
"""simple docstring"""


import argparse

import torch

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


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






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     WavLMForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE  ,config=_SCREAMING_SNAKE_CASE	)
			lowerCamelCase_ 		=     downstream_dict['projector.weight']
			lowerCamelCase_ 		=     downstream_dict['projector.bias']
			lowerCamelCase_ 		=     downstream_dict['model.post_net.linear.weight']
			lowerCamelCase_ 		=     downstream_dict['model.post_net.linear.bias']
			return model






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     WavLMForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE  ,config=_SCREAMING_SNAKE_CASE	)
			lowerCamelCase_ 		=     downstream_dict['model.linear.weight']
			lowerCamelCase_ 		=     downstream_dict['model.linear.bias']
			return model






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     WavLMForXVector.from_pretrained(_SCREAMING_SNAKE_CASE  ,config=_SCREAMING_SNAKE_CASE	)
			lowerCamelCase_ 		=     downstream_dict['connector.weight']
			lowerCamelCase_ 		=     downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
						lowerCamelCase_ 		=     downstream_dict[
						    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						lowerCamelCase_ 		=     downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			lowerCamelCase_ 		=     downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			lowerCamelCase_ 		=     downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			lowerCamelCase_ 		=     downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			lowerCamelCase_ 		=     downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			lowerCamelCase_ 		=     downstream_dict['objective.W']
			return model






@torch.no_grad()
def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     torch.load(_SCREAMING_SNAKE_CASE  ,map_location='''cpu'''	)

			lowerCamelCase_ 		=     checkpoint['Downstream']

			lowerCamelCase_ 		=     WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCamelCase_ 		=     WavaVecaFeatureExtractor.from_pretrained(
			    _SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,do_normalize=_SCREAMING_SNAKE_CASE	)

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

			if hf_config.use_weighted_layer_sum:
						lowerCamelCase_ 		=     checkpoint['Featurizer']['weights']

			hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE	)
			hf_model.save_pretrained(_SCREAMING_SNAKE_CASE	)


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





"""simple docstring"""


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


A_						       =				logging.get_logger(__name__)

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




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Any									=						'swiftformer'




				def __init__(							self       ,	UpperCAmelCase=3       ,	UpperCAmelCase=[3, 3, 6, 4]       ,	UpperCAmelCase=[48, 56, 112, 220]       ,	UpperCAmelCase=4       ,	UpperCAmelCase=[True, True, True, True]       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=3       ,	UpperCAmelCase=2       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1e-5       ,	UpperCAmelCase=1e-5       ,	**UpperCAmelCase       ,	):
							super().__init__(**UpperCAmelCase      )
							lowerCamelCase_ 		=     num_channels
							lowerCamelCase_ 		=     depths
							lowerCamelCase_ 		=     embed_dims
							lowerCamelCase_ 		=     mlp_ratio
							lowerCamelCase_ 		=     downsamples
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     down_patch_size
							lowerCamelCase_ 		=     down_stride
							lowerCamelCase_ 		=     down_pad
							lowerCamelCase_ 		=     drop_path_rate
							lowerCamelCase_ 		=     use_layer_scale
							lowerCamelCase_ 		=     layer_scale_init_value
							lowerCamelCase_ 		=     batch_norm_eps




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			str									=						version.parse('1.11' )




				@property
				def 					UpperCAmelCase__	(							self      ):
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]      )





				@property
				def 					UpperCAmelCase__	(							self      ):
							return 1e-4



