



"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()
__snake_case							:     List[Any]   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[Any]:
			__lowerCAmelCase   :						Any				= UniSpeechSatForSequenceClassification.from_pretrained(__snake_case      ,config=__snake_case			)
			__lowerCAmelCase   :						str				= downstream_dict["projector.weight"]
			__lowerCAmelCase   :						str				= downstream_dict["projector.bias"]
			__lowerCAmelCase   :						Optional[int]				= downstream_dict["model.post_net.linear.weight"]
			__lowerCAmelCase   :						List[str]				= downstream_dict["model.post_net.linear.bias"]
			return model


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			__lowerCAmelCase   :						str				= UniSpeechSatForAudioFrameClassification.from_pretrained(__snake_case      ,config=__snake_case			)
			__lowerCAmelCase   :						Any				= downstream_dict["model.linear.weight"]
			__lowerCAmelCase   :						Optional[int]				= downstream_dict["model.linear.bias"]
			return model


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			__lowerCAmelCase   :						int				= UniSpeechSatForXVector.from_pretrained(__snake_case      ,config=__snake_case			)
			__lowerCAmelCase   :						Any				= downstream_dict["connector.weight"]
			__lowerCAmelCase   :						str				= downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
						__lowerCAmelCase   :						Tuple				= downstream_dict[
						    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
						]
						__lowerCAmelCase   :						Union[str, Any]				= downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

			__lowerCAmelCase   :						Tuple				= downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
			__lowerCAmelCase   :						Dict				= downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
			__lowerCAmelCase   :						Optional[int]				= downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
			__lowerCAmelCase   :						Dict				= downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
			__lowerCAmelCase   :						int				= downstream_dict["objective.W"]
			return model


@torch.no_grad()
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Tuple				= torch.load(__snake_case      ,map_location="cpu"			)

			__lowerCAmelCase   :						Tuple				= checkpoint["Downstream"]

			__lowerCAmelCase   :						Any				= UniSpeechSatConfig.from_pretrained(__snake_case			)
			__lowerCAmelCase   :						Dict				= WavaVecaFeatureExtractor.from_pretrained(
			    __snake_case      ,return_attention_mask=__snake_case      ,do_normalize=__snake_case			)

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

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

			hf_feature_extractor.save_pretrained(__snake_case			)
			hf_model.save_pretrained(__snake_case			)


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




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
			torch.save(model.state_dict()      ,__snake_case			)


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)