


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							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Optional[int]					      =				{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Union[str, Any]   				=      '''layoutlmv3'''
					def __init__(				self       :str      ,__snake_case       :str=5_02_65      ,__snake_case       :Any=7_68      ,__snake_case       :Union[str, Any]=12      ,__snake_case       :str=12      ,__snake_case       :Dict=30_72      ,__snake_case       :Tuple="gelu"      ,__snake_case       :str=0.1      ,__snake_case       :Optional[int]=0.1      ,__snake_case       :List[Any]=5_12      ,__snake_case       :List[str]=2      ,__snake_case       :List[Any]=0.02      ,__snake_case       :Optional[int]=1E-5      ,__snake_case       :List[str]=1      ,__snake_case       :Tuple=0      ,__snake_case       :Optional[int]=2      ,__snake_case       :str=10_24      ,__snake_case       :List[Any]=1_28      ,__snake_case       :Optional[Any]=1_28      ,__snake_case       :Any=True      ,__snake_case       :Optional[Any]=32      ,__snake_case       :Optional[int]=1_28      ,__snake_case       :Any=64      ,__snake_case       :int=2_56      ,__snake_case       :str=True      ,__snake_case       :Union[str, Any]=True      ,__snake_case       :Tuple=True      ,__snake_case       :Optional[int]=2_24      ,__snake_case       :int=3      ,__snake_case       :str=16      ,__snake_case       :List[Any]=None      ,**__snake_case       :int      ,) ->   Any:
												super().__init__(
												    vocab_size=__snake_case      ,hidden_size=__snake_case      ,num_hidden_layers=__snake_case      ,num_attention_heads=__snake_case      ,intermediate_size=__snake_case      ,hidden_act=__snake_case      ,hidden_dropout_prob=__snake_case      ,attention_probs_dropout_prob=__snake_case      ,max_position_embeddings=__snake_case      ,type_vocab_size=__snake_case      ,initializer_range=__snake_case      ,layer_norm_eps=__snake_case      ,pad_token_id=__snake_case      ,bos_token_id=__snake_case      ,eos_token_id=__snake_case      ,**__snake_case      ,)
												a__					=					max_ad_position_embeddings
												a__					=					coordinate_size
												a__					=					shape_size
												a__					=					has_relative_attention_bias
												a__					=					rel_pos_bins
												a__					=					max_rel_pos
												a__					=					has_spatial_attention_bias
												a__					=					rel_ad_pos_bins
												a__					=					max_rel_ad_pos
												a__					=					text_embed
												a__					=					visual_embed
												a__					=					input_size
												a__					=					num_channels
												a__					=					patch_size
												a__					=					classifier_dropout







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      version.parse('''1.12'''				)
					@property
					def 			lowerCamelCase__(				self       :int				) ->   Mapping[str, Mapping[int, str]]:
												# The order of inputs is different for question answering and sequence classification
												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       :Optional[Any]				) ->   float:
												return 1E-5
					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   int:
												return 12



					def 			lowerCamelCase__(				self       :Dict      ,__snake_case       :"ProcessorMixin"      ,__snake_case       :int = -1      ,__snake_case       :int = -1      ,__snake_case       :bool = False      ,__snake_case       :Optional["TensorType"] = None      ,__snake_case       :int = 3      ,__snake_case       :int = 40      ,__snake_case       :int = 40      ,) ->   Mapping[str, Any]:
												setattr(processor.image_processor      ,'apply_ocr'      ,__snake_case				)

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

												# Generate dummy bounding boxes
												a__					=					[[[48, 84, 73, 1_28]]] * 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)
												a__					=					self._generate_dummy_images(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

												a__					=					dict(
												    processor(
												        __snake_case      ,text=__snake_case      ,boxes=__snake_case      ,return_tensors=__snake_case      ,)				)

												return inputs








from collections import defaultdict
from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0 ,   __lowerCAmelCase							:    int = 1_0       ):
							a__					=					defaultdict(__lowerCAmelCase       )

							for outer_width in range(3 ,   (t_limit // 4) + 2       ):
														if outer_width * outer_width > t_limit:
																					a__					=					max(
																					    ceil(sqrt(outer_width * outer_width - t_limit       )       ) ,   1       )
														else:
																					a__					=					1

														hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

														for hole_width in range(__lowerCAmelCase ,   outer_width - 1 ,   2       ):
																					count[outer_width * outer_width - hole_width * hole_width] += 1

							return sum(1 for n in count.values() if 1 <= n <= 1_0       )


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




