



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_      :      Optional[int]    =     logging.get_logger(__name__)

a_      :      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    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Tuple      						=					'''longformer'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE = 5_1_2		,				SCREAMING_SNAKE_CASE = 2		,				SCREAMING_SNAKE_CASE = 1		,				SCREAMING_SNAKE_CASE = 0		,				SCREAMING_SNAKE_CASE = 2		,				SCREAMING_SNAKE_CASE = 3_0_5_2_2		,				SCREAMING_SNAKE_CASE = 7_6_8		,				SCREAMING_SNAKE_CASE = 1_2		,				SCREAMING_SNAKE_CASE = 1_2		,				SCREAMING_SNAKE_CASE = 3_0_7_2		,				SCREAMING_SNAKE_CASE = "gelu"		,				SCREAMING_SNAKE_CASE = 0.1		,				SCREAMING_SNAKE_CASE = 0.1		,				SCREAMING_SNAKE_CASE = 5_1_2		,				SCREAMING_SNAKE_CASE = 2		,				SCREAMING_SNAKE_CASE = 0.02		,				SCREAMING_SNAKE_CASE = 1e-1_2		,				SCREAMING_SNAKE_CASE = False		,				**SCREAMING_SNAKE_CASE		,				) ->			int:
  super().__init__(pad_token_id=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  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    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = "default"		,				SCREAMING_SNAKE_CASE = None      ) ->			Optional[int]:
  super().__init__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  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		,				1_4      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = -1		,				SCREAMING_SNAKE_CASE = -1		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = None		,				) ->			Mapping[str, Any]:
  a__												= super().generate_dummy_inputs(
      preprocessor=SCREAMING_SNAKE_CASE		,				batch_size=SCREAMING_SNAKE_CASE		,				seq_length=SCREAMING_SNAKE_CASE		,				is_pair=SCREAMING_SNAKE_CASE		,				framework=SCREAMING_SNAKE_CASE      )
  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










import requests
from bsa import BeautifulSoup






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= BeautifulSoup(requests.get(__UpperCAmelCase							,  params=__UpperCAmelCase       ).content							,  '''html.parser'''       )
 a__												= soup.find('''div'''							,  attrs={'''class''': '''gs_ri'''}       )
 a__												= div.find('''div'''							,  attrs={'''class''': '''gs_fl'''}       ).find_all('''a'''       )
 return anchors[2].get_text()


if __name__ == "__main__":
     a_      :      Optional[Any]    =     {
         'title': (
             'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
             'capacitance, volumetric capacitance, and energy density'
         ),
         'journal': 'Chem. Mater.',
         'volume': 30,
         'pages': '3979-3990',
         'year': 20_18,
         'hl': 'en',
     }
     print(get_citation('https://scholar.google.com/scholar_lookup', params=params))





