





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

__lowercase       :Any       =  logging.get_logger(__name__)

__lowercase       :Union[str, Any]       =  {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"imagegpt"
							snake_case_							 =						["past_key_values"]
							snake_case_							 =						{
							    "hidden_size": "n_embd",
							    "max_position_embeddings": "n_positions",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}

							def __init__(      self				:     Tuple							,	a				:     Optional[Any]=5_12 + 1							,	a				:     Any=32 * 32							,	a				:     Tuple=5_12							,	a				:     Dict=24							,	a				:     Dict=8							,	a				:     str=None							,	a				:     Dict="quick_gelu"							,	a				:     str=0.1							,	a				:     Union[str, Any]=0.1							,	a				:     List[str]=0.1							,	a				:     Dict=1E-5							,	a				:     Tuple=0.02							,	a				:     str=True							,	a				:     Tuple=True							,	a				:     Dict=False							,	a				:     Optional[Any]=False							,	a				:     Union[str, Any]=False							,	**a				:     Tuple							,	)	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					n_positions
													SCREAMING_SNAKE_CASE__     :				List[str]   =					n_embd
													SCREAMING_SNAKE_CASE__     :				List[str]   =					n_layer
													SCREAMING_SNAKE_CASE__     :				Dict   =					n_head
													SCREAMING_SNAKE_CASE__     :				List[str]   =					n_inner
													SCREAMING_SNAKE_CASE__     :				List[str]   =					activation_function
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					resid_pdrop
													SCREAMING_SNAKE_CASE__     :				Tuple   =					embd_pdrop
													SCREAMING_SNAKE_CASE__     :				Dict   =					attn_pdrop
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					layer_norm_epsilon
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					initializer_range
													SCREAMING_SNAKE_CASE__     :				Tuple   =					scale_attn_weights
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					use_cache
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					scale_attn_by_inverse_layer_idx
													SCREAMING_SNAKE_CASE__     :				int   =					reorder_and_upcast_attn
													SCREAMING_SNAKE_CASE__     :				Dict   =					tie_word_embeddings

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







class    _a     (				lowercase__							):






							"""simple docstring"""

							@property
							def      A_  (      self				:     Dict    )	->Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "sequence"}),
													    ]    )






							def      A_  (      self				:     List[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							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self._generate_dummy_images(a							,	a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					dict(preprocessor(images=a							,	return_tensors=a    )    )

													return inputs






def       UpperCAmelCase							(     _lowerCamelCase      :  int = 1_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					-1
						SCREAMING_SNAKE_CASE__     :				str   =					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
												SCREAMING_SNAKE_CASE__     :				Tuple   =					(n * n - 2 * a * n) // (2 * n - 2 * a)
												SCREAMING_SNAKE_CASE__     :				Dict   =					n - a - b
												if c * c == (a * a + b * b):
																		SCREAMING_SNAKE_CASE__     :				str   =					a * b * c
																		if candidate >= product:
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					candidate
						return product


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