




'''simple docstring'''







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 						snake_case_							(_a	:							Dict    ,   _a	:							Tuple     ):
    # Load checkpoint
    UpperCAmelCase			       =		torch.load(_a    ,   map_location='''cpu'''     )

    UpperCAmelCase			       =		chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    UpperCAmelCase			       =		{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            UpperCAmelCase			       =		v
        else:
            UpperCAmelCase			       =		v

    UpperCAmelCase			       =		chkpt['''params''']
    UpperCAmelCase			       =		{n: v for n, v in config.items() if not isinstance(_a    ,   (torch.FloatTensor, numpy.ndarray)     )}

    UpperCAmelCase			       =		chkpt['''dico_word2id''']
    UpperCAmelCase			       =		{s + '''</w>''' if s.find('''@@'''     ) == -1 and i > 1_3 else s.replace('''@@'''    ,   ''''''     ): i for s, i in vocab.items()}

    # Save pytorch-model
    UpperCAmelCase			       =		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    UpperCAmelCase			       =		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    UpperCAmelCase			       =		pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(F"Save PyTorch model to {pytorch_weights_dump_path}"     )
    torch.save(_a    ,   _a     )

    print(F"Save configuration file to {pytorch_config_dump_path}"     )
    with open(_a    ,   '''w'''    ,   encoding='''utf-8'''     ) as f:
        f.write(json.dumps(_a    ,   indent=2     ) + '''\n'''     )

    print(F"Save vocab file to {pytorch_config_dump_path}"     )
    with open(_a    ,   '''w'''    ,   encoding='''utf-8'''     ) as f:
        f.write(json.dumps(_a    ,   indent=2     ) + '''\n'''     )


if __name__ == "__main__":
   A     =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   A     =parser.parse_args()
   convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np





def 						snake_case_							(_a	:							dict    ,   _a	:							str    ,   _a	:							set    ,   _a	:							set    ,   _a	:							dict    ,   _a	:							dict    ,   _a	:							PriorityQueue    ,   _a	:							dict    ,   _a	:							float | int    ,   ):
    for nxt, d in graph[v]:
        if nxt in visited_forward:
            continue
        UpperCAmelCase			       =		cst_fwd.get(_a    ,   np.inf     )
        UpperCAmelCase			       =		cst_fwd[v] + d
        if new_cost_f < old_cost_f:
            queue.put((new_cost_f, nxt)     )
            UpperCAmelCase			       =		new_cost_f
            UpperCAmelCase			       =		v
        if nxt in visited_backward:
            if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                UpperCAmelCase			       =		cst_fwd[v] + d + cst_bwd[nxt]
    return shortest_distance





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							dict    ,   _a	:							dict     ):
    UpperCAmelCase			       =		-1

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

    UpperCAmelCase			       =		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():
        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_forward.get()
        visited_forward.add(_a     )

        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_backward.get()
        visited_backward.add(_a     )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        UpperCAmelCase			       =		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:
        UpperCAmelCase			       =		shortest_distance
    return shortest_path_distance


A     ={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A     ={
    '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()




