
"""simple docstring"""





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

UpperCamelCase_					:						Tuple				= logging.get_logger(__name__)

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






class __lowerCAmelCase				(					UpperCamelCase_      ):




			"""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						:					List[Any]       ,   _snake_case						:					Union[str, Any]=512 + 1       ,   _snake_case						:					str=32 * 32       ,   _snake_case						:					int=512       ,   _snake_case						:					Optional[Any]=24       ,   _snake_case						:					Any=8       ,   _snake_case						:					List[str]=None       ,   _snake_case						:					Any="quick_gelu"       ,   _snake_case						:					List[str]=0.1       ,   _snake_case						:					Optional[int]=0.1       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					Optional[int]=1e-5       ,   _snake_case						:					str=0.0_2       ,   _snake_case						:					List[str]=True       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Optional[int]=False       ,   _snake_case						:					str=False       ,   _snake_case						:					Dict=False       ,   **_snake_case						:					List[str]       ,   )      -> List[Any]:







								"""simple docstring"""
								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 __lowerCAmelCase				(					UpperCamelCase_      ):




			"""simple docstring"""







			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> Mapping[str, Mapping[int, str]]:







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






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Tuple       ,   _snake_case						:					Union[str, Any] = 1       ,   _snake_case						:					Union[str, Any] = -1       ,   _snake_case						:					Tuple = False       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					Union[str, Any] = 3       ,   _snake_case						:					str = 32       ,   _snake_case						:					Dict = 32       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						self._generate_dummy_images(_a       ,   _a       ,   _a       ,   _a	)
								A_				 =						dict(preprocessor(images=_a       ,   return_tensors=_a	)	)

								return inputs






"""simple docstring"""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0] * n for i in range(__a				)]
					for i in range(__a				):
										A_				 =						y_points[i]

					for i in range(2				,   __a				):
										for j in range(__a				,   __a				):
															A_				 =						(
															    (xa - x_points[j - i + 1]) * q[j][i - 1]
															    - (xa - x_points[j]) * q[j - 1][i - 1]
															) / (x_points[j] - x_points[j - i + 1])

					return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




