


'''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


a_   :      str				    =      logging.get_logger(__name__)

a_   :      Dict				    =      {
    "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       (       _lowercase    ):
 _lowerCAmelCase	   =		"""longformer"""
 def __init__(	self ,			__magic_name__ = 5_12 ,			__magic_name__ = 2 ,			__magic_name__ = 1 ,			__magic_name__ = 0 ,			__magic_name__ = 2 ,			__magic_name__ = 3_05_22 ,			__magic_name__ = 7_68 ,			__magic_name__ = 12 ,			__magic_name__ = 12 ,			__magic_name__ = 30_72 ,			__magic_name__ = "gelu" ,			__magic_name__ = 0.1 ,			__magic_name__ = 0.1 ,			__magic_name__ = 5_12 ,			__magic_name__ = 2 ,			__magic_name__ = 0.0_2 ,			__magic_name__ = 1e-12 ,			__magic_name__ = False ,			**__magic_name__ ,			)   ->							Optional[Any]:
      super().__init__(pad_token_id=__UpperCamelCase ,			**__UpperCamelCase  )

      _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 	a       (       _lowercase    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ = "default" ,			__magic_name__ = None  )   ->							Dict:
      super().__init__(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
      _a      =							True
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Mapping[str, Mapping[int, str]]:
      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  )   ->							Mapping[str, Mapping[int, str]]:
      _a      =							super().outputs
      if self.task == "default":
           _a      =							{0: 'batch'}
      return outputs
 @property
 def 							__UpperCAmelCase						(	self  )   ->							float:
      return 1e-4
 @property
 def 							__UpperCAmelCase						(	self  )   ->							int:
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset ,			14  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = -1 ,			__magic_name__ = -1 ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							Mapping[str, Any]:
      _a      =							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
      _a      =							torch.zeros_like(inputs['input_ids']  )
      # make every second token global
      _a      =							1

      return inputs






'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _a      =							2
     _a      =							[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(lowerCAmelCase__						)
     if n > 1:
          factors.append(lowerCAmelCase__						)
     return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()

