





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	if "model" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''model.''' ,''''''							)
	if "norm1" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''norm1''' ,'''attention.output.LayerNorm'''							)
	if "norm2" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''norm2''' ,'''output.LayerNorm'''							)
	if "norm" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''norm''' ,'''LayerNorm'''							)
	if "transformer" in orig_key:
		lowerCamelCase__              =	orig_key.split('''.'''							)[0].split('''_'''							)[-1]
		lowerCamelCase__              =	orig_key.replace(F'transformer_{layer_num}' ,F'encoder.layer.{layer_num}'							)
	if "mha.attn" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''mha.attn''' ,'''attention.self'''							)
	if "mha" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''mha''' ,'''attention'''							)
	if "W_q" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''W_q''' ,'''self.query'''							)
	if "W_k" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''W_k''' ,'''self.key'''							)
	if "W_v" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''W_v''' ,'''self.value'''							)
	if "ff1" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''ff1''' ,'''intermediate.dense'''							)
	if "ff2" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''ff2''' ,'''output.dense'''							)
	if "ff" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''ff''' ,'''output.dense'''							)
	if "mlm_class" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''mlm.mlm_class''' ,'''cls.predictions.decoder'''							)
	if "mlm" in orig_key:
		lowerCamelCase__              =	orig_key.replace('''mlm''' ,'''cls.predictions.transform'''							)
	if "cls" not in orig_key:
		lowerCamelCase__              =	'''yoso.''' + orig_key

	return orig_key





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	for key in orig_state_dict.copy().keys():
		lowerCamelCase__              =	orig_state_dict.pop(__snake_case							)

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			lowerCamelCase__              =	val

	lowerCamelCase__              =	orig_state_dict['''cls.predictions.decoder.bias''']
	lowerCamelCase__              =	torch.arange(__snake_case							).expand((1, -1)							) + 2

	return orig_state_dict





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	torch.load(__snake_case ,map_location='''cpu'''							)['''model_state_dict''']
	lowerCamelCase__              =	YosoConfig.from_json_file(__snake_case							)
	lowerCamelCase__              =	YosoForMaskedLM(__snake_case							)

	lowerCamelCase__              =	convert_checkpoint_helper(config.max_position_embeddings ,__snake_case							)

	print(model.load_state_dict(__snake_case							)							)
	model.eval()
	model.save_pretrained(__snake_case							)

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


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










from queue import PriorityQueue
from typing import Any

import numpy as np





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)       ->   float | int:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	-1

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

	lowerCamelCase__              =	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():
		lowerCamelCase__       ,			lowerCamelCase__              =	queue_forward.get()
		visited_forward.add(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	queue_backward.get()
		visited_backward.add(__snake_case							)

		lowerCamelCase__              =	pass_and_relaxation(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)

		lowerCamelCase__              =	pass_and_relaxation(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)

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

	if shortest_distance != np.inf:
		lowerCamelCase__              =	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()



