
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   (	UpperCamelCase_							):
			UpperCamelCase__        =       ['image_processor', 'tokenizer']
			UpperCamelCase__        =       'BlipImageProcessor'
			UpperCamelCase__        =       'AutoTokenizer'







			def __init__(							self		,  _a		,  _a		,  _a						):
				super().__init__(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE						)

				# add QFormer tokenizer
				__magic_name__						:						Any        =	qformer_tokenizer







			def __call__(							self		,  _a = None		,  _a = None		,  _a = True		,  _a = False		,  _a = None		,  _a = None		,  _a = 0		,  _a = None		,  _a = None		,  _a = False		,  _a = False		,  _a = False		,  _a = False		,  _a = False		,  _a = True		,  _a = None		,  **_a		,  ):







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

				__magic_name__						:						int        =	BatchFeature()

				if text is not None:
					__magic_name__						:						Optional[int]        =	self.tokenizer(
					    text=__SCREAMING_SNAKE_CASE		,  add_special_tokens=__SCREAMING_SNAKE_CASE		,  padding=__SCREAMING_SNAKE_CASE		,  truncation=__SCREAMING_SNAKE_CASE		,  max_length=__SCREAMING_SNAKE_CASE		,  stride=__SCREAMING_SNAKE_CASE		,  pad_to_multiple_of=__SCREAMING_SNAKE_CASE		,  return_attention_mask=__SCREAMING_SNAKE_CASE		,  return_overflowing_tokens=__SCREAMING_SNAKE_CASE		,  return_special_tokens_mask=__SCREAMING_SNAKE_CASE		,  return_offsets_mapping=__SCREAMING_SNAKE_CASE		,  return_token_type_ids=__SCREAMING_SNAKE_CASE		,  return_length=__SCREAMING_SNAKE_CASE		,  verbose=__SCREAMING_SNAKE_CASE		,  return_tensors=__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE		,  )
					encoding.update(__SCREAMING_SNAKE_CASE						)
					__magic_name__						:						Dict        =	self.qformer_tokenizer(
					    text=__SCREAMING_SNAKE_CASE		,  add_special_tokens=__SCREAMING_SNAKE_CASE		,  padding=__SCREAMING_SNAKE_CASE		,  truncation=__SCREAMING_SNAKE_CASE		,  max_length=__SCREAMING_SNAKE_CASE		,  stride=__SCREAMING_SNAKE_CASE		,  pad_to_multiple_of=__SCREAMING_SNAKE_CASE		,  return_attention_mask=__SCREAMING_SNAKE_CASE		,  return_overflowing_tokens=__SCREAMING_SNAKE_CASE		,  return_special_tokens_mask=__SCREAMING_SNAKE_CASE		,  return_offsets_mapping=__SCREAMING_SNAKE_CASE		,  return_token_type_ids=__SCREAMING_SNAKE_CASE		,  return_length=__SCREAMING_SNAKE_CASE		,  verbose=__SCREAMING_SNAKE_CASE		,  return_tensors=__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE		,  )
					__magic_name__						:						List[Any]        =	qformer_text_encoding.pop("input_ids"						)
					__magic_name__						:						int        =	qformer_text_encoding.pop("attention_mask"						)

				if images is not None:
					__magic_name__						:						Optional[Any]        =	self.image_processor(__SCREAMING_SNAKE_CASE		,  return_tensors=__SCREAMING_SNAKE_CASE						)
					encoding.update(__SCREAMING_SNAKE_CASE						)

				return encoding







			def      SCREAMING_SNAKE_CASE       (							self		,  *_a		,  **_a						):
				return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						)







			def      SCREAMING_SNAKE_CASE       (							self		,  *_a		,  **_a						):
				return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						)







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







			def      SCREAMING_SNAKE_CASE       (							self		,  _a		,  **_a						):







				if os.path.isfile(__SCREAMING_SNAKE_CASE						):
					raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''						)
				os.makedirs(__SCREAMING_SNAKE_CASE		,  exist_ok=__SCREAMING_SNAKE_CASE						)
				__magic_name__						:						Optional[Any]        =	os.path.join(__SCREAMING_SNAKE_CASE		,  "qformer_tokenizer"						)
				self.qformer_tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE						)
				return super().save_pretrained(__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						)







			@classmethod
			def      SCREAMING_SNAKE_CASE       (							cls		,  _a		,  **_a						):
				__magic_name__						:						Dict        =	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		,  subfolder="qformer_tokenizer"						)
				__magic_name__						:						Dict        =	cls._get_arguments_from_pretrained(__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						)
				args.append(__SCREAMING_SNAKE_CASE						)
				return cls(*__SCREAMING_SNAKE_CASE						)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = 2_0_0_0_0_0_0		):
      UpperCamelCase__     : Dict											=					[0 for i in range(n + 1		)]
      UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : int											=					1

      for i in range(2			,		int(n**0.5		) + 1		):
            if primality_list[i] == 0:
                  for j in range(i * i			,		n + 1			,		UpperCamelCase__		):
                        UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : Optional[Any]											=					0
      for i in range(UpperCamelCase__		):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


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


