



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


__A       =						logging.get_logger(__name__)

__A       =						{
    '''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 		lowercase						(			UpperCamelCase__):






	"""simple docstring"""


	a__				: Dict		       =							'''longformer'''




	def __init__(     self     :						str						,  __UpperCAmelCase     :						Union[List[int], int] = 512						,  __UpperCAmelCase     :						int = 2						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 2						,  __UpperCAmelCase     :						int = 30_522						,  __UpperCAmelCase     :						int = 768						,  __UpperCAmelCase     :						int = 12						,  __UpperCAmelCase     :						int = 12						,  __UpperCAmelCase     :						int = 3_072						,  __UpperCAmelCase     :						str = "gelu"						,  __UpperCAmelCase     :						float = 0.1						,  __UpperCAmelCase     :						float = 0.1						,  __UpperCAmelCase     :						int = 512						,  __UpperCAmelCase     :						int = 2						,  __UpperCAmelCase     :						float = 0.02						,  __UpperCAmelCase     :						float = 1E-12						,  __UpperCAmelCase     :						bool = False						,  **__UpperCAmelCase     :						Tuple						,  )      ->			List[str]:
							super().__init__(pad_token_id=UpperCamelCase_						,  **UpperCamelCase_					)

							UpperCAmelCase_=       attention_window
							UpperCAmelCase_=       sep_token_id
							UpperCAmelCase_=       bos_token_id
							UpperCAmelCase_=       eos_token_id
							UpperCAmelCase_=       vocab_size
							UpperCAmelCase_=       hidden_size
							UpperCAmelCase_=       num_hidden_layers
							UpperCAmelCase_=       num_attention_heads
							UpperCAmelCase_=       hidden_act
							UpperCAmelCase_=       intermediate_size
							UpperCAmelCase_=       hidden_dropout_prob
							UpperCAmelCase_=       attention_probs_dropout_prob
							UpperCAmelCase_=       max_position_embeddings
							UpperCAmelCase_=       type_vocab_size
							UpperCAmelCase_=       initializer_range
							UpperCAmelCase_=       layer_norm_eps
							UpperCAmelCase_=       onnx_export






class 		lowercase						(			UpperCamelCase__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						"PretrainedConfig"						,  __UpperCAmelCase     :						str = "default"						,  __UpperCAmelCase     :						"List[PatchingSpec]" = None					)      ->			List[Any]:
							super().__init__(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_					)
							UpperCAmelCase_=       True




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







							if self.task == "multiple-choice":
													UpperCAmelCase_=       {0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
													UpperCAmelCase_=       {0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							        ("""global_attention_mask""", dynamic_axis),
							    ]					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_=       super().outputs
							if self.task == "default":
													UpperCAmelCase_=       {0: """batch"""}
							return outputs




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			float:
							return 1E-4




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			int:
							return max(super().default_onnx_opset						,  14					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						"PreTrainedTokenizerBase"						,  __UpperCAmelCase     :						int = -1						,  __UpperCAmelCase     :						int = -1						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						Optional[TensorType] = None						,  )      ->			Mapping[str, Any]:
							UpperCAmelCase_=       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_=       torch.zeros_like(inputs["""input_ids"""]					)
							# make every second token global
							UpperCAmelCase_=       1

							return inputs




from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




