





from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase			:Tuple	 =     logging.get_logger(__name__)

__lowerCAmelCase			:Any	 =     {
    '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 							_a(     __A     ):
		lowerCamelCase__  :Optional[int] 					=		'plbart'
		lowerCamelCase__  :Union[str, Any] 					=		['past_key_values']
		lowerCamelCase__  :List[str] 					=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







		def __init__(     self			,    __snake_case=5_0_0_0_5			,    __snake_case=1_0_2_4			,    __snake_case=6			,    __snake_case=3_0_7_2			,    __snake_case=1_2			,    __snake_case=6			,    __snake_case=3_0_7_2			,    __snake_case=1_2			,    __snake_case=0.0			,    __snake_case=0.0			,    __snake_case=True			,    __snake_case=True			,    __snake_case="gelu"			,    __snake_case=7_6_8			,    __snake_case=0.1			,    __snake_case=0.1			,    __snake_case=0.0			,    __snake_case=0.02			,    __snake_case=0.0			,    __snake_case=True			,    __snake_case=1			,    __snake_case=0			,    __snake_case=2			,    __snake_case=2			,    **__snake_case			,    )   ->   Any:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= vocab_size
							_snake_case							:       List[str]												= max_position_embeddings
							_snake_case							:       List[str]												= d_model
							_snake_case							:       List[Any]												= encoder_ffn_dim
							_snake_case							:       Dict												= encoder_layers
							_snake_case							:       Optional[Any]												= encoder_attention_heads
							_snake_case							:       Tuple												= decoder_ffn_dim
							_snake_case							:       Optional[int]												= decoder_layers
							_snake_case							:       Union[str, Any]												= decoder_attention_heads
							_snake_case							:       List[Any]												= dropout
							_snake_case							:       Tuple												= attention_dropout
							_snake_case							:       int												= activation_dropout
							_snake_case							:       Dict												= activation_function
							_snake_case							:       Dict												= init_std
							_snake_case							:       Optional[int]												= encoder_layerdrop
							_snake_case							:       Tuple												= decoder_layerdrop
							_snake_case							:       Dict												= classifier_dropout
							_snake_case							:       Tuple												= use_cache
							_snake_case							:       Dict												= encoder_layers
							_snake_case							:       List[Any]												= scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=__snake_case			,    bos_token_id=__snake_case			,    eos_token_id=__snake_case			,    is_encoder_decoder=__snake_case			,    forced_eos_token_id=__snake_case			,    **__snake_case			,    )






class 							_a(     __A     ):







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


							'''simple docstring'''

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







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


							'''simple docstring'''

							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"}),
												    ]					)






def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					):
										_snake_case							:       Optional[Any]												= F"""Input value of [number={number}] must be an integer"""
										raise TypeError(UpperCAmelCase					)
					if number < 1:
										raise ValueError("Input must be a positive integer"					)
					return number * (2 * number - 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()