





from itertools import combinations, product


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		int			,							lowerCAmelCase:		int			,							lowerCAmelCase:		int )->						bool:
  if xa == ya == 0 or xa == ya == 0:
    return False
  _snake_case      : Optional[Any]														=		xa * xa + ya * ya
  _snake_case      : int														=		xa * xa + ya * ya
  _snake_case      : Optional[int]														=		(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
  return (
      a_square + b_square == c_square
      or a_square + c_square == b_square
      or b_square + c_square == a_square
  )


def 			lowerCamelCase_		(					lowerCAmelCase:		int = 50 )->						int:
  return sum(
      1
      for pta, pta in combinations(product(range(limit + 1 )			,							repeat=2 )			,							2 )
      if is_right(*lowerCAmelCase			,							*lowerCAmelCase ) )


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











from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_							:				Union[str, Any]					    ="""mobilebert"""







  def __init__(					self					:    int					, UpperCamelCase					:    int=3_05_22					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    Any=24					, UpperCamelCase					:    List[str]=4					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    str="relu"					, UpperCamelCase					:    Dict=0.0					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Optional[Any]=5_12					, UpperCamelCase					:    str=2					, UpperCamelCase					:    Tuple=0.02					, UpperCamelCase					:    Union[str, Any]=1e-1_2					, UpperCamelCase					:    Optional[Any]=0					, UpperCamelCase					:    Union[str, Any]=1_28					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    int=True					, UpperCamelCase					:    List[Any]=1_28					, UpperCamelCase					:    Dict=False					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    int="no_norm"					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    str=None					, **UpperCamelCase					:    str					, ):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase					, **UpperCamelCase							)

    _snake_case      : Union[str, Any]														=		vocab_size
    _snake_case      : Any														=		hidden_size
    _snake_case      : Tuple														=		num_hidden_layers
    _snake_case      : Any														=		num_attention_heads
    _snake_case      : Optional[Any]														=		hidden_act
    _snake_case      : List[Any]														=		intermediate_size
    _snake_case      : List[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		max_position_embeddings
    _snake_case      : Optional[int]														=		type_vocab_size
    _snake_case      : Any														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Tuple														=		embedding_size
    _snake_case      : int														=		trigram_input
    _snake_case      : Optional[int]														=		use_bottleneck
    _snake_case      : List[Any]														=		intra_bottleneck_size
    _snake_case      : Any														=		use_bottleneck_attention
    _snake_case      : str														=		key_query_shared_bottleneck
    _snake_case      : List[str]														=		num_feedforward_networks
    _snake_case      : int														=		normalization_type
    _snake_case      : str														=		classifier_activation

    if self.use_bottleneck:
      _snake_case      : Any														=		intra_bottleneck_size
    else:
      _snake_case      : Union[str, Any]														=		hidden_size

    _snake_case      : Dict														=		classifier_dropout




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case      : str														=		{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case      : Dict														=		{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]							)




