



import argparse

import torch

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


logging.set_verbosity_info()
_lowerCamelCase    :		Any		      =    logging.get_logger(__name__)

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Any:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				WavaVecaForSequenceClassification.from_pretrained(__lowerCAmelCase      ,	config=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				downstream_dict['projector.weight']
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				downstream_dict['projector.bias']
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				downstream_dict['model.post_net.linear.weight']
					SCREAMING_SNAKE_CASE							:     List[str]						=				downstream_dict['model.post_net.linear.bias']
					return model

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     int						=				WavaVecaForAudioFrameClassification.from_pretrained(__lowerCAmelCase      ,	config=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Dict						=				downstream_dict['model.linear.weight']
					SCREAMING_SNAKE_CASE							:     str						=				downstream_dict['model.linear.bias']
					return model

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				WavaVecaForXVector.from_pretrained(__lowerCAmelCase      ,	config=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Any						=				downstream_dict['connector.weight']
					SCREAMING_SNAKE_CASE							:     str						=				downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
										SCREAMING_SNAKE_CASE							:     int						=				downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										SCREAMING_SNAKE_CASE							:     str						=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

@torch.no_grad()
def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     str						=				torch.load(__lowerCAmelCase      ,	map_location='cpu'   )

					SCREAMING_SNAKE_CASE							:     Tuple						=				checkpoint['Downstream']

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				WavaVecaConfig.from_pretrained(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[Any]						=				WavaVecaFeatureExtractor.from_pretrained(
					    __lowerCAmelCase      ,	return_attention_mask=__lowerCAmelCase      ,	do_normalize=__lowerCAmelCase   )

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

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

					hf_feature_extractor.save_pretrained(__lowerCAmelCase   )
					hf_model.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
			_lowerCamelCase    :		Optional[int]		      =    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    :		str		      =    parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

										# find the target layer
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				layer_infos.pop(0   )
										while len(__lowerCAmelCase   ) > -1:
															try:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				curr_layer.__getattr__(__lowerCAmelCase   )
																				if len(__lowerCAmelCase   ) > 0:
																									SCREAMING_SNAKE_CASE							:     Optional[int]						=				layer_infos.pop(0   )
																				elif len(__lowerCAmelCase   ) == 0:
																									break
															except Exception:
																				if len(__lowerCAmelCase   ) > 0:
																									temp_name += "_" + layer_infos.pop(0   )
																				else:
																									SCREAMING_SNAKE_CASE							:     List[str]						=				layer_infos.pop(0   )

										SCREAMING_SNAKE_CASE							:     str						=				[]
										if "lora_down" in key:
															pair_keys.append(key.replace('lora_down'      ,	'lora_up'   )   )
															pair_keys.append(__lowerCAmelCase   )
										else:
															pair_keys.append(__lowerCAmelCase   )
															pair_keys.append(key.replace('lora_up'      ,	'lora_down'   )   )

										# update weight
										if len(state_dict[pair_keys[0]].shape   ) == 4:
															SCREAMING_SNAKE_CASE							:     Any						=				state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     str						=				state_dict[pair_keys[1]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   ).unsqueeze(2   ).unsqueeze(3   )
										else:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				state_dict[pair_keys[0]].to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				state_dict[pair_keys[1]].to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   )

										# update visited list
										for item in pair_keys:
															visited.append(__lowerCAmelCase   )

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    argparse.ArgumentParser()

			parser.add_argument(
			    """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
			)
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
			parser.add_argument(
			    """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
			)
			parser.add_argument(
			    """--lora_prefix_text_encoder""",
			    default="""lora_te""",
			    type=str,
			    help="""The prefix of text encoder weight in safetensors""",
			)
			parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
			parser.add_argument(
			    """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
			)
			parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)