





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

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


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, TensorType

__lowerCamelCase			:	Optional[int]					    =	logging.get_logger(__name__)

__lowerCamelCase			:	int					    =	{
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Optional[int]		       =  'imagegpt'
      _UpperCAmelCase      :Any		       =  ['past_key_values']
      _UpperCAmelCase      :List[str]		       =  {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(					self			,				A_=512 + 1			,				A_=32 * 32			,				A_=512			,				A_=24			,				A_=8			,				A_=None			,				A_="quick_gelu"			,				A_=0.1			,				A_=0.1			,				A_=0.1			,				A_=1e-5			,				A_=0.02			,				A_=True			,				A_=True			,				A_=False			,				A_=False			,				A_=False			,				**A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  vocab_size
       UpperCamelCase      :   List[Any]			      =  n_positions
       UpperCamelCase      :   Any			      =  n_embd
       UpperCamelCase      :   Any			      =  n_layer
       UpperCamelCase      :   Tuple			      =  n_head
       UpperCamelCase      :   Optional[int]			      =  n_inner
       UpperCamelCase      :   List[Any]			      =  activation_function
       UpperCamelCase      :   Tuple			      =  resid_pdrop
       UpperCamelCase      :   Dict			      =  embd_pdrop
       UpperCamelCase      :   Optional[Any]			      =  attn_pdrop
       UpperCamelCase      :   Optional[int]			      =  layer_norm_epsilon
       UpperCamelCase      :   Any			      =  initializer_range
       UpperCamelCase      :   List[str]			      =  scale_attn_weights
       UpperCamelCase      :   int			      =  use_cache
       UpperCamelCase      :   Tuple			      =  scale_attn_by_inverse_layer_idx
       UpperCamelCase      :   Optional[int]			      =  reorder_and_upcast_attn
       UpperCamelCase      :   Optional[int]			      =  tie_word_embeddings

       super().__init__(tie_word_embeddings=A_			,				**A_  )



class    A__  (			__snake_case     ):





      @property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "sequence"}),
           ]  )





      def 			__UpperCamelCase(					self			,				A_			,				A_ = 1			,				A_ = -1			,				A_ = False			,				A_ = None			,				A_ = 3			,				A_ = 32			,				A_ = 32			,				):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  self._generate_dummy_images(A_			,				A_			,				A_			,				A_  )
       UpperCamelCase      :   Tuple			      =  dict(preprocessor(images=A_			,				return_tensors=A_  )  )

       return inputs







def 						A_							(	_lowerCAmelCase = 1000	)		->					int:
 UpperCamelCase      :   Optional[int]			      =  -1
 UpperCamelCase      :   int			      =  0
 for a in range(1				,  n // 3	):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  UpperCamelCase      :   Optional[Any]			      =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  UpperCamelCase      :   List[str]			      =  n - a - b
  if c * c == (a * a + b * b):
   UpperCamelCase      :   Union[str, Any]			      =  a * b * c
   if candidate >= product:
    UpperCamelCase      :   Tuple			      =  candidate
 return product


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