




'''simple docstring'''






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = ['''UtteranceLevel''']

@torch.no_grad()
def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      Tuple									=       torch.load(snake_case__ ,				map_location="cpu"						)
			if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
						raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"						)

			__UpperCamelCase				:      Dict									=       checkpoint["Downstream"]

			__UpperCamelCase				:      Optional[Any]									=       HubertConfig.from_pretrained(snake_case__						)
			__UpperCamelCase				:      Tuple									=       HubertForSequenceClassification.from_pretrained(snake_case__ ,				config=snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       WavaVecaFeatureExtractor.from_pretrained(
			    snake_case__ ,				return_attention_mask=snake_case__ ,				do_normalize=snake_case__						)

			if hf_congfig.use_weighted_layer_sum:
						__UpperCamelCase				:      Dict									=       checkpoint["Featurizer"]["weights"]

			__UpperCamelCase				:      List[str]									=       downstream_dict["projector.weight"]
			__UpperCamelCase				:      Union[str, Any]									=       downstream_dict["projector.bias"]
			__UpperCamelCase				:      Any									=       downstream_dict["model.post_net.linear.weight"]
			__UpperCamelCase				:      str									=       downstream_dict["model.post_net.linear.bias"]

			hf_feature_extractor.save_pretrained(snake_case__						)
			hf_model.save_pretrained(snake_case__						)


if __name__ == "__main__":
				_lowerCAmelCase				     = 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				     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



