






'''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 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					['image_processor', 'tokenizer']
		_UpperCamelCase				  =					'BlipImageProcessor'
		_UpperCamelCase				  =					'AutoTokenizer'






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								super().__init__(_lowerCAmelCase   ,_lowerCAmelCase       )

								# add QFormer tokenizer
								lowerCamelCase__         =  qformer_tokenizer






		def __call__(      self   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = True   ,_lowerCAmelCase = False   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								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=_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   ,)
														encoding.update(_lowerCAmelCase       )
														lowerCamelCase__         =  self.qformer_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   ,)
														lowerCamelCase__         =  qformer_text_encoding.pop("""input_ids"""       )
														lowerCamelCase__         =  qformer_text_encoding.pop("""attention_mask"""       )

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

								return encoding






		def 			UpperCamelCase_					(      self   ,*_lowerCAmelCase   ,**_lowerCAmelCase       ):
								return self.tokenizer.batch_decode(*_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,*_lowerCAmelCase   ,**_lowerCAmelCase       ):
								return self.tokenizer.decode(*_lowerCAmelCase   ,**_lowerCAmelCase       )






		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 			UpperCamelCase_					(      self       ):
								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   ,_lowerCAmelCase   ,**_lowerCAmelCase       ):
								if os.path.isfile(_lowerCAmelCase       ):
														raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''       )
								os.makedirs(_lowerCAmelCase   ,exist_ok=_lowerCAmelCase       )
								lowerCamelCase__         =  os.path.join(_lowerCAmelCase   ,"""qformer_tokenizer"""       )
								self.qformer_tokenizer.save_pretrained(_lowerCAmelCase       )
								return super().save_pretrained(_lowerCAmelCase   ,**_lowerCAmelCase       )






		@classmethod
		def 			UpperCamelCase_					(      cls   ,_lowerCAmelCase   ,**_lowerCAmelCase       ):
								lowerCamelCase__         =  AutoTokenizer.from_pretrained(_lowerCAmelCase   ,subfolder="""qformer_tokenizer"""       )
								lowerCamelCase__         =  cls._get_arguments_from_pretrained(_lowerCAmelCase   ,**_lowerCAmelCase       )
								args.append(_lowerCAmelCase       )
								return cls(*_lowerCAmelCase       )









'''simple docstring'''







from __future__ import annotations

import math
def 						A__			(				__lowerCAmelCase		:			int						):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,    int(math.sqrt(__lowerCAmelCase						) + 1						)   ,    6						):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


UpperCamelCase    :      str  			=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise ValueError("""n must be an integer"""						)
						if n <= 0:
												raise ValueError("""n must be >= 0"""						)

						lowerCamelCase__         =  []
						for num in range(len(__lowerCAmelCase						)						):
												lowerCamelCase__         =  0
												while 2 * i * i <= odd_composites[num]:
																		lowerCamelCase__         =  odd_composites[num] - 2 * i * i
																		if is_prime(__lowerCAmelCase						):
																								break
																		i += 1
												else:
																		list_nums.append(odd_composites[num]						)
																		if len(__lowerCAmelCase						) == n:
																								return list_nums

						return []
def 						A__			(				):
						return compute_nums(1						)[0]


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

