





"""simple docstring"""


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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__magic_name__					:				Any	= logging.get_logger(__name__)

__magic_name__					:				Tuple	= {
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							List[Any]								=     """gpt2"""
			__lowerCAmelCase    :							Optional[int]								=     ["""past_key_values"""]
			__lowerCAmelCase    :							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=5_0_2_5_7	,      _A=1_0_2_4	,      _A=7_6_8	,      _A=1_2	,      _A=1_2	,      _A=None	,      _A="gelu_new"	,      _A=0.1	,      _A=0.1	,      _A=0.1	,      _A=1e-5	,      _A=0.02	,      _A="cls_index"	,      _A=True	,      _A=None	,      _A=True	,      _A=0.1	,      _A=True	,      _A=True	,      _A=5_0_2_5_6	,      _A=5_0_2_5_6	,      _A=False	,      _A=False	,      **_A	,      ):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = vocab_size
									UpperCamelCase							:		str							      = n_positions
									UpperCamelCase							:		Dict							      = n_embd
									UpperCamelCase							:		str							      = n_layer
									UpperCamelCase							:		List[str]							      = n_head
									UpperCamelCase							:		Dict							      = n_inner
									UpperCamelCase							:		List[str]							      = activation_function
									UpperCamelCase							:		Union[str, Any]							      = resid_pdrop
									UpperCamelCase							:		Optional[Any]							      = embd_pdrop
									UpperCamelCase							:		Any							      = attn_pdrop
									UpperCamelCase							:		Optional[Any]							      = layer_norm_epsilon
									UpperCamelCase							:		List[Any]							      = initializer_range
									UpperCamelCase							:		Any							      = summary_type
									UpperCamelCase							:		Optional[int]							      = summary_use_proj
									UpperCamelCase							:		str							      = summary_activation
									UpperCamelCase							:		str							      = summary_first_dropout
									UpperCamelCase							:		Union[str, Any]							      = summary_proj_to_labels
									UpperCamelCase							:		Optional[Any]							      = scale_attn_weights
									UpperCamelCase							:		Any							      = use_cache
									UpperCamelCase							:		Optional[Any]							      = scale_attn_by_inverse_layer_idx
									UpperCamelCase							:		Dict							      = reorder_and_upcast_attn

									UpperCamelCase							:		int							      = bos_token_id
									UpperCamelCase							:		int							      = eos_token_id

									super().__init__(bos_token_id=_A	,      eos_token_id=_A	,      **_A						)






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""







			def __init__(			self	,      _A	,      _A = "default"	,      _A = None	,      _A = False	,      ):






									'''simple docstring'''







									super().__init__(_A	,      task=_A	,      patching_specs=_A	,      use_past=_A						)
									if not getattr(self._config	,      """pad_token_id"""	,      _A						):
															# TODO: how to do that better?
															UpperCamelCase							:		int							      = 0







			@property
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
									if self.use_past:
															self.fill_with_past_key_values_(_A	,      direction="""inputs"""						)
															UpperCamelCase							:		Union[str, Any]							      = {0: """batch""", 1: """past_sequence + sequence"""}
									else:
															UpperCamelCase							:		List[Any]							      = {0: """batch""", 1: """sequence"""}

									return common_inputs







			@property
			def      _a			(			self						):






									'''simple docstring'''







									return self._config.n_layer







			@property
			def      _a			(			self						):






									'''simple docstring'''







									return self._config.n_head







			def      _a			(			self	,      _A	,      _A = -1	,      _A = -1	,      _A = False	,      _A = None	,      ):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = super(_A	,      self						).generate_dummy_inputs(
									    _A	,      batch_size=_A	,      seq_length=_A	,      is_pair=_A	,      framework=_A						)

									# We need to order the input in the way they appears in the forward()
									UpperCamelCase							:		str							      = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}						)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
															else:
																					import torch

																					UpperCamelCase      ,	UpperCamelCase							:		Union[str, Any]							      = common_inputs["""input_ids"""].shape
																					# Not using the same length for past_key_values
																					UpperCamelCase							:		Dict							      = seqlen + 2
																					UpperCamelCase							:		List[Any]							      = (
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					UpperCamelCase							:		int							      = [
																					    (torch.zeros(_A						), torch.zeros(_A						)) for _ in range(self.num_layers						)
																					]

									UpperCamelCase							:		Any							      = common_inputs["""attention_mask"""]
									if self.use_past:
															UpperCamelCase							:		int							      = ordered_inputs["""attention_mask"""].dtype
															UpperCamelCase							:		Any							      = torch.cat(
															    [ordered_inputs["""attention_mask"""], torch.ones(_A	,      _A	,      dtype=_A						)]	,      dim=1						)

									return ordered_inputs







			@property
			def      _a			(			self						):






									'''simple docstring'''







									return 1_3










"""simple docstring"""


def     UpperCamelCase       (SCREAMING_SNAKE_CASE = 1000    ):
						UpperCamelCase							:		List[str]							      = []
						UpperCamelCase							:		int							      = []
						UpperCamelCase							:		Dict							      = 3
						UpperCamelCase							:		Dict							      = 5
						UpperCamelCase							:		Optional[int]							      = 1
						while True:
												UpperCamelCase							:		int							      = z * temp
												if result < n:
																		zmulti.append(SCREAMING_SNAKE_CASE    )
																		temp += 1
												else:
																		UpperCamelCase							:		Optional[int]							      = 1
																		break
						while True:
												UpperCamelCase							:		Any							      = x * temp
												if result < n:
																		xmulti.append(SCREAMING_SNAKE_CASE    )
																		temp += 1
												else:
																		break
						UpperCamelCase							:		List[Any]							      = list(set(xmulti + zmulti    )    )
						return sum(SCREAMING_SNAKE_CASE    )


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



