





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       List[str] = False						)			-> Optional[Any]:



					'''simple docstring'''




					if n == 2:
										return True
					if not n % 2 or n < 2:
										return False
					if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
										return False
					if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
										raise ValueError(
										    'Warning: upper bound of deterministic test is exceeded. '
										    'Pass allow_probable=True to allow probabilistic test. '
										    'A return value of True indicates a probable prime.'						)
					# array bounds provided by analysis
					_snake_case									=				[
					    2_047,
					    1_373_653,
					    25_326_001,
					    3_215_031_751,
					    2_152_302_898_747,
					    3_474_749_660_383,
					    341_550_071_728_321,
					    1,
					    3_825_123_056_546_413_051,
					    1,
					    1,
					    318_665_857_834_031_151_167_461,
					    3_317_044_064_679_887_385_961_981,
					]

					_snake_case									=				[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
					for idx, _p in enumerate(SCREAMING_SNAKE_CASE_       ,    1						):
										if n < _p:
															# then we have our last prime to check
															_snake_case									=				primes[:idx]
															break
					_snake_case							,     _snake_case									=				n - 1, 0
					# break up n -1 into a power of 2 (s) and
					# remaining odd component
					# essentially, solve for d * 2 ** s == n - 1
					while d % 2 == 0:
										d //= 2
										s += 1
					for prime in plist:
										_snake_case									=				False
										for r in range(SCREAMING_SNAKE_CASE_						):
															_snake_case									=				pow(SCREAMING_SNAKE_CASE_       ,    d * 2**r       ,    SCREAMING_SNAKE_CASE_						)
															# see article for analysis explanation for m
															if (r == 0 and m == 1) or ((m + 1) % n == 0):
																				_snake_case									=				True
																				# this loop will not determine compositeness
																				break
										if pr:
															continue
										# if pr is False, then the above loop never evaluated to true,
										# and the n MUST be composite
										return False
					return True





def 						lowerCamelCase__     (     )			-> str:



					'''simple docstring'''




					assert not miller_rabin(561						)
					assert miller_rabin(563						)
					# 2047

					assert not miller_rabin(838_201						)
					assert miller_rabin(838_207						)
					# 1_373_653

					assert not miller_rabin(17_316_001						)
					assert miller_rabin(17_316_017						)
					# 25_326_001

					assert not miller_rabin(3_078_386_641						)
					assert miller_rabin(3_078_386_653						)
					# 3_215_031_751

					assert not miller_rabin(1_713_045_574_801						)
					assert miller_rabin(1_713_045_574_819						)
					# 2_152_302_898_747

					assert not miller_rabin(2_779_799_728_307						)
					assert miller_rabin(2_779_799_728_327						)
					# 3_474_749_660_383

					assert not miller_rabin(113_850_023_909_441						)
					assert miller_rabin(113_850_023_909_527						)
					# 341_550_071_728_321

					assert not miller_rabin(1_275_041_018_848_804_351						)
					assert miller_rabin(1_275_041_018_848_804_391						)
					# 3_825_123_056_546_413_051

					assert not miller_rabin(79_666_464_458_507_787_791_867						)
					assert miller_rabin(79_666_464_458_507_787_791_951						)
					# 318_665_857_834_031_151_167_461

					assert not miller_rabin(552_840_677_446_647_897_660_333						)
					assert miller_rabin(552_840_677_446_647_897_660_359						)
					# 3_317_044_064_679_887_385_961_981
					# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()










import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase_      							=     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_  )		->							int:
										super().__init__()
										_snake_case									=				torchvision.models.resnetaaa(pretrained=lowerCAmelCase_  )
										_snake_case									=				list(model.children()  )[:-2]
										_snake_case									=				nn.Sequential(*lowerCAmelCase_  )
										_snake_case									=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
										_snake_case									=				self.pool(self.model(lowerCAmelCase_  )  )
										_snake_case									=				torch.flatten(lowerCAmelCase_				,						start_dim=2  )
										_snake_case									=				out.transpose(1				,						2  ).contiguous()
										return out  # BxNx2048





class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				[json.loads(lowerCAmelCase_  ) for l in open(lowerCAmelCase_  )]
										_snake_case									=				os.path.dirname(lowerCAmelCase_  )
										_snake_case									=				tokenizer
										_snake_case									=				labels
										_snake_case									=				len(lowerCAmelCase_  )
										_snake_case									=				max_seq_length

										_snake_case									=				transforms




					def __len__(		self  )		->							Any:
										return len(self.data  )




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,						add_special_tokens=lowerCAmelCase_  )  )
										_snake_case							,     _snake_case							,     _snake_case									=				sentence[0], sentence[1:-1], sentence[-1]
										_snake_case									=				sentence[: self.max_seq_length]

										_snake_case									=				torch.zeros(self.n_classes  )
										_snake_case									=				1

										_snake_case									=				Image.open(os.path.join(self.data_dir				,						self.data[index]['img']  )  ).convert('RGB'  )
										_snake_case									=				self.transforms(lowerCAmelCase_  )

										return {
										    "image_start_token": start_token,
										    "image_end_token": end_token,
										    "sentence": sentence,
										    "image": image,
										    "label": label,
										}






					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				Counter()
										for row in self.data:
															label_freqs.update(row['label']  )
										return label_freqs





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Dict:



					'''simple docstring'''




					_snake_case									=				[len(row['sentence']						) for row in batch]
					_snake_case							,     _snake_case									=				len(UpperCamelCase__						), max(UpperCamelCase__						)

					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)
					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)

					for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__       ,    UpperCamelCase__						)						):
										_snake_case									=				input_row['sentence']
										_snake_case									=				1

					_snake_case									=				torch.stack([row['image'] for row in batch]						)
					_snake_case									=				torch.stack([row['label'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_start_token'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_end_token'] for row in batch]						)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 						lowerCamelCase__     (     )			-> str:



					'''simple docstring'''




					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]





def 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




					return transforms.Compose(
					    [
					        transforms.Resize(256						),
					        transforms.CenterCrop(224						),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]       ,    std=[0.1222_1994, 0.1214_5835, 0.1438_0469]       ,    ),
					    ]						)



