






'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase							=logging.get_logger(__name__)

__lowerCAmelCase							={
    "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    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      "plbart"
					_UpperCamelCase		 =      ["past_key_values"]
					_UpperCamelCase		 =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




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



class    _snake_case   (				snake_case					):

					"""simple docstring"""





					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	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     )      ->	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'}),
															    ]     )









'''simple docstring'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()

