

def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      int					)				->							float:
 if common_ratio == 1:
  # Formula for sum if common ratio is 1
  return num_of_terms * first_term

 # Formula for finding sum of n terms of a GeometricProgression
 return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)








from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				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     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      str    				=							'''plbart'''
	_lowercase		:      str    				=							['''past_key_values''']
	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
		__A   : List[Any]								= vocab_size
		__A   : str								= max_position_embeddings
		__A   : Union[str, Any]								= d_model
		__A   : List[str]								= encoder_ffn_dim
		__A   : Optional[Any]								= encoder_layers
		__A   : Tuple								= encoder_attention_heads
		__A   : List[Any]								= decoder_ffn_dim
		__A   : Optional[int]								= decoder_layers
		__A   : Any								= decoder_attention_heads
		__A   : Optional[int]								= dropout
		__A   : Any								= attention_dropout
		__A   : Tuple								= activation_dropout
		__A   : List[Any]								= activation_function
		__A   : Tuple								= init_std
		__A   : List[Any]								= encoder_layerdrop
		__A   : str								= decoder_layerdrop
		__A   : Union[str, Any]								= classifier_dropout
		__A   : Optional[Any]								= use_cache
		__A   : List[str]								= encoder_layers
		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
		super().__init__(
		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)






class     lowerCamelCase_			(  _lowercase  ):




	@property
	def 					lowerCAmelCase_					(    self	:    Any       ):
		return OrderedDict(
		    [
		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
		    ]       )




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		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"""}),
			    ]       )





