


'''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


__SCREAMING_SNAKE_CASE					:				Any									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Any									=		{
    '''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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       int   						="longformer"






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      Union[List[int], int] = 5_12 ,  lowerCAmelCase							:      int = 2 ,  lowerCAmelCase							:      int = 1 ,  lowerCAmelCase							:      int = 0 ,  lowerCAmelCase							:      int = 2 ,  lowerCAmelCase							:      int = 3_05_22 ,  lowerCAmelCase							:      int = 7_68 ,  lowerCAmelCase							:      int = 12 ,  lowerCAmelCase							:      int = 12 ,  lowerCAmelCase							:      int = 30_72 ,  lowerCAmelCase							:      str = "gelu" ,  lowerCAmelCase							:      float = 0.1 ,  lowerCAmelCase							:      float = 0.1 ,  lowerCAmelCase							:      int = 5_12 ,  lowerCAmelCase							:      int = 2 ,  lowerCAmelCase							:      float = 0.0_2 ,  lowerCAmelCase							:      float = 1e-12 ,  lowerCAmelCase							:      bool = False ,  **lowerCAmelCase							:      Union[str, Any] ,  ):
													super().__init__(pad_token_id=lowerCAmelCase ,  **lowerCAmelCase	)

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






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      "PretrainedConfig" ,  lowerCAmelCase							:      str = "default" ,  lowerCAmelCase							:      "List[PatchingSpec]" = None	):
													super().__init__(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		True






						@property
						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													if self.task == "multiple-choice":
																				A_			    =		{0: "batch", 1: "choice", 2: "sequence"}
													else:
																				A_			    =		{0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													        ("global_attention_mask", dynamic_axis),
													    ]	)






						@property
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		super().outputs
													if self.task == "default":
																				A_			    =		{0: "batch"}
													return outputs






						@property
						def 				_UpperCAmelCase       (  self							:      int	):
													return 1e-4






						@property
						def 				_UpperCAmelCase       (  self							:      Dict	):
													# needs to be >= 14 to support tril operator
													return max(super().default_onnx_opset ,  14	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      "PreTrainedTokenizerBase" ,  lowerCAmelCase							:      int = -1 ,  lowerCAmelCase							:      int = -1 ,  lowerCAmelCase							:      bool = False ,  lowerCAmelCase							:      Optional[TensorType] = None ,  ):
													A_			    =		super().generate_dummy_inputs(
													    preprocessor=lowerCAmelCase ,  batch_size=lowerCAmelCase ,  seq_length=lowerCAmelCase ,  is_pair=lowerCAmelCase ,  framework=lowerCAmelCase	)
													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
													A_			    =		torch.zeros_like(inputs["input_ids"]	)
													# make every second token global
													A_			    =		1

													return inputs









'''simple docstring'''
from __future__ import annotations

from typing import Any







def       a_				(	UpperCamelCase_ ):
							create_state_space_tree(UpperCamelCase_						,  []						,  0 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):

							if index == len(UpperCamelCase_ ):
														print(UpperCamelCase_ )
														return

							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.append(sequence[index] )
							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.pop()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				list[Any]									=		[3, 1, 2, 4]
							generate_all_subsequences(seq)

							seq.clear()
							seq.extend(['''A''', '''B''', '''C'''])
							generate_all_subsequences(seq)





