




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

__magic_name__	:       List[Any]     =	logging.get_logger(__name__)

__magic_name__	:       Optional[Any]     =	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}




class 					snake_case__							(_SCREAMING_SNAKE_CASE     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      "imagegpt"
				SCREAMING_SNAKE_CASE_ :							int											=      ["past_key_values"]
				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      {
				    "hidden_size": "n_embd",
				    "max_position_embeddings": "n_positions",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}





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

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




class 					snake_case__							(_SCREAMING_SNAKE_CASE     ):
				"""simple docstring"""



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





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  Optional[Any] = -1      ,   __lowerCamelCase       :  Dict = False      ,   __lowerCamelCase       :  Dict = None      ,   __lowerCamelCase       :  Dict = 3      ,   __lowerCamelCase       :  Optional[int] = 32      ,   __lowerCamelCase       :  Optional[Any] = 32      ,   )      ->  Mapping[str, Any]:
							a					=    self._generate_dummy_images(A_      ,   A_      ,   A_      ,   A_							)
							a					=    dict(preprocessor(images=A_      ,   return_tensors=A_							)							)

							return inputs












__lowerCAmelCase	:       List[Any]     =	{str(digit): digit**5 for digit in range(10)}




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A     )     )




def     __magic_name__       (				):






			'''simple docstring'''







			return sum(
			    number
			    for number in range(1000,						1000000     )
			    if number == digits_fifth_powers_sum(A     )     )


if __name__ == "__main__":
		print(solution())






