

"""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


__UpperCAmelCase =				logging.get_logger(__name__)

__UpperCAmelCase =				{
    '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			(    __lowerCamelCase					):
  snake_case_				       =       """longformer"""

  def __init__(  self		:			Tuple					,A		:			Union[List[int], int] = 512					,A		:			int = 2					,A		:			int = 1					,A		:			int = 0					,A		:			int = 2					,A		:			int = 30_522					,A		:			int = 768					,A		:			int = 12					,A		:			int = 12					,A		:			int = 3_072					,A		:			str = "gelu"					,A		:			float = 0.1					,A		:			float = 0.1					,A		:			int = 512					,A		:			int = 2					,A		:			float = 0.0_2					,A		:			float = 1e-12					,A		:			bool = False					,**A		:			Union[str, Any]					,):






   '''simple docstring'''
   super().__init__(pad_token_id=A					,**A    )

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







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			Any					,A		:			"PretrainedConfig"					,A		:			str = "default"					,A		:			"List[PatchingSpec]" = None    ):






   '''simple docstring'''
   super().__init__(A					,A					,A    )
   UpperCAmelCase__					:  List[Any]					   =					True

  @property
  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   if self.task == "multiple-choice":
    UpperCAmelCase__					:  Optional[Any]					   =					{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
    UpperCAmelCase__					:  Tuple					   =					{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""global_attention_mask""", dynamic_axis),
       ]    )

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					super().outputs
   if self.task == "default":
    UpperCAmelCase__					:  List[str]					   =					{0: """batch"""}
   return outputs

  @property
  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   return 1e-4

  @property
  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   # needs to be >= 14 to support tril operator
   return max(super().default_onnx_opset					,14    )




  def    __lowercase       (  self		:			Union[str, Any]					,A		:			"PreTrainedTokenizerBase"					,A		:			int = -1					,A		:			int = -1					,A		:			bool = False					,A		:			Optional[TensorType] = None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					super().generate_dummy_inputs(
       preprocessor=A					,batch_size=A					,seq_length=A					,is_pair=A					,framework=A    )
   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__					:  Tuple					   =					torch.zeros_like(inputs["""input_ids"""]    )
   # make every second token global
   UpperCAmelCase__					:  Optional[Any]					   =					1

   return inputs








"""simple docstring"""




__UpperCAmelCase =				'Tobias Carryer'

from time import time







class      __lowercase			:

  def __init__(  self		:			Union[str, Any]					,A		:			Dict					,A		:			Optional[int]					,A		:			Union[str, Any]					,A		:			int=int(time()    )    ):  # noqa: B008






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					multiplier
   UpperCAmelCase__					:  Union[str, Any]					   =					increment
   UpperCAmelCase__					:  Dict					   =					modulo
   UpperCAmelCase__					:  Tuple					   =					seed




  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					(self.multiplier * self.seed + self.increment) % self.modulo
   return self.seed


if __name__ == "__main__":
      # Show the LCG in action.
      __UpperCAmelCase =				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
      while True:
            print(lcg.next_number())





