





from __future__ import annotations

from queue import Queue







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       tuple[int, int]:
  a ^= b
  b ^= a
  a ^= b
  return a, b







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       list[list[int]]:
  __snake_case:		Any					    =		1
  while (1 << j) < max_node:
    for i in range(1   ,   max_node + 1):
      __snake_case:		List[Any]					    =		parent[j - 1][parent[j - 1][i]]
    j += 1
  return parent







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  # u must be deeper in the tree than v
  if level[u] < level[v]:
    __snake_case    ,  __snake_case:		Any					    =		swap(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  # making depth of u same as depth of v
  for i in range(18   ,   -1   ,   -1):
    if level[u] - (1 << i) >= level[v]:
      __snake_case:		Dict					    =		parent[i][u]
    # at the same depth if u==v that mean lca is found
  if u == v:
    return u
  # moving both nodes upwards till lca in found
  for i in range(18   ,   -1   ,   -1):
    if parent[i][u] not in [0, parent[i][v]]:
      __snake_case    ,  __snake_case:		List[str]					    =		parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
  return parent[0][u]







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__ = 1   ,   )  ->       tuple[list[int], list[list[int]]]:
  __snake_case:		Tuple					    =		0
  __snake_case:		Queue[int]					    =		Queue(maxsize=SCREAMING_SNAKE_CASE__)
  q.put(SCREAMING_SNAKE_CASE__)
  while q.qsize() != 0:
    __snake_case:		Tuple					    =		q.get()
    for v in graph[u]:
      if level[v] == -1:
        __snake_case:		List[Any]					    =		level[u] + 1
        q.put(SCREAMING_SNAKE_CASE__)
        __snake_case:		Optional[Any]					    =		u
  return level, parent







def      A__				(       )  ->       None:
  __snake_case:		Optional[Any]					    =		13
  # initializing with 0
  __snake_case:		Union[str, Any]					    =		[[0 for _ in range(max_node + 10)] for _ in range(20)]
  # initializing with -1 which means every node is unvisited
  __snake_case:		Optional[int]					    =		[-1 for _ in range(max_node + 10)]
  __snake_case:		dict[int, list[int]]					    =		{
      1: [2, 3, 4],
      2: [5],
      3: [6, 7],
      4: [8],
      5: [9, 10],
      6: [11],
      7: [],
      8: [12, 13],
      9: [],
      10: [],
      11: [],
      12: [],
      13: [],
  }
  __snake_case    ,  __snake_case:		Dict					    =		breadth_first_search(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   1)
  __snake_case:		List[str]					    =		create_sparse(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  print("""LCA of node 1 and 3 is: """   ,   lowest_common_ancestor(1   ,   3   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  print("""LCA of node 5 and 6 is: """   ,   lowest_common_ancestor(5   ,   6   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  print("""LCA of node 7 and 11 is: """   ,   lowest_common_ancestor(7   ,   11   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  print("""LCA of node 6 and 7 is: """   ,   lowest_common_ancestor(6   ,   7   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  print("""LCA of node 4 and 12 is: """   ,   lowest_common_ancestor(4   ,   12   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  print("""LCA of node 8 and 8 is: """   ,   lowest_common_ancestor(8   ,   8   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))


if __name__ == "__main__":
      main()













import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  if "model" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""model."""   ,   """""")
  if "norm1" in orig_key:
    __snake_case:		Tuple					    =		orig_key.replace("""norm1"""   ,   """attention.output.LayerNorm""")
  if "norm2" in orig_key:
    __snake_case:		Any					    =		orig_key.replace("""norm2"""   ,   """output.LayerNorm""")
  if "norm" in orig_key:
    __snake_case:		List[str]					    =		orig_key.replace("""norm"""   ,   """LayerNorm""")
  if "transformer" in orig_key:
    __snake_case:		str					    =		orig_key.split(""".""")[0].split("""_""")[-1]
    __snake_case:		int					    =		orig_key.replace(F'''transformer_{layer_num}'''   ,   F'''encoder.layer.{layer_num}''')
  if "mha.attn" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""mha.attn"""   ,   """attention.self""")
  if "mha" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mha"""   ,   """attention""")
  if "W_q" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""W_q"""   ,   """self.query""")
  if "W_k" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""W_k"""   ,   """self.key""")
  if "W_v" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""W_v"""   ,   """self.value""")
  if "ff1" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""ff1"""   ,   """intermediate.dense""")
  if "ff2" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""ff2"""   ,   """output.dense""")
  if "ff" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""ff"""   ,   """output.dense""")
  if "mlm_class" in orig_key:
    __snake_case:		Union[str, Any]					    =		orig_key.replace("""mlm.mlm_class"""   ,   """cls.predictions.decoder""")
  if "mlm" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mlm"""   ,   """cls.predictions.transform""")
  if "cls" not in orig_key:
    __snake_case:		Dict					    =		"""nystromformer.""" + orig_key

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      __snake_case:		Tuple					    =		val

  __snake_case:		str					    =		orig_state_dict["""cls.predictions.decoder.bias"""]
  __snake_case:		int					    =		(
      torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
  )

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		convert_checkpoint_helper(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  model.load_state_dict(SCREAMING_SNAKE_CASE__)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

  print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''')


if __name__ == "__main__":
      __UpperCAmelCase			:  str										=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The json file for Nystromformer model config.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      __UpperCAmelCase			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






