

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase__						=	["""UtteranceLevel"""]



@torch.no_grad()
def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							List[Any] )     -> Tuple:




				'''simple docstring'''





				A__            =     torch.load(SCREAMING_SNAKE_CASE_		,    map_location="cpu" )
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}' )

				A__            =     checkpoint["Downstream"]

				A__            =     HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
				A__            =     HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_		,    config=SCREAMING_SNAKE_CASE_ )
				A__            =     WavaVecaFeatureExtractor.from_pretrained(
				    SCREAMING_SNAKE_CASE_		,    return_attention_mask=SCREAMING_SNAKE_CASE_		,    do_normalize=SCREAMING_SNAKE_CASE_ )

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

				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"]

				hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_ )
				hf_model.save_pretrained(SCREAMING_SNAKE_CASE_ )


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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

