


import argparse

import torch

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


logging.set_verbosity_info()
_lowercase       :      Any	=	logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Optional[Any]						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__							,					config=UpperCamelCase__						)
	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   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     str							,					UpperCamelCase__:     Any						) ->   List[str]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Tuple						) ->   int:





	"""simple docstring"""







	A								=				WavaVecaForXVector.from_pretrained(UpperCamelCase__							,					config=UpperCamelCase__						)
	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   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)

	A								=				checkpoint["""Downstream"""]

	A								=				WavaVecaConfig.from_pretrained(UpperCamelCase__						)
	A								=				WavaVecaFeatureExtractor.from_pretrained(
	    UpperCamelCase__							,					return_attention_mask=UpperCamelCase__							,					do_normalize=UpperCamelCase__						)

	A								=				hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""						):
		A								=				convert_classification(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
	elif arch.endswith("""ForAudioFrameClassification"""						):
		A								=				convert_diarization(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
	elif arch.endswith("""ForXVector"""						):
		A								=				convert_xvector(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
	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(UpperCamelCase__						)
	hf_model.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
				_lowercase       :      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.")
				_lowercase       :      Tuple	=	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				(   UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







	def wrapper(*UpperCamelCase__:     Union[str, Any]							,					**UpperCamelCase__:     List[str]						):
		A								=				timeit.default_timer()
		A								=				func(*UpperCamelCase__							,					**UpperCamelCase__						)
		A								=				timeit.default_timer() - starttime
		return delta

	A								=				func.__name__

	return wrapper





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     int=None						) ->   Any:





	"""simple docstring"""







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

		dummy_data.append((i, example)						)

	return dummy_data





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     str=None						) ->   Optional[int]:





	"""simple docstring"""







	A								=				generate_examples(UpperCamelCase__							,					num_examples=UpperCamelCase__							,					seq_shapes=UpperCamelCase__						)

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

		A					,			A								=				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}.'						)

	A								=				datasets.Dataset.from_file(filename=UpperCamelCase__							,					info=datasets.DatasetInfo(features=UpperCamelCase__						)						)

	return dataset



