,					**UpperCAmelCase    )







      @classmethod
      def       lowerCamelCase__     (       cls      :			Any							,					UpperCAmelCase      :			Any							,					**UpperCAmelCase      :			int    ):
             __lowerCamelCase					:		Dict		   =     AutoTokenizer.from_pretrained(UpperCAmelCase							,					subfolder="qformer_tokenizer"    )
             __lowerCamelCase					:		Optional[Any]		   =     cls._get_arguments_from_pretrained(UpperCAmelCase							,					**UpperCAmelCase    )
             args.append(UpperCAmelCase    )
             return cls(*UpperCAmelCase    )
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							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."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


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