



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"plbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      str=5_00_05						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Tuple=6						,						__lowerCamelCase   :      Tuple=30_72						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      List[Any]=6						,						__lowerCamelCase   :      Dict=30_72						,						__lowerCamelCase   :      Optional[Any]=12						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      Any=True						,						__lowerCamelCase   :      Any=True						,						__lowerCamelCase   :      Union[str, Any]="gelu"						,						__lowerCamelCase   :      Optional[Any]=7_68						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Optional[Any]=0.1						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Optional[Any]=0.02						,						__lowerCamelCase   :      str=0.0						,						__lowerCamelCase   :      int=True						,						__lowerCamelCase   :      Tuple=1						,						__lowerCamelCase   :      Tuple=0						,						__lowerCamelCase   :      Union[str, Any]=2						,						__lowerCamelCase   :      Union[str, Any]=2						,						**__lowerCamelCase   :      List[str]						,						)   ->					List[str]:
									A				:       Optional[Any]														=    vocab_size
									A				:       Tuple														=    max_position_embeddings
									A				:       Tuple														=    d_model
									A				:       Optional[int]														=    encoder_ffn_dim
									A				:       Tuple														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Dict														=    decoder_ffn_dim
									A				:       int														=    decoder_layers
									A				:       Dict														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       List[Any]														=    attention_dropout
									A				:       Union[str, Any]														=    activation_dropout
									A				:       Optional[Any]														=    activation_function
									A				:       int														=    init_std
									A				:       Any														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       List[str]														=    classifier_dropout
									A				:       Tuple														=    use_cache
									A				:       Any														=    encoder_layers
									A				:       Union[str, Any]														=    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__lowerCamelCase						,						bos_token_id=__lowerCamelCase						,						eos_token_id=__lowerCamelCase						,						is_encoder_decoder=__lowerCamelCase						,						forced_eos_token_id=__lowerCamelCase						,						**__lowerCamelCase						,						)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







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







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Mapping[str, Mapping[int, str]]:
									if self.use_past:
												return OrderedDict(
												    [
												        ("last_hidden_state", {0: "batch", 1: "sequence"}),
												        ("past_keys", {0: "batch", 2: "sequence"}),
												        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
												    ]			)
									else:
												return OrderedDict(
												    [
												        ("last_hidden_state", {0: "batch", 1: "sequence"}),
												        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
												    ]			)




import math
from decimal import Decimal, getcontext




def 						UpperCAmelCase (    _lowerCamelCase = 400_0000     ):

			try:
						A				:       Any														=    int(_lowerCamelCase     )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."     )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."     )
			A				:       int														=    100
			A				:       Tuple														=    (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

			A				:       str														=    (math.floor(math.log(n * (phi + 2)			,     _lowerCamelCase     ) - 1     ) // 3) * 3 + 2
			A				:       Optional[int]														=    Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
			A				:       str														=    num // 2
			return int(_lowerCamelCase     )


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