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       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "imagegpt"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "hidden_size": "n_embd",
							    "max_position_embeddings": "n_positions",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}







							def __init__(   self      :     int ,			_A      :     Any=512 + 1 ,			_A      :     Tuple=32 * 32 ,			_A      :     Union[str, Any]=512 ,			_A      :     Union[str, Any]=24 ,			_A      :     Dict=8 ,			_A      :     List[str]=None ,			_A      :     List[Any]="quick_gelu" ,			_A      :     int=0.1 ,			_A      :     Optional[Any]=0.1 ,			_A      :     int=0.1 ,			_A      :     int=1e-5 ,			_A      :     List[str]=0.02 ,			_A      :     Optional[Any]=True ,			_A      :     List[str]=True ,			_A      :     Any=False ,			_A      :     Optional[Any]=False ,			_A      :     List[str]=False ,			**_A      :     Any ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     n_positions
												_UpperCamelCase			     =     n_embd
												_UpperCamelCase			     =     n_layer
												_UpperCamelCase			     =     n_head
												_UpperCamelCase			     =     n_inner
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     resid_pdrop
												_UpperCamelCase			     =     embd_pdrop
												_UpperCamelCase			     =     attn_pdrop
												_UpperCamelCase			     =     layer_norm_epsilon
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     scale_attn_weights
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_attn_by_inverse_layer_idx
												_UpperCamelCase			     =     reorder_and_upcast_attn
												_UpperCamelCase			     =     tie_word_embeddings

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




class     lowerCAmelCase_     (       __lowercase ):







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
												    ]		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     "FeatureExtractionMixin" ,			_A      :     int = 1 ,			_A      :     int = -1 ,			_A      :     bool = False ,			_A      :     Optional["TensorType"] = None ,			_A      :     int = 3 ,			_A      :     int = 32 ,			_A      :     int = 32 ,			):
												_UpperCamelCase			     =     self._generate_dummy_images(_A ,			_A ,			_A ,			_A		)
												_UpperCamelCase			     =     dict(preprocessor(images=_A ,			return_tensors=_A		)		)

												return inputs

def 					_snake_case	(  __snake_case = 100						):
					_UpperCamelCase			     =     (n * (n + 1) // 2) ** 2
					_UpperCamelCase			     =     n * (n + 1) * (2 * n + 1) // 6
					return sum_cubes - sum_squares


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