


'''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


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'layoutlmv3'




		def __init__(		self    ,   UpperCamelCase_=50265    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-5    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_=1024    ,   UpperCamelCase_=128    ,   UpperCamelCase_=128    ,   UpperCamelCase_=True    ,   UpperCamelCase_=32    ,   UpperCamelCase_=128    ,   UpperCamelCase_=64    ,   UpperCamelCase_=256    ,   UpperCamelCase_=True    ,   UpperCamelCase_=True    ,   UpperCamelCase_=True    ,   UpperCamelCase_=224    ,   UpperCamelCase_=3    ,   UpperCamelCase_=16    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''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_    ,   )
							UpperCamelCase__   :Tuple												= max_ad_position_embeddings
							UpperCamelCase__   :List[Any]												= coordinate_size
							UpperCamelCase__   :Union[str, Any]												= shape_size
							UpperCamelCase__   :int												= has_relative_attention_bias
							UpperCamelCase__   :Any												= rel_pos_bins
							UpperCamelCase__   :Tuple												= max_rel_pos
							UpperCamelCase__   :Optional[Any]												= has_spatial_attention_bias
							UpperCamelCase__   :Union[str, Any]												= rel_ad_pos_bins
							UpperCamelCase__   :Union[str, Any]												= max_rel_ad_pos
							UpperCamelCase__   :Dict												= text_embed
							UpperCamelCase__   :Optional[Any]												= visual_embed
							UpperCamelCase__   :Dict												= input_size
							UpperCamelCase__   :List[Any]												= num_channels
							UpperCamelCase__   :Optional[int]												= patch_size
							UpperCamelCase__   :str												= classifier_dropout



class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				version.parse('1.12'					)




		@property
		def     lowerCAmelCase__				(		self							):

							'''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							):

							'''simple docstring'''


							return 1e-5




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return 12




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 3    ,   UpperCamelCase_ = 40    ,   UpperCamelCase_ = 40    ,   ):

							'''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
							UpperCamelCase__   :Optional[int]												= 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
							UpperCamelCase__   :Union[str, Any]												= processor.tokenizer.num_special_tokens_to_add(UpperCamelCase_							)
							UpperCamelCase__   :Union[str, Any]												= 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
							UpperCamelCase__   :Union[str, Any]												= [[''' '''.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							UpperCamelCase__   :str												= [[[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)
							UpperCamelCase__   :Optional[Any]												= self._generate_dummy_images(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							UpperCamelCase__   :Any												= dict(
							    processor(
							        UpperCamelCase_    ,   text=UpperCamelCase_    ,   boxes=UpperCamelCase_    ,   return_tensors=UpperCamelCase_    ,   )							)

							return inputs



'''simple docstring'''
from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt


def 			a      (			__a   )  ->	bool:



					'''simple docstring'''






					UpperCamelCase__   :int												= int(number**0.5   )
					return number == sq * sq


def 			a      (			__a ,					__a ,					__a ,					__a ,					__a ,					__a   )  ->	tuple[int, int]:



					'''simple docstring'''






					UpperCamelCase__   :int												= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
					UpperCamelCase__   :int												= x_den * y_den * z_den
					UpperCamelCase__   :int												= gcd(__a ,					__a   )
					top //= hcf
					bottom //= hcf
					return top, bottom


def 			a      (			__a = 35   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :set												= set()
					UpperCamelCase__   :int
					UpperCamelCase__   :Fraction												= Fraction(0   )
					UpperCamelCase__   :tuple[int, int]

					for x_num in range(1 ,					order + 1   ):
										for x_den in range(x_num + 1 ,					order + 1   ):
															for y_num in range(1 ,					order + 1   ):
																				for y_den in range(y_num + 1 ,					order + 1   ):
																									# n=1
																									UpperCamelCase__   :int												= x_num * y_den + x_den * y_num
																									UpperCamelCase__   :Any												= x_den * y_den
																									UpperCamelCase__   :Tuple												= gcd(__a ,					__a   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														UpperCamelCase__   :Tuple												= add_three(
																														    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																														unique_s.add(__a   )

																									# n=2
																									UpperCamelCase__   :List[str]												= (
																									    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																									)
																									UpperCamelCase__   :Dict												= x_den * x_den * y_den * y_den
																									if is_sq(__a   ) and is_sq(__a   ):
																														UpperCamelCase__   :Any												= int(sqrt(__a   )   )
																														UpperCamelCase__   :Optional[int]												= int(sqrt(__a   )   )
																														UpperCamelCase__   :int												= gcd(__a ,					__a   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			UpperCamelCase__   :Tuple												= add_three(
																																			    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																																			unique_s.add(__a   )

                    # n=-1
																									UpperCamelCase__   :Tuple												= x_num * y_num
																									UpperCamelCase__   :Union[str, Any]												= x_den * y_num + x_num * y_den
																									UpperCamelCase__   :List[str]												= gcd(__a ,					__a   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														UpperCamelCase__   :Union[str, Any]												= add_three(
																														    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																														unique_s.add(__a   )

																									# n=2
																									UpperCamelCase__   :Optional[Any]												= x_num * x_num * y_num * y_num
																									UpperCamelCase__   :Tuple												= (
																									    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																									)
																									if is_sq(__a   ) and is_sq(__a   ):
																														UpperCamelCase__   :str												= int(sqrt(__a   )   )
																														UpperCamelCase__   :Any												= int(sqrt(__a   )   )
																														UpperCamelCase__   :Dict												= gcd(__a ,					__a   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			UpperCamelCase__   :int												= add_three(
																																			    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																																			unique_s.add(__a   )

					for num, den in unique_s:
										total += Fraction(__a ,					__a   )

					return total.denominator + total.numerator


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