





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase       :Union[str, Any]       =  logging.get_logger(__name__)

__lowercase       :Any       =  {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"xglm"
							snake_case_							 =						["past_key_values"]

							snake_case_							 =						{
							    "num_attention_heads": "attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "num_layers",
							}

							def __init__(      self				:     Union[str, Any]							,	a				:     int=25_60_08							,	a				:     str=20_48							,	a				:     Union[str, Any]=10_24							,	a				:     str=40_96							,	a				:     Optional[Any]=24							,	a				:     Union[str, Any]=16							,	a				:     Optional[Any]="gelu"							,	a				:     List[Any]=0.1							,	a				:     Dict=0.1							,	a				:     Any=0.0							,	a				:     Optional[int]=0.0							,	a				:     Union[str, Any]=0.02							,	a				:     List[Any]=True							,	a				:     Optional[Any]=True							,	a				:     Union[str, Any]=2							,	a				:     str=1							,	a				:     Union[str, Any]=0							,	a				:     List[Any]=2							,	**a				:     Any							,	)	->Tuple:
													SCREAMING_SNAKE_CASE__     :				str   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				str   =					max_position_embeddings
													SCREAMING_SNAKE_CASE__     :				str   =					d_model
													SCREAMING_SNAKE_CASE__     :				Any   =					ffn_dim
													SCREAMING_SNAKE_CASE__     :				str   =					num_layers
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					attention_heads
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					activation_function
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					dropout
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					attention_dropout
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					activation_dropout
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					layerdrop
													SCREAMING_SNAKE_CASE__     :				int   =					init_std
													SCREAMING_SNAKE_CASE__     :				str   =					scale_embedding  # scale factor will be sqrt(d_model) if True
													SCREAMING_SNAKE_CASE__     :				str   =					use_cache

													super().__init__(
													    pad_token_id=a							,	bos_token_id=a							,	eos_token_id=a							,	decoder_start_token_id=a							,	**a							,	)










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 4_000_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[0, 1]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						while fib[i] <= n:
												fib.append(fib[i] + fib[i + 1]     )
												if fib[i + 2] > n:
																		break
												i += 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						for j in range(len(_lowerCamelCase     ) - 1     ):
												if fib[j] % 2 == 0:
																		total += fib[j]

						return total


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