



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


lowerCAmelCase__		:     Optional[Any]       						=logging.get_logger(__name__)

lowerCAmelCase__		:     Tuple       						={
    '''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_  (   UpperCamelCase_       ):

						'''simple docstring'''




						UpperCamelCase__			: Optional[int]				  =  "longformer"






						def __init__(    self      ,							_A = 512      ,							_A = 2      ,							_A = 1      ,							_A = 0      ,							_A = 2      ,							_A = 30_522      ,							_A = 768      ,							_A = 12      ,							_A = 12      ,							_A = 3_072      ,							_A = "gelu"      ,							_A = 0.1      ,							_A = 0.1      ,							_A = 512      ,							_A = 2      ,							_A = 0.0_2      ,							_A = 1e-12      ,							_A = False      ,							**_A      ,							):







													'''simple docstring'''

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

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







class    UpperCAmelCase_  (   UpperCamelCase_       ):

						'''simple docstring'''




						def __init__(    self      ,							_A      ,							_A = "default"      ,							_A = None		):







													'''simple docstring'''

													super().__init__(snake_case__      ,							snake_case__      ,							snake_case__		)
													__SCREAMING_SNAKE_CASE									=					True






						@property
						def        _A	(    self		):







													'''simple docstring'''

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






						@property
						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					super().outputs
													if self.task == "default":
																				__SCREAMING_SNAKE_CASE									=					{0: "batch"}
													return outputs






						@property
						def        _A	(    self		):







													'''simple docstring'''

													return 1e-4






						@property
						def        _A	(    self		):







													'''simple docstring'''

													return max(super().default_onnx_opset      ,							14		)






						def        _A	(    self      ,							_A      ,							_A = -1      ,							_A = -1      ,							_A = False      ,							_A = None      ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					super().generate_dummy_inputs(
													    preprocessor=snake_case__      ,							batch_size=snake_case__      ,							seq_length=snake_case__      ,							is_pair=snake_case__      ,							framework=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
													__SCREAMING_SNAKE_CASE									=					torch.zeros_like(inputs['input_ids']		)
													# make every second token global
													__SCREAMING_SNAKE_CASE									=					1

													return inputs










"""simple docstring"""







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				int
       a  :				TreeNode | None   =None
       a  :				TreeNode | None   =None

lowerCAmelCase__      				=   namedtuple('''CoinsDistribResult''', '''moves excess''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    TreeNode | None				):


  '''simple docstring'''







  if root is None:
    return 0

  # Validation
  def count_nodes(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_nodes(node.left				) + count_nodes(node.right				) + 1

  def count_coins(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_coins(node.left				) + count_coins(node.right				) + node.data

  if count_nodes(SCREAMING_SNAKE_CASE				) != count_coins(SCREAMING_SNAKE_CASE				):
    raise ValueError("The nodes number should be same as the number of coins"				)

  # Main calculation
  def get_distrib(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> CoinsDistribResult:

    if node is None:
      return CoinsDistribResult(0				,      1				)

    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.left				)
    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.right				)

    lowerCAmelCase     :     Union[str, Any]								=							1 - left_distrib_excess
    lowerCAmelCase     :     List[Any]								=							1 - right_distrib_excess

    lowerCAmelCase     :     int								=							(
        left_distrib_moves
        + right_distrib_moves
        + abs(SCREAMING_SNAKE_CASE				)
        + abs(SCREAMING_SNAKE_CASE				)
    )
    lowerCAmelCase     :     int								=							node.data - coins_to_left - coins_to_right

    return CoinsDistribResult(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  return get_distrib(SCREAMING_SNAKE_CASE				)[0]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





