






"""simple docstring"""



import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_snake_case							:   Union[str, Any]	  =       logging.get_logger(__name__)




def     lowerCamelCase_					(	__A	:		List[Any]      ,						__A	:		List[str]      ,						__A	:		Dict      ):
					lowerCamelCase__					=WavLMForSequenceClassification.from_pretrained(_lowerCamelCase      ,						config=_lowerCamelCase      )
					lowerCamelCase__					=downstream_dict["projector.weight"]
					lowerCamelCase__					=downstream_dict["projector.bias"]
					lowerCamelCase__					=downstream_dict["model.post_net.linear.weight"]
					lowerCamelCase__					=downstream_dict["model.post_net.linear.bias"]
					return model




def     lowerCamelCase_					(	__A	:		Optional[int]      ,						__A	:		List[str]      ,						__A	:		List[str]      ):
					lowerCamelCase__					=WavLMForAudioFrameClassification.from_pretrained(_lowerCamelCase      ,						config=_lowerCamelCase      )
					lowerCamelCase__					=downstream_dict["model.linear.weight"]
					lowerCamelCase__					=downstream_dict["model.linear.bias"]
					return model




def     lowerCamelCase_					(	__A	:		List[str]      ,						__A	:		Tuple      ,						__A	:		int      ):
					lowerCamelCase__					=WavLMForXVector.from_pretrained(_lowerCamelCase      ,						config=_lowerCamelCase      )
					lowerCamelCase__					=downstream_dict["connector.weight"]
					lowerCamelCase__					=downstream_dict["connector.bias"]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
										lowerCamelCase__					=downstream_dict[
										    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
										]
										lowerCamelCase__					=downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

					lowerCamelCase__					=downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
					lowerCamelCase__					=downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
					lowerCamelCase__					=downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
					lowerCamelCase__					=downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
					lowerCamelCase__					=downstream_dict["objective.W"]
					return model




@torch.no_grad()
def     lowerCamelCase_					(	__A	:		int      ,						__A	:		Any      ,						__A	:		Optional[int]      ,						__A	:		Tuple      ):
					lowerCamelCase__					=torch.load(_lowerCamelCase      ,						map_location='cpu'      )

					lowerCamelCase__					=checkpoint["Downstream"]

					lowerCamelCase__					=WavLMConfig.from_pretrained(_lowerCamelCase      )
					lowerCamelCase__					=WavaVecaFeatureExtractor.from_pretrained(
					    _lowerCamelCase      ,						return_attention_mask=_lowerCamelCase      ,						do_normalize=_lowerCamelCase      )

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

					if hf_config.use_weighted_layer_sum:
										lowerCamelCase__					=checkpoint["Featurizer"]["weights"]

					hf_feature_extractor.save_pretrained(_lowerCamelCase      )
					hf_model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
						_snake_case							:   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.")
						_snake_case							:   Union[str, Any]	  =       parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class  _a  :






							"""simple docstring"""

							snake_case_							=    ("_front", "_back", "_len")



							@dataclass
							class  _a  :






														"""simple docstring"""

														snake_case_							=    None
														snake_case_							=    None
														snake_case_							=    None







							class  _a  :






														"""simple docstring"""

														snake_case_							=    ("_cur",)

														def __init__(   self					:  Tuple				,						a					:  Deque._Node | None     )						->None:
																				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       cur

														def __iter__(   self					:  Dict     )						->Deque._Iterator:
																				return self






														def   A_	(   self					:  int     )						->Any:
																				if self._cur is None:
																										# finished iterating
																										raise StopIteration
																				SCREAMING_SNAKE_CASE__  :					Tuple =       self._cur.val
																				SCREAMING_SNAKE_CASE__  :					int =       self._cur.next_node

																				return val







							def __init__(   self					:  str				,						a					:  Iterable[Any] | None = None     )						->None:
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					int =       0

													if iterable is not None:
																			# append every value to the deque
																			for val in iterable:
																									self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					int =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       node
																			SCREAMING_SNAKE_CASE__  :					Any =       self._back
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node  # assign new back to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  List[str]				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					str =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Any =       self._front
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node
																			SCREAMING_SNAKE_CASE__  :					int =       node  # assign new front to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  Dict				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.appendleft(a     )

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._back
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._back.prev_node  # set new back
													# drop the last node - python will deallocate memory automatically
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					Any =       self._front
													SCREAMING_SNAKE_CASE__  :					Dict =       self._front.next_node  # set new front and drop the first node
													SCREAMING_SNAKE_CASE__  :					Dict =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  str     )						->bool:
													return self._front is None

							def __len__(   self					:  List[str]     )						->int:
													return self._len

							def __eq__(   self					:  List[Any]				,						a					:  object     )						->bool:

													if not isinstance(a				,						a     ):
																			return NotImplemented

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._front
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       other._front

													# if the length of the dequeues are not the same, they are not equal
													if len(self     ) != len(a     ):
																			return False

													while me is not None and oth is not None:
																			# compare every value
																			if me.val != oth.val:
																									return False
																			SCREAMING_SNAKE_CASE__  :					Any =       me.next_node
																			SCREAMING_SNAKE_CASE__  :					Any =       oth.next_node

													return True

							def __iter__(   self					:  Any     )						->Deque._Iterator:
													return Deque._Iterator(self._front     )






							def __repr__(   self					:  Any     )						->str:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
													SCREAMING_SNAKE_CASE__  :					int =       self._front
													while aux is not None:
																			# append the values in a list to display
																			values_list.append(aux.val     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       aux.next_node

													return f"""[{', '.join(repr(a     ) for val in values_list     )}]"""


if __name__ == "__main__":
				import doctest

				doctest.testmod()