





import argparse

import torch

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


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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	WavaVecaForSequenceClassification.from_pretrained(A_ ,config=A_							)
	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__(__snake_case ,__snake_case ,__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__              =	WavaVecaForAudioFrameClassification.from_pretrained(A_ ,config=A_							)
	lowerCamelCase__              =	downstream_dict['''model.linear.weight''']
	lowerCamelCase__              =	downstream_dict['''model.linear.bias''']
	return model





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	WavaVecaForXVector.from_pretrained(A_ ,config=A_							)
	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__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	torch.load(A_ ,map_location='''cpu'''							)

	lowerCamelCase__              =	checkpoint['''Downstream''']

	lowerCamelCase__              =	WavaVecaConfig.from_pretrained(A_							)
	lowerCamelCase__              =	WavaVecaFeatureExtractor.from_pretrained(
	    A_ ,return_attention_mask=A_ ,do_normalize=A_							)

	lowerCamelCase__              =	hf_config.architectures[0]
	if arch.endswith('''ForSequenceClassification'''							):
		lowerCamelCase__              =	convert_classification(A_ ,A_ ,A_							)
	elif arch.endswith('''ForAudioFrameClassification'''							):
		lowerCamelCase__              =	convert_diarization(A_ ,A_ ,A_							)
	elif arch.endswith('''ForXVector'''							):
		lowerCamelCase__              =	convert_xvector(A_ ,A_ ,A_							)
	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(A_							)
	hf_model.save_pretrained(A_							)


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










import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def lowerCAmelCase__(__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	def wrapper(*__snake_case ,**__snake_case							):
		lowerCamelCase__              =	timeit.default_timer()
		lowerCamelCase__              =	func(*__snake_case ,**__snake_case							)
		lowerCamelCase__              =	timeit.default_timer() - starttime
		return delta

	lowerCamelCase__              =	func.__name__

	return wrapper





def lowerCAmelCase__(__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	[]
	lowerCamelCase__              =	seq_shapes or {}
	for i in range(__snake_case							):
		lowerCamelCase__              =	{}
		for col_id, (k, v) in enumerate(features.items()							):
			if isinstance(__snake_case ,_ArrayXD							):
				lowerCamelCase__              =	np.random.rand(*v.shape							).astype(v.dtype							)
			elif isinstance(__snake_case ,datasets.Value							):
				if v.dtype == "string":
					lowerCamelCase__              =	'''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCamelCase__              =	np.random.randint(10 ,size=1							).astype(v.dtype							).item()
			elif isinstance(__snake_case ,datasets.Sequence							):
				while isinstance(__snake_case ,datasets.Sequence							):
					lowerCamelCase__              =	v.feature
				lowerCamelCase__              =	seq_shapes[k]
				lowerCamelCase__              =	np.random.rand(*__snake_case							).astype(v.dtype							)
			lowerCamelCase__              =	data

		dummy_data.append((i, example)							)

	return dummy_data





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   str:







	'''simple docstring'''







	lowerCamelCase__              =	generate_examples(__snake_case ,num_examples=__snake_case ,seq_shapes=__snake_case							)

	with ArrowWriter(features=__snake_case ,path=__snake_case							) as writer:
		for key, record in dummy_data:
			lowerCamelCase__              =	features.encode_example(__snake_case							)
			writer.write(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'							)

	lowerCamelCase__              =	datasets.Dataset.from_file(filename=__snake_case ,info=datasets.DatasetInfo(features=__snake_case							)							)

	return dataset



