
from queue import PriorityQueue
from typing import Any

import numpy as np

def a__    (       A_,     A_,     A_,     A_,     A_,     A_,     A_,     A_,     A_,     ):






  '''simple docstring'''





  for nxt, d in graph[v]:
    if nxt in visited_forward:
      continue
    __magic_name__				     =       cst_fwd.get(A_,     np.inf					)
    __magic_name__				     =       cst_fwd[v] + d
    if new_cost_f < old_cost_f:
      queue.put((new_cost_f, nxt)					)
      __magic_name__				     =       new_cost_f
      __magic_name__				     =       v
    if nxt in visited_backward:
      if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
        __magic_name__				     =       cst_fwd[v] + d + cst_bwd[nxt]
  return shortest_distance

def a__    (       A_,     A_,     A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       -1

  __magic_name__				     =       set()
  __magic_name__				     =       set()
  __magic_name__				     =       {source: 0}
  __magic_name__				     =       {destination: 0}
  __magic_name__				     =       {source: None}
  __magic_name__				     =       {destination: None}
  __magic_name__				     =       PriorityQueue()
  __magic_name__				     =       PriorityQueue()

  __magic_name__				     =       np.inf

  queue_forward.put((0, source)					)
  queue_backward.put((0, destination)					)

  if source == destination:
    return 0

  while not queue_forward.empty() and not queue_backward.empty():
    __magic_name__     ,    __magic_name__				     =       queue_forward.get()
    visited_forward.add(A_					)

    __magic_name__     ,    __magic_name__				     =       queue_backward.get()
    visited_backward.add(A_					)

    __magic_name__				     =       pass_and_relaxation(
        A_,     A_,     A_,     A_,     A_,     A_,     A_,     A_,     A_,     )

    __magic_name__				     =       pass_and_relaxation(
        A_,     A_,     A_,     A_,     A_,     A_,     A_,     A_,     A_,     )

    if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
      break

  if shortest_distance != np.inf:
    __magic_name__				     =       shortest_distance
  return shortest_path_distance


__lowerCAmelCase			:     Optional[Any]           =   {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
__lowerCAmelCase			:     Optional[Any]           =   {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       [
      """decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(A_,     A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__     ,    __magic_name__				     =       emb.weight.shape
  __magic_name__				     =       nn.Linear(A_,     A_,     bias=A_					)
  __magic_name__				     =       emb.weight.data
  return lin_layer

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       torch.load(A_,     map_location="""cpu"""					)
  __magic_name__				     =       Namespace(**checkpoint["""cfg"""]["""model"""]					)
  __magic_name__				     =       checkpoint["""model"""]
  remove_ignore_keys_(A_					)
  __magic_name__				     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  __magic_name__				     =       {key.replace("""decoder""",     """model"""					): val for key, val in state_dict.items()}

  __magic_name__				     =       XGLMConfig(
      vocab_size=A_,     max_position_embeddings=args.max_target_positions,     num_layers=args.decoder_layers,     attention_heads=args.decoder_attention_heads,     ffn_dim=args.decoder_ffn_embed_dim,     d_model=args.decoder_embed_dim,     layerdrop=args.decoder_layerdrop,     dropout=args.dropout,     attention_dropout=args.attention_dropout,     activation_dropout=args.activation_dropout,     activation_function="""gelu""",     scale_embedding=not args.no_scale_embedding,     tie_word_embeddings=args.share_decoder_input_output_embed,     )

  __magic_name__				     =       XGLMForCausalLM(A_					)
  __magic_name__				     =       model.load_state_dict(A_,     strict=A_					)
  print(A_					)
  __magic_name__				     =       make_linear_from_emb(model.model.embed_tokens					)

  return model


if __name__ == "__main__":
    __lowerCAmelCase			:     Dict           =   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    __lowerCAmelCase			:     List[str]           =   parser.parse_args()
    __lowerCAmelCase			:     str           =   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)

