

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Optional[int]	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						WavaVecaForSequenceClassification.from_pretrained(UpperCAmelCase_							,       config=UpperCAmelCase_	)
	_UpperCamelCase   :		Tuple			=						downstream_dict['projector.weight']
	_UpperCamelCase   :		int			=						downstream_dict['projector.bias']
	_UpperCamelCase   :		str			=						downstream_dict['model.post_net.linear.weight']
	_UpperCamelCase   :		Any			=						downstream_dict['model.post_net.linear.bias']
	return model






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str	)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						WavaVecaForAudioFrameClassification.from_pretrained(UpperCAmelCase_							,       config=UpperCAmelCase_	)
	_UpperCamelCase   :		Dict			=						downstream_dict['model.linear.weight']
	_UpperCamelCase   :		Optional[Any]			=						downstream_dict['model.linear.bias']
	return model






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						WavaVecaForXVector.from_pretrained(UpperCAmelCase_							,       config=UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						downstream_dict['connector.weight']
	_UpperCamelCase   :		Dict			=						downstream_dict['connector.bias']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
		_UpperCamelCase   :		Tuple			=						downstream_dict[
		    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
		]
		_UpperCamelCase   :		str			=						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

	_UpperCamelCase   :		str			=						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
	_UpperCamelCase   :		Optional[int]			=						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
	_UpperCamelCase   :		List[str]			=						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
	_UpperCamelCase   :		Any			=						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
	_UpperCamelCase   :		Dict			=						downstream_dict['objective.W']
	return model






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     int	)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)

	_UpperCamelCase   :		Tuple			=						checkpoint['Downstream']

	_UpperCamelCase   :		Tuple			=						WavaVecaConfig.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Dict			=						WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase_							,       return_attention_mask=UpperCAmelCase_							,       do_normalize=UpperCAmelCase_	)

	_UpperCamelCase   :		Dict			=						hf_config.architectures[0]
	if arch.endswith('ForSequenceClassification'	):
		_UpperCamelCase   :		Union[str, Any]			=						convert_classification(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	elif arch.endswith('ForAudioFrameClassification'	):
		_UpperCamelCase   :		List[Any]			=						convert_diarization(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	elif arch.endswith('ForXVector'	):
		_UpperCamelCase   :		Any			=						convert_xvector(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	else:
		raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''	)

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

	hf_feature_extractor.save_pretrained(UpperCAmelCase_	)
	hf_model.save_pretrained(UpperCAmelCase_	)


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__								=		{
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "rwkv"
		a__					       =  {"max_position_embeddings": "context_length"}


		def __init__(				self	,       __snake_case=5_02_77	,       __snake_case=10_24	,       __snake_case=40_96	,       __snake_case=32	,       __snake_case=None	,       __snake_case=None	,       __snake_case=1e-5	,       __snake_case=0	,       __snake_case=0	,       __snake_case=6	,       __snake_case=False	,       __snake_case=True	,       **__snake_case	,       ):
			_UpperCamelCase   :		str			=						vocab_size
			_UpperCamelCase   :		int			=						context_length
			_UpperCamelCase   :		Tuple			=						hidden_size
			_UpperCamelCase   :		Tuple			=						num_hidden_layers
			_UpperCamelCase   :		Dict			=						attention_hidden_size if attention_hidden_size is not None else hidden_size
			_UpperCamelCase   :		Tuple			=						intermediate_size if intermediate_size is not None else 4 * hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						layer_norm_epsilon
			_UpperCamelCase   :		Dict			=						rescale_every
			_UpperCamelCase   :		Optional[Any]			=						use_cache

			_UpperCamelCase   :		str			=						bos_token_id
			_UpperCamelCase   :		Optional[Any]			=						eos_token_id

			super().__init__(
			    tie_word_embeddings=__snake_case	,       bos_token_id=__snake_case	,       eos_token_id=__snake_case	,       **__snake_case)




