






"""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


lowercase_				     =     logging.get_logger(__name__)

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


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	str						 = "layoutlmv3"


   def __init__(	self :     Optional[int],       _lowerCamelCase :     Union[str, Any]=5_02_65,       _lowerCamelCase :     Union[str, Any]=7_68,       _lowerCamelCase :     Union[str, Any]=12,       _lowerCamelCase :     Union[str, Any]=12,       _lowerCamelCase :     Dict=30_72,       _lowerCamelCase :     Any="gelu",       _lowerCamelCase :     Union[str, Any]=0.1,       _lowerCamelCase :     List[Any]=0.1,       _lowerCamelCase :     Any=5_12,       _lowerCamelCase :     Tuple=2,       _lowerCamelCase :     List[Any]=0.02,       _lowerCamelCase :     Optional[Any]=1e-5,       _lowerCamelCase :     str=1,       _lowerCamelCase :     Dict=0,       _lowerCamelCase :     Optional[Any]=2,       _lowerCamelCase :     Any=10_24,       _lowerCamelCase :     Union[str, Any]=1_28,       _lowerCamelCase :     Optional[int]=1_28,       _lowerCamelCase :     Tuple=True,       _lowerCamelCase :     Optional[int]=32,       _lowerCamelCase :     int=1_28,       _lowerCamelCase :     Optional[int]=64,       _lowerCamelCase :     Tuple=2_56,       _lowerCamelCase :     str=True,       _lowerCamelCase :     List[str]=True,       _lowerCamelCase :     Dict=True,       _lowerCamelCase :     str=2_24,       _lowerCamelCase :     int=3,       _lowerCamelCase :     Any=16,       _lowerCamelCase :     Union[str, Any]=None,       **_lowerCamelCase :     Dict,       ):

     '''simple docstring'''
     super().__init__(
         vocab_size=_lowerCamelCase,       hidden_size=_lowerCamelCase,       num_hidden_layers=_lowerCamelCase,       num_attention_heads=_lowerCamelCase,       intermediate_size=_lowerCamelCase,       hidden_act=_lowerCamelCase,       hidden_dropout_prob=_lowerCamelCase,       attention_probs_dropout_prob=_lowerCamelCase,       max_position_embeddings=_lowerCamelCase,       type_vocab_size=_lowerCamelCase,       initializer_range=_lowerCamelCase,       layer_norm_eps=_lowerCamelCase,       pad_token_id=_lowerCamelCase,       bos_token_id=_lowerCamelCase,       eos_token_id=_lowerCamelCase,       **_lowerCamelCase,       )
     __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						):


   '''simple docstring'''

   A_						:	List[Any]						 = version.parse("1.12"						)


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # 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 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     return 1e-5


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     return 12






   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     "ProcessorMixin",       _lowerCamelCase :     int = -1,       _lowerCamelCase :     int = -1,       _lowerCamelCase :     bool = False,       _lowerCamelCase :     Optional["TensorType"] = None,       _lowerCamelCase :     int = 3,       _lowerCamelCase :     int = 40,       _lowerCamelCase :     int = 40,       ):

     '''simple docstring'''
     setattr(processor.image_processor,       '''apply_ocr''',       _lowerCamelCase  )

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     __A									= compute_effective_axis_dimension(
         _lowerCamelCase,       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(_lowerCamelCase  )
     __A									= compute_effective_axis_dimension(
         _lowerCamelCase,       fixed_dimension=OnnxConfig.default_fixed_sequence,       num_token_to_add=_lowerCamelCase  )
     # 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(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase  )

     __A									= dict(
         processor(
             _lowerCamelCase,       text=_lowerCamelCase,       boxes=_lowerCamelCase,       return_tensors=_lowerCamelCase,       )  )

     return inputs












"""simple docstring"""


from collections import defaultdict
from math import ceil, sqrt



def 					lowerCAmelCase				(    __UpperCamelCase = 1_0_0_0_0_0_0		,       __UpperCamelCase = 1_0 ):




  """simple docstring"""







  __A									= defaultdict(__UpperCamelCase )

  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(__UpperCamelCase		,       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() = }''')




