
import argparse

import torch

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


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


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			WavaVecaForSequenceClassification.from_pretrained(a_		,       config=a_						)
				__A             =			downstream_dict["projector.weight"]
				__A             =			downstream_dict["projector.bias"]
				__A             =			downstream_dict["model.post_net.linear.weight"]
				__A             =			downstream_dict["model.post_net.linear.bias"]
				return model


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     int:







				"""simple docstring"""



				__A             =			WavaVecaForAudioFrameClassification.from_pretrained(a_		,       config=a_						)
				__A             =			downstream_dict["model.linear.weight"]
				__A             =			downstream_dict["model.linear.bias"]
				return model


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			WavaVecaForXVector.from_pretrained(a_		,       config=a_						)
				__A             =			downstream_dict["connector.weight"]
				__A             =			downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
								__A             =			downstream_dict[
								    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								__A             =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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


@torch.no_grad()
def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			torch.load(a_		,       map_location="cpu"						)

				__A             =			checkpoint["Downstream"]

				__A             =			WavaVecaConfig.from_pretrained(a_						)
				__A             =			WavaVecaFeatureExtractor.from_pretrained(
				    a_		,       return_attention_mask=a_		,       do_normalize=a_						)

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

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

				hf_feature_extractor.save_pretrained(a_						)
				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE							:List[str]										=    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							:Tuple										=    parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="automatic-speech-recognition"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"audio": Audio()}  )
	snake_case_    =					Features({"transcription": Value("string"  )}  )
	snake_case_    =					"audio"
	snake_case_    =					"transcription"






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					if self.audio_column not in features:
									raise ValueError(f'''Column {self.audio_column} is not present in features.'''			)
					if not isinstance(features[self.audio_column]							,A			):
									raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.input_schema.copy()
					__A             =			features[self.audio_column]
					__A             =			input_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			int			):
					return {self.audio_column: "audio", self.transcription_column: "transcription"}
