





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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "longformer"

 def __init__(self    :       Optional[Any]	,			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-1_2	,			UpperCAmelCase_    :       bool = False	,			**UpperCAmelCase_    :       Union[str, Any]	,			)	->Optional[int]:



   '''simple docstring'''



   super().__init__(pad_token_id=UpperCAmelCase_	,			**UpperCAmelCase_)

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



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       "PretrainedConfig"	,			UpperCAmelCase_    :       str = "default"	,			UpperCAmelCase_    :       "List[PatchingSpec]" = None)	->Tuple:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
   lowerCamelCase__:				Dict        =True

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__:				Any        ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__:				Tuple        ={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    :       Union[str, Any])	->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =super().outputs
   if self.task == "default":
     lowerCamelCase__:				List[str]        ={0: "batch"}
   return outputs

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->float:



   '''simple docstring'''



   return 1E-4

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->int:



   '''simple docstring'''



   return max(super().default_onnx_opset	,			14)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       "PreTrainedTokenizerBase"	,			UpperCAmelCase_    :       int = -1	,			UpperCAmelCase_    :       int = -1	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       Optional[TensorType] = None	,			)	->Mapping[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        =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
   lowerCamelCase__:				Any        =torch.zeros_like(inputs["input_ids"])
   # make every second token global
   lowerCamelCase__:				Dict        =1

   return inputs








def 					lowerCAmelCase_			(	__a		)					->		str:







  """simple docstring"""





  if isinstance(__a     ,   __a		):
    raise TypeError("'float' object cannot be interpreted as an integer"		)
  if isinstance(__a     ,   __a		):
    raise TypeError("'str' object cannot be interpreted as an integer"		)

  if num == 0:
    return "0b0"

  lowerCamelCase__:				Optional[int]        =False

  if num < 0:
    lowerCamelCase__:				Optional[Any]        =True
    lowerCamelCase__:				List[Any]        =-num

  lowerCamelCase__:				list[int]        =[]
  while num > 0:
    binary.insert(0     ,   num % 2		)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(__a		) for e in binary		)

  return "0b" + "".join(str(__a		) for e in binary		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

