




"""simple docstring"""

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase  :			Dict        =       logging.get_logger(__name__)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		WavaVecaForSequenceClassification.from_pretrained(_UpperCAmelCase  ,      config=_UpperCAmelCase	)
	A_  :				int	   =		downstream_dict['''projector.weight''']
	A_  :				str	   =		downstream_dict['''projector.bias''']
	A_  :				List[Any]	   =		downstream_dict['''model.post_net.linear.weight''']
	A_  :				Dict	   =		downstream_dict['''model.post_net.linear.bias''']
	return model

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		WavaVecaForAudioFrameClassification.from_pretrained(_UpperCAmelCase  ,      config=_UpperCAmelCase	)
	A_  :				Optional[Any]	   =		downstream_dict['''model.linear.weight''']
	A_  :				int	   =		downstream_dict['''model.linear.bias''']
	return model

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Dict	   =		WavaVecaForXVector.from_pretrained(_UpperCAmelCase  ,      config=_UpperCAmelCase	)
	A_  :				Optional[Any]	   =		downstream_dict['''connector.weight''']
	A_  :				Dict	   =		downstream_dict['''connector.bias''']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
		A_  :				Optional[Any]	   =		downstream_dict[
		    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
		]
		A_  :				Union[str, Any]	   =		downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

	A_  :				Tuple	   =		downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
	A_  :				Tuple	   =		downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
	A_  :				Optional[int]	   =		downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
	A_  :				List[str]	   =		downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
	A_  :				int	   =		downstream_dict['''objective.W''']
	return model

@torch.no_grad()
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				List[Any]	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)

	A_  :				str	   =		checkpoint['''Downstream''']

	A_  :				Union[str, Any]	   =		WavaVecaConfig.from_pretrained(_UpperCAmelCase	)
	A_  :				Tuple	   =		WavaVecaFeatureExtractor.from_pretrained(
	    _UpperCAmelCase  ,      return_attention_mask=_UpperCAmelCase  ,      do_normalize=_UpperCAmelCase	)

	A_  :				int	   =		hf_config.architectures[0]
	if arch.endswith('''ForSequenceClassification'''	):
		A_  :				Dict	   =		convert_classification(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
	elif arch.endswith('''ForAudioFrameClassification'''	):
		A_  :				Dict	   =		convert_diarization(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
	elif arch.endswith('''ForXVector'''	):
		A_  :				Optional[Any]	   =		convert_xvector(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
	else:
		raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""	)

	if hf_config.use_weighted_layer_sum:
		A_  :				Optional[int]	   =		checkpoint['''Featurizer''']['''weights''']

	hf_feature_extractor.save_pretrained(_UpperCAmelCase	)
	hf_model.save_pretrained(_UpperCAmelCase	)


if __name__ == "__main__":
		_lowerCamelCase  :			Dict        =       argparse.ArgumentParser()
		parser.add_argument(
		    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
		)
		parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
		parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
		_lowerCamelCase  :			List[str]        =       parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











"""simple docstring"""

from queue import PriorityQueue
from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      ):




	"""simple docstring"""






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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		-1

	A_  :				Any	   =		set()
	A_  :				Tuple	   =		set()
	A_  :				Optional[int]	   =		{source: 0}
	A_  :				Dict	   =		{destination: 0}
	A_  :				Any	   =		{source: None}
	A_  :				Optional[Any]	   =		{destination: None}
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()

	A_  :				Union[str, Any]	   =		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():
		A_   , A_  :				Union[str, Any]	   =		queue_forward.get()
		visited_forward.add(_UpperCAmelCase	)

		A_   , A_  :				Optional[int]	   =		queue_backward.get()
		visited_backward.add(_UpperCAmelCase	)

		A_  :				Any	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

		A_  :				Dict	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

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

	if shortest_distance != np.inf:
		A_  :				Optional[Any]	   =		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  :			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()





