


"""simple docstring"""
import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:    List[str]		=    logging.get_logger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							snake_case_           =		UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE				, config=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		downstream_dict["""projector.weight"""]
							snake_case_           =		downstream_dict["""projector.bias"""]
							snake_case_           =		downstream_dict["""model.post_net.linear.weight"""]
							snake_case_           =		downstream_dict["""model.post_net.linear.bias"""]
							return model







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE				, config=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		downstream_dict["""model.linear.weight"""]
							snake_case_           =		downstream_dict["""model.linear.bias"""]
							return model







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							snake_case_           =		UniSpeechSatForXVector.from_pretrained(_SCREAMING_SNAKE_CASE				, config=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		downstream_dict["""connector.weight"""]
							snake_case_           =		downstream_dict["""connector.bias"""]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
														snake_case_           =		downstream_dict[
														    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
														]
														snake_case_           =		downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		torch.load(_SCREAMING_SNAKE_CASE				, map_location="""cpu"""				)

							snake_case_           =		checkpoint["""Downstream"""]

							snake_case_           =		UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		WavaVecaFeatureExtractor.from_pretrained(
							    _SCREAMING_SNAKE_CASE				, return_attention_mask=_SCREAMING_SNAKE_CASE				, do_normalize=_SCREAMING_SNAKE_CASE				)

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

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

							hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE				)
							hf_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Tuple		=    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.')
				__SCREAMING_SNAKE_CASE							:    Any		=    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

from collections import deque





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      list[str]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		[]
														self.adlist.append(
														    {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}       )

														for keyword in keywords:
																					self.add_keyword(UpperCAmelCase_       )
														self.set_fail_transitions()



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      str       )					->int | None:



														"""simple docstring"""



														for state in self.adlist[current_state]["next_states"]:
																					if char == self.adlist[state]["value"]:
																												return state
														return None



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str       )					->None:



														"""simple docstring"""



														snake_case_           =		0
														for character in keyword:
																					snake_case_           =		self.find_next_state(UpperCAmelCase_		,       UpperCAmelCase_       )
																					if next_state is None:
																												self.adlist.append(
																												    {
																												        """value""": character,
																												        """next_states""": [],
																												        """fail_state""": 0,
																												        """output""": [],
																												    }       )
																												self.adlist[current_state]["next_states"].append(len(self.adlist       ) - 1       )
																												snake_case_           =		len(self.adlist       ) - 1
																					else:
																												snake_case_           =		next_state
														self.adlist[current_state]["output"].append(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->None:



														"""simple docstring"""



														snake_case_           =		deque()
														for node in self.adlist[0]["next_states"]:
																					q.append(UpperCAmelCase_       )
																					snake_case_           =		0
														while q:
																					snake_case_           =		q.popleft()
																					for child in self.adlist[r]["next_states"]:
																												q.append(UpperCAmelCase_       )
																												snake_case_           =		self.adlist[r]["""fail_state"""]
																												while (
																												    self.find_next_state(UpperCAmelCase_		,       self.adlist[child]["""value"""]       ) is None
																												    and state != 0
																												):
																																			snake_case_           =		self.adlist[state]["""fail_state"""]
																												snake_case_           =		self.find_next_state(
																												    UpperCAmelCase_		,       self.adlist[child]["""value"""]       )
																												if self.adlist[child]["fail_state"] is None:
																																			snake_case_           =		0
																												snake_case_           =		(
																												    self.adlist[child]["""output"""]
																												    + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
																												)



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str       )					->dict[str, list[int]]:



														"""simple docstring"""



														snake_case_           =		{}  # returns a dict with keywords and list of its occurrences
														snake_case_           =		0
														for i in range(len(UpperCAmelCase_       )       ):
																					while (
																					    self.find_next_state(UpperCAmelCase_		,       string[i]       ) is None
																					    and current_state != 0
																					):
																												snake_case_           =		self.adlist[current_state]["""fail_state"""]
																					snake_case_           =		self.find_next_state(UpperCAmelCase_		,       string[i]       )
																					if next_state is None:
																												snake_case_           =		0
																					else:
																												snake_case_           =		next_state
																												for key in self.adlist[current_state]["output"]:
																																			if key not in result:
																																										snake_case_           =		[]
																																			result[key].append(i - len(UpperCAmelCase_       ) + 1       )
														return result


if __name__ == "__main__":
				import doctest

				doctest.testmod()



