



import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
  from ... import PreTrainedTokenizerBase, TensorType

a_								=       logging.get_logger(__name__)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""vision-encoder-decoder"""
 lowerCAmelCase__							       =	True




 def __init__(    self						,					**__UpperCAmelCase					):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase					)
    if "encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError(
           F"""A configuraton of type {self.model_type} cannot be instantiated because """
           F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""					)

    __lowerCamelCase    				=     kwargs.pop('''encoder'''					)
    __lowerCamelCase    				=     encoder_config.pop('''model_type'''					)
    __lowerCamelCase    				=     kwargs.pop('''decoder'''					)
    __lowerCamelCase    				=     decoder_config.pop('''model_type'''					)

    __lowerCamelCase    				=     AutoConfig.for_model(__UpperCAmelCase						,					**__UpperCAmelCase					)
    __lowerCamelCase    				=     AutoConfig.for_model(__UpperCAmelCase						,					**__UpperCAmelCase					)
    __lowerCamelCase    				=     True




 @classmethod
 def 							lowerCamelCase   (    cls						,					__UpperCAmelCase						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''					)
    __lowerCamelCase    				=     True
    __lowerCamelCase    				=     True

    return cls(encoder=encoder_config.to_dict()						,					decoder=decoder_config.to_dict()						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     copy.deepcopy(self.__dict__					)
    __lowerCamelCase    				=     self.encoder.to_dict()
    __lowerCamelCase    				=     self.decoder.to_dict()
    __lowerCamelCase    				=     self.__class__.model_type
    return output



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	version.parse("""1.11""" )




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return OrderedDict(
        [
            ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
        ]					)




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return 1E-4




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}					)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     OrderedDict()
    __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    __lowerCamelCase    				=     {0: '''batch''', 1: '''encoder_sequence'''}

    return common_inputs




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = -1						,					__UpperCAmelCase = -1						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					):



    '''simple docstring'''
    import torch

    __lowerCamelCase    				=     OrderedDict()

    __lowerCamelCase    				=     super().generate_dummy_inputs(
        __UpperCAmelCase						,					batch_size=__UpperCAmelCase						,					seq_length=__UpperCAmelCase						,					is_pair=__UpperCAmelCase						,					framework=__UpperCAmelCase					)

    __lowerCamelCase				,__lowerCamelCase    				=     dummy_input['''input_ids'''].shape
    __lowerCamelCase    				=     (batch, encoder_sequence, self._config.encoder_hidden_size)
    __lowerCamelCase    				=     dummy_input.pop('''input_ids'''					)
    __lowerCamelCase    				=     dummy_input.pop('''attention_mask'''					)
    __lowerCamelCase    				=     torch.zeros(__UpperCAmelCase					)

    return common_inputs



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return VisionEncoderDecoderEncoderOnnxConfig(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = "default"					):



    '''simple docstring'''
    __lowerCamelCase    				=     encoder_config.hidden_size
    return VisionEncoderDecoderDecoderOnnxConfig(__UpperCAmelCase						,					__UpperCAmelCase					)











from __future__ import annotations

from math import ceil, floor, sqrt





def 		a__      (		_UpperCamelCase      :			int = 2_00_00_00				):
   __lowerCamelCase    				=     [0]
   __lowerCamelCase    				=     42

   for idx in range(1			,ceil(sqrt(target * 2				) * 1.1				)				):
      triangle_numbers.append(triangle_numbers[-1] + idx				)

   # we want this to be as close as possible to target
   __lowerCamelCase    				=     0
   # the area corresponding to the grid that gives the product closest to target
   __lowerCamelCase    				=     0
   # an estimate of b, using the quadratic formula
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_floor
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_ceil
   __lowerCamelCase    				=     42

   for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,1				):
      __lowerCamelCase    				=     (-1 + sqrt(1 + 8 * target / triangle_a				)) / 2
      __lowerCamelCase    				=     floor(_UpperCamelCase				)
      __lowerCamelCase    				=     ceil(_UpperCamelCase				)
      __lowerCamelCase    				=     triangle_numbers[b_floor]
      __lowerCamelCase    				=     triangle_numbers[b_ceil]

      if abs(target - triangle_b_first_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_first_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_floor

      if abs(target - triangle_b_second_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_second_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_ceil

   return area


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






