


"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
				from ...processing_utils import ProcessorMixin
				from ...utils import TensorType


__SCREAMING_SNAKE_CASE							:      List[str]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Dict						=      {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Dict							 =      """layoutlmv3"""



							def __init__(			self							: Any						,    UpperCAmelCase_							: Union[str, Any]=50_265						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: str=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: Optional[Any]="gelu"						,    UpperCAmelCase_							: Union[str, Any]=0.1						,    UpperCAmelCase_							: Union[str, Any]=0.1						,    UpperCAmelCase_							: Union[str, Any]=512						,    UpperCAmelCase_							: int=2						,    UpperCAmelCase_							: Optional[int]=0.02						,    UpperCAmelCase_							: Tuple=1E-5						,    UpperCAmelCase_							: List[str]=1						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: Optional[int]=2						,    UpperCAmelCase_							: int=1_024						,    UpperCAmelCase_							: Optional[Any]=128						,    UpperCAmelCase_							: Tuple=128						,    UpperCAmelCase_							: List[Any]=True						,    UpperCAmelCase_							: str=32						,    UpperCAmelCase_							: Dict=128						,    UpperCAmelCase_							: Tuple=64						,    UpperCAmelCase_							: Any=256						,    UpperCAmelCase_							: int=True						,    UpperCAmelCase_							: Optional[int]=True						,    UpperCAmelCase_							: Union[str, Any]=True						,    UpperCAmelCase_							: Tuple=224						,    UpperCAmelCase_							: str=3						,    UpperCAmelCase_							: Optional[Any]=16						,    UpperCAmelCase_							: Union[str, Any]=None						,    **UpperCAmelCase_							: Optional[int]						,    )   ->Tuple:



														"""simple docstring"""



														super().__init__(
														    vocab_size=UpperCAmelCase_						,    hidden_size=UpperCAmelCase_						,    num_hidden_layers=UpperCAmelCase_						,    num_attention_heads=UpperCAmelCase_						,    intermediate_size=UpperCAmelCase_						,    hidden_act=UpperCAmelCase_						,    hidden_dropout_prob=UpperCAmelCase_						,    attention_probs_dropout_prob=UpperCAmelCase_						,    max_position_embeddings=UpperCAmelCase_						,    type_vocab_size=UpperCAmelCase_						,    initializer_range=UpperCAmelCase_						,    layer_norm_eps=UpperCAmelCase_						,    pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_						,    **UpperCAmelCase_						,    )
														snake_case_          =						max_ad_position_embeddings
														snake_case_          =						coordinate_size
														snake_case_          =						shape_size
														snake_case_          =						has_relative_attention_bias
														snake_case_          =						rel_pos_bins
														snake_case_          =						max_rel_pos
														snake_case_          =						has_spatial_attention_bias
														snake_case_          =						rel_ad_pos_bins
														snake_case_          =						max_rel_ad_pos
														snake_case_          =						text_embed
														snake_case_          =						visual_embed
														snake_case_          =						input_size
														snake_case_          =						num_channels
														snake_case_          =						patch_size
														snake_case_          =						classifier_dropout





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						str							 =      version.parse("""1.12""")



							@property
							def 						lowerCAmelCase    (			self							: Any    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														if self.task in ["question-answering", "sequence-classification"]:
																					return OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																					        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																					        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
																					    ]    )
														else:
																					return OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																					        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																					        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
																					    ]    )



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->float:



														"""simple docstring"""



														return 1E-5



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



														"""simple docstring"""



														return 12



							def 						lowerCAmelCase    (			self							: Dict						,    UpperCAmelCase_							: "ProcessorMixin"						,    UpperCAmelCase_							: int = -1						,    UpperCAmelCase_							: int = -1						,    UpperCAmelCase_							: bool = False						,    UpperCAmelCase_							: Optional["TensorType"] = None						,    UpperCAmelCase_							: int = 3						,    UpperCAmelCase_							: int = 40						,    UpperCAmelCase_							: int = 40						,    )   ->Mapping[str, Any]:



														"""simple docstring"""



														setattr(processor.image_processor						,    """apply_ocr"""						,    UpperCAmelCase_    )

														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														snake_case_          =						compute_effective_axis_dimension(
														    UpperCAmelCase_						,    fixed_dimension=OnnxConfig.default_fixed_batch						,    num_token_to_add=0    )
														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														snake_case_          =						processor.tokenizer.num_special_tokens_to_add(UpperCAmelCase_    )
														snake_case_          =						compute_effective_axis_dimension(
														    UpperCAmelCase_						,    fixed_dimension=OnnxConfig.default_fixed_sequence						,    num_token_to_add=UpperCAmelCase_    )
														# Generate dummy inputs according to compute batch and sequence
														snake_case_          =						[[""" """.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

														# Generate dummy bounding boxes
														snake_case_          =						[[[48, 84, 73, 128]]] * batch_size

														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
														snake_case_          =						self._generate_dummy_images(UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )

														snake_case_          =						dict(
														    processor(
														        UpperCAmelCase_						,    text=UpperCAmelCase_						,    boxes=UpperCAmelCase_						,    return_tensors=UpperCAmelCase_						,    )    )

														return inputs







"""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() = }""")



