


"""simple docstring"""
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class        __A       (__snake_case):





							'''simple docstring'''

							__lowercase:						Union[str, Any]							 =      ['image_processor', 'tokenizer']
							__lowercase:						Union[str, Any]							 =      'BlipImageProcessor'
							__lowercase:						Dict							 =      ('BertTokenizer', 'BertTokenizerFast')



							def __init__(			self							: str						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: Dict    )   ->str:



														"""simple docstring"""



														snake_case_          =						False
														super().__init__(UpperCAmelCase_						,    UpperCAmelCase_    )
														snake_case_          =						self.image_processor



							def __call__(			self							: Dict						,    UpperCAmelCase_							: List[str] = None						,    UpperCAmelCase_							: Optional[Any] = None						,    UpperCAmelCase_							: List[Any] = True						,    UpperCAmelCase_							: Any = False						,    UpperCAmelCase_							: List[str] = None						,    UpperCAmelCase_							: Dict = None						,    UpperCAmelCase_							: List[Any] = 0						,    UpperCAmelCase_							: List[str] = None						,    UpperCAmelCase_							: Optional[Any] = None						,    UpperCAmelCase_							: List[Any] = False						,    UpperCAmelCase_							: str = False						,    UpperCAmelCase_							: List[str] = False						,    UpperCAmelCase_							: str = False						,    UpperCAmelCase_							: Union[str, Any] = False						,    UpperCAmelCase_							: int = True						,    UpperCAmelCase_							: Dict = None						,    **UpperCAmelCase_							: Optional[Any]						,    )   ->BatchEncoding:



														"""simple docstring"""



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

														# Get only text
														if images is None:
																					snake_case_          =						self.tokenizer
																					snake_case_          =						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_						,    )
																					return text_encoding

														# add pixel_values
														snake_case_          =						self.image_processor(UpperCAmelCase_						,    return_tensors=UpperCAmelCase_    )

														if text is not None:
																					snake_case_          =						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_						,    )
														else:
																					snake_case_          =						None

														if text_encoding is not None:
																					encoding_image_processor.update(UpperCAmelCase_    )

														return encoding_image_processor



							def 						lowerCAmelCase    (			self							: Tuple						,    *UpperCAmelCase_							: Tuple						,    **UpperCAmelCase_							: str    )   ->Optional[int]:



														"""simple docstring"""



														return self.tokenizer.batch_decode(*UpperCAmelCase_						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Dict						,    *UpperCAmelCase_							: Optional[int]						,    **UpperCAmelCase_							: List[Any]    )   ->Optional[int]:



														"""simple docstring"""



														return self.tokenizer.decode(*UpperCAmelCase_						,    **UpperCAmelCase_    )



							@property
							def 						lowerCAmelCase    (			self							: Tuple    )   ->int:



														"""simple docstring"""



														snake_case_          =						self.tokenizer.model_input_names
														snake_case_          =						self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







"""simple docstring"""
from collections import defaultdict
from math import gcd







def   _a			( _SCREAMING_SNAKE_CASE = 1_500_000			)      ->			int:
							snake_case_          =						defaultdict(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						2
							while 2 * euclid_m * (euclid_m + 1) <= limit:
														for euclid_n in range((euclid_m % 2) + 1			,	_SCREAMING_SNAKE_CASE			,	2			):
																					if gcd(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) > 1:
																												continue
																					snake_case_          =						2 * euclid_m * (euclid_m + euclid_n)
																					for perimeter in range(_SCREAMING_SNAKE_CASE			,	limit + 1			,	_SCREAMING_SNAKE_CASE			):
																												frequencies[perimeter] += 1
														euclid_m += 1
							return sum(1 for frequency in frequencies.values() if frequency == 1			)


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



