






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""image_processor""", """tokenizer"""]
				lowercase__       							= """BridgeTowerImageProcessor"""
				lowercase__       							= ("""RobertaTokenizer""", """RobertaTokenizerFast""")




				def __init__(  self		:					List[str],						lowerCamelCase		:					str,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								super().__init__(lowerCamelCase,						lowerCamelCase						)




				def __call__(  self		:					Dict,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,						lowerCamelCase		:					bool = True,						lowerCamelCase		:					Union[bool, str, PaddingStrategy] = False,						lowerCamelCase		:					Union[bool, str, TruncationStrategy] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					int = 0,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = True,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						**lowerCamelCase		:					str,						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer(
								    text=lowerCamelCase,						add_special_tokens=lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=lowerCamelCase,						stride=lowerCamelCase,						pad_to_multiple_of=lowerCamelCase,						return_token_type_ids=lowerCamelCase,						return_attention_mask=lowerCamelCase,						return_overflowing_tokens=lowerCamelCase,						return_special_tokens_mask=lowerCamelCase,						return_offsets_mapping=lowerCamelCase,						return_length=lowerCamelCase,						verbose=lowerCamelCase,						return_tensors=lowerCamelCase,						**lowerCamelCase,						)
								# add pixel_values + pixel_mask
								lowercase__     				=    self.image_processor(
								    lowerCamelCase,						return_tensors=lowerCamelCase,						do_normalize=lowerCamelCase,						do_center_crop=lowerCamelCase,						**lowerCamelCase						)
								encoding.update(lowerCamelCase						)

								return encoding




				def        lowercase__     (  self		:					str,						*lowerCamelCase		:					int,						**lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,						**lowerCamelCase						)




				def        lowercase__     (  self		:					List[Any],						*lowerCamelCase		:					Optional[int],						**lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,						**lowerCamelCase						)




				@property
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer.model_input_names
								lowercase__     				=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)












from collections import defaultdict
from math import gcd






def 						a    (		lowerCamelCase_ = 150_0000       ):






				'''simple docstring'''
				lowercase__     				=    defaultdict(lowerCamelCase_       )
				lowercase__     				=    2
				while 2 * euclid_m * (euclid_m + 1) <= limit:
								for euclid_n in range((euclid_m % 2) + 1	,     lowerCamelCase_	,     2       ):
												if gcd(lowerCamelCase_	,     lowerCamelCase_       ) > 1:
																continue
												lowercase__     				=    2 * euclid_m * (euclid_m + euclid_n)
												for perimeter in range(lowerCamelCase_	,     limit + 1	,     lowerCamelCase_       ):
																frequencies[perimeter] += 1
								euclid_m += 1
				return sum(1 for frequency in frequencies.values() if frequency == 1       )


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




