





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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

def      a    (   _lowercase :  Optional[Any]			,    _lowercase :  str			,    _lowercase :  str							)  ->Optional[Any]:
		UpperCamelCase__								=						WavLMForSequenceClassification.from_pretrained(_lowercase			,    config=_lowercase							)
		UpperCamelCase__								=						downstream_dict["""projector.weight"""]
		UpperCamelCase__								=						downstream_dict["""projector.bias"""]
		UpperCamelCase__								=						downstream_dict["""model.post_net.linear.weight"""]
		UpperCamelCase__								=						downstream_dict["""model.post_net.linear.bias"""]
		return model

def      a    (   _lowercase :  Any			,    _lowercase :  Optional[int]			,    _lowercase :  List[Any]							)  ->List[Any]:
		UpperCamelCase__								=						WavLMForAudioFrameClassification.from_pretrained(_lowercase			,    config=_lowercase							)
		UpperCamelCase__								=						downstream_dict["""model.linear.weight"""]
		UpperCamelCase__								=						downstream_dict["""model.linear.bias"""]
		return model

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

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

@torch.no_grad()
def      a    (   _lowercase :  Any			,    _lowercase :  int			,    _lowercase :  Dict			,    _lowercase :  Optional[int]							)  ->Optional[int]:
		UpperCamelCase__								=						torch.load(_lowercase			,    map_location="""cpu"""							)

		UpperCamelCase__								=						checkpoint["""Downstream"""]

		UpperCamelCase__								=						WavLMConfig.from_pretrained(_lowercase							)
		UpperCamelCase__								=						WavaVecaFeatureExtractor.from_pretrained(
		    _lowercase			,    return_attention_mask=_lowercase			,    do_normalize=_lowercase							)

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

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

		hf_feature_extractor.save_pretrained(_lowercase							)
		hf_model.save_pretrained(_lowercase							)


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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase 	=      logging.get_logger(__name__)

__lowerCAmelCase 	=      {
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "videomae"




					def __init__(    self     ,   __lowerCAmelCase=224     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=3     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=768     ,   __lowerCAmelCase=12     ,   __lowerCAmelCase=12     ,   __lowerCAmelCase=3_072     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.0     ,   __lowerCAmelCase=0.0     ,   __lowerCAmelCase=0.0_2     ,   __lowerCAmelCase=1E-1_2     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=6     ,   __lowerCAmelCase=384     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=1_536     ,   __lowerCAmelCase=True     ,   **__lowerCAmelCase     ,   )    ->	Any:
							super().__init__(**__lowerCAmelCase							)

							UpperCamelCase__								=						image_size
							UpperCamelCase__								=						patch_size
							UpperCamelCase__								=						num_channels
							UpperCamelCase__								=						num_frames
							UpperCamelCase__								=						tubelet_size

							UpperCamelCase__								=						hidden_size
							UpperCamelCase__								=						num_hidden_layers
							UpperCamelCase__								=						num_attention_heads
							UpperCamelCase__								=						intermediate_size
							UpperCamelCase__								=						hidden_act
							UpperCamelCase__								=						hidden_dropout_prob
							UpperCamelCase__								=						attention_probs_dropout_prob
							UpperCamelCase__								=						initializer_range
							UpperCamelCase__								=						layer_norm_eps
							UpperCamelCase__								=						qkv_bias
							UpperCamelCase__								=						use_mean_pooling

							UpperCamelCase__								=						decoder_num_attention_heads
							UpperCamelCase__								=						decoder_hidden_size
							UpperCamelCase__								=						decoder_num_hidden_layers
							UpperCamelCase__								=						decoder_intermediate_size
							UpperCamelCase__								=						norm_pix_loss





