



'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
					from ...onnx.config import PatchingSpec
					from ...tokenization_utils_base import PreTrainedTokenizerBase


_UpperCamelCase							 =	logging.get_logger(__name__)

_UpperCamelCase							 =	{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class _A     (			__A			):
		_SCREAMING_SNAKE_CASE    :			Any  					=	'longformer'





		def __init__(   self       ,				__UpperCAmelCase = 512       ,				__UpperCAmelCase = 2       ,				__UpperCAmelCase = 1       ,				__UpperCAmelCase = 0       ,				__UpperCAmelCase = 2       ,				__UpperCAmelCase = 30_522       ,				__UpperCAmelCase = 768       ,				__UpperCAmelCase = 12       ,				__UpperCAmelCase = 12       ,				__UpperCAmelCase = 3_072       ,				__UpperCAmelCase = "gelu"       ,				__UpperCAmelCase = 0.1       ,				__UpperCAmelCase = 0.1       ,				__UpperCAmelCase = 512       ,				__UpperCAmelCase = 2       ,				__UpperCAmelCase = 0.02       ,				__UpperCAmelCase = 1E-12       ,				__UpperCAmelCase = False       ,				**__UpperCAmelCase       ,				)				->   Optional[int]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase       ,				**__UpperCAmelCase )

					__UpperCAmelCase      :					List[str]   =   attention_window
					__UpperCAmelCase      :					str   =   sep_token_id
					__UpperCAmelCase      :					Dict   =   bos_token_id
					__UpperCAmelCase      :					List[Any]   =   eos_token_id
					__UpperCAmelCase      :					int   =   vocab_size
					__UpperCAmelCase      :					Any   =   hidden_size
					__UpperCAmelCase      :					str   =   num_hidden_layers
					__UpperCAmelCase      :					Optional[Any]   =   num_attention_heads
					__UpperCAmelCase      :					str   =   hidden_act
					__UpperCAmelCase      :					Optional[Any]   =   intermediate_size
					__UpperCAmelCase      :					Optional[int]   =   hidden_dropout_prob
					__UpperCAmelCase      :					Dict   =   attention_probs_dropout_prob
					__UpperCAmelCase      :					Tuple   =   max_position_embeddings
					__UpperCAmelCase      :					Union[str, Any]   =   type_vocab_size
					__UpperCAmelCase      :					Any   =   initializer_range
					__UpperCAmelCase      :					Dict   =   layer_norm_eps
					__UpperCAmelCase      :					List[Any]   =   onnx_export



class _A     (			__A			):





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = "default"       ,				__UpperCAmelCase = None )				->   int:

					'''simple docstring'''
					super().__init__(__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase )
					__UpperCAmelCase      :					Dict   =   True





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

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase      :					Dict   =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase      :					Optional[Any]   =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""global_attention_mask""", dynamic_axis),
					    ] )





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

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   super().outputs
					if self.task == "default":
								__UpperCAmelCase      :					Any   =   {0: """batch"""}
					return outputs





		@property
		def 							__A				(   self )				->   float:

					'''simple docstring'''
					return 1E-4





		@property
		def 							__A				(   self )				->   int:

					'''simple docstring'''
					# needs to be >= 14 to support tril operator
					return max(super().default_onnx_opset       ,				14 )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = -1       ,				__UpperCAmelCase = -1       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = None       ,				)				->   Mapping[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[Any]   =   super().generate_dummy_inputs(
					    preprocessor=__UpperCAmelCase       ,				batch_size=__UpperCAmelCase       ,				seq_length=__UpperCAmelCase       ,				is_pair=__UpperCAmelCase       ,				framework=__UpperCAmelCase )
					import torch

					# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
					# makes the export fail randomly
					__UpperCAmelCase      :					List[str]   =   torch.zeros_like(inputs["""input_ids"""] )
					# make every second token global
					__UpperCAmelCase      :					int   =   1

					return inputs





"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase							 =     TypeVar("T")
class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							data
													A__		   =							self
													A__		   =							0






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# map from node name to the node object
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# create a new set with x as its member
													A__		   =							DisjointSetTreeNode(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> DisjointSetTreeNode[T]:
													# find the set x belongs to (with path-compression)
													A__		   =							self.map[data]
													if elem_ref != elem_ref.parent:
																				A__		   =							self.find_set(elem_ref.parent.data   )
													return elem_ref.parent





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# helper function for union operation
													if nodea.rank > nodea.rank:
																				A__		   =							nodea
													else:
																				A__		   =							nodea
																				if nodea.rank == nodea.rank:
																											nodea.rank += 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# merge 2 disjoint sets
													self.link(self.find_set(__UpperCAmelCase   )						,self.find_set(__UpperCAmelCase   )   )






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# connections: map from the node to the neighbouring nodes (with weights)
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# add a node ONLY if its not present in the graph
													if node not in self.connections:
																				A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# add an edge with the given weight
													self.add_node(__UpperCAmelCase   )
													self.add_node(__UpperCAmelCase   )
													A__		   =							weight
													A__		   =							weight





						def  snake_case__		(				self   )    -> GraphUndirectedWeighted[T]:
													A__		   =							[]
													A__		   =							set()
													for start in self.connections:
																				for end in self.connections[start]:
																											if (start, end) not in seen:
																																		seen.add((end, start)   )
																																		edges.append((start, end, self.connections[start][end])   )
													edges.sort(key=lambda __UpperCAmelCase   : x[2]   )

													# creating the disjoint set
													A__		   =							DisjointSetTree[T]()
													for node in self.connections:
																				disjoint_set.make_set(__UpperCAmelCase   )

													# MST generation
													A__		   =							0
													A__		   =							0
													A__		   =							GraphUndirectedWeighted[T]()
													while num_edges < len(self.connections   ) - 1:
																				A__   ,     A__   ,     A__		   =							edges[index]
																				index += 1
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				if parent_u != parent_v:
																											num_edges += 1
																											graph.add_edge(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
																											disjoint_set.union(__UpperCAmelCase						,__UpperCAmelCase   )
													return graph





