

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	 =			logging.get_logger(__name__)

_lowerCamelCase	 =			{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     List[Any]          ="tvlt"






			def __init__(   self       ,_snake_case=2_24       ,_snake_case=20_48       ,_snake_case=1_28       ,_snake_case=[16, 16]       ,_snake_case=[16, 16]       ,_snake_case=3       ,_snake_case=1       ,_snake_case=8       ,_snake_case=7_68       ,_snake_case=12       ,_snake_case=12       ,_snake_case=30_72       ,_snake_case="gelu"       ,_snake_case=0.0       ,_snake_case=0.0       ,_snake_case=0.02       ,_snake_case=1E-6       ,_snake_case=True       ,_snake_case=False       ,_snake_case=16       ,_snake_case=5_12       ,_snake_case=8       ,_snake_case=20_48       ,_snake_case=0.75       ,_snake_case=0.15       ,_snake_case="frame-level"       ,_snake_case=True       ,_snake_case=True       ,_snake_case="classification"       ,**_snake_case       ,):
				super().__init__(**_snake_case			)

				if audio_mask_type not in ("frame-level", "patch_level"):
					raise ValueError(
					    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
					    f'''got {audio_mask_type}'''			)

				UpperCAmelCase_			:	List[str]      =   image_size
				UpperCAmelCase_			:	List[str]      =   spectrogram_length
				UpperCAmelCase_			:	Any      =   frequency_length
				UpperCAmelCase_			:	Optional[int]      =   image_patch_size
				UpperCAmelCase_			:	Tuple      =   audio_patch_size
				UpperCAmelCase_			:	Dict      =   num_image_channels
				UpperCAmelCase_			:	int      =   num_audio_channels
				UpperCAmelCase_			:	Any      =   num_frames

				UpperCAmelCase_			:	Tuple      =   hidden_size
				UpperCAmelCase_			:	str      =   num_hidden_layers
				UpperCAmelCase_			:	Union[str, Any]      =   num_attention_heads
				UpperCAmelCase_			:	List[str]      =   intermediate_size
				UpperCAmelCase_			:	List[str]      =   hidden_act
				UpperCAmelCase_			:	Any      =   hidden_dropout_prob
				UpperCAmelCase_			:	List[Any]      =   attention_probs_dropout_prob
				UpperCAmelCase_			:	Tuple      =   initializer_range
				UpperCAmelCase_			:	Optional[int]      =   layer_norm_eps
				UpperCAmelCase_			:	Optional[Any]      =   qkv_bias
				UpperCAmelCase_			:	Tuple      =   use_mean_pooling

				UpperCAmelCase_			:	List[Any]      =   decoder_num_attention_heads
				UpperCAmelCase_			:	List[str]      =   decoder_hidden_size
				UpperCAmelCase_			:	List[Any]      =   decoder_num_hidden_layers
				UpperCAmelCase_			:	Dict      =   decoder_intermediate_size
				UpperCAmelCase_			:	Optional[int]      =   pixel_mask_ratio
				UpperCAmelCase_			:	List[Any]      =   audio_mask_ratio
				UpperCAmelCase_			:	str      =   audio_mask_type

				UpperCAmelCase_			:	Optional[int]      =   task_matching
				UpperCAmelCase_			:	List[Any]      =   task_mae
				UpperCAmelCase_			:	Any      =   loss_type










import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


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

