


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A			:						str        =  logging.get_logger(__name__)

A			:						str        =  {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
A			:						List[str]        =  [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				int       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				str       ,       __magic_name__	:				Any	)  ->    int:
		"""simple docstring"""




		for attribute in key.split("""."""	):
				lowercase__	     =						getattr(__magic_name__       ,       __magic_name__	)

		if weight_type is not None:
				lowercase__	     =						getattr(__magic_name__       ,       __magic_name__	).shape
		else:
				lowercase__	     =						hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''	)

		if weight_type == "weight":
				lowercase__	     =						value
		elif weight_type == "weight_g":
				lowercase__	     =						value
		elif weight_type == "weight_v":
				lowercase__	     =						value
		elif weight_type == "bias":
				lowercase__	     =						value
		else:
				lowercase__	     =						value

		logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						[]
		lowercase__	     =						fairseq_model.state_dict()

		lowercase__	     =						hf_model.unispeech_sat.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase__	     =						False
				if "conv_layers" in name:
						load_conv_layer(
						    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       hf_model.config.feat_extract_norm == """group"""       ,       )
						lowercase__	     =						True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase__	     =						"""unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
										if "layer_norm_for_extract" in name and (".".join(name.split("""."""	)[:-1]	) != key):
												# special case since naming is very similar
												continue
										lowercase__	     =						True
										if "*" in mapped_key:
												lowercase__	     =						name.split(__magic_name__	)[0].split("""."""	)[-2]
												lowercase__	     =						mapped_key.replace("""*"""       ,       __magic_name__	)
										if "weight_g" in name:
												lowercase__	     =						"""weight_g"""
										elif "weight_v" in name:
												lowercase__	     =						"""weight_v"""
										elif "bias" in name:
												lowercase__	     =						"""bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowercase__	     =						"""weight"""
										else:
												lowercase__	     =						None
										set_recursively(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)
								continue
				if not is_used:
						unused_weights.append(__magic_name__	)

		logger.warning(f'''Unused weights: {unused_weights}'''	)






def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Any       ,       __magic_name__	:				str       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						full_name.split("""conv_layers."""	)[-1]
		lowercase__	     =						name.split("""."""	)
		lowercase__	     =						int(items[0]	)
		lowercase__	     =						int(items[1]	)

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
						lowercase__	     =						value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
						lowercase__	     =						value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''	)
						lowercase__	     =						value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''	)
						lowercase__	     =						value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
		else:
				unused_weights.append(__magic_name__	)






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				Any=None       ,       __magic_name__	:				Optional[int]=None       ,       __magic_name__	:				Optional[Any]=True	)  ->    List[Any]:
		"""simple docstring"""




		if config_path is not None:
				lowercase__	     =						UniSpeechSatConfig.from_pretrained(__magic_name__	)
		else:
				lowercase__	     =						UniSpeechSatConfig()

		lowercase__	     =						""""""

		if is_finetuned:
				lowercase__	     =						UniSpeechSatForCTC(__magic_name__	)
		else:
				lowercase__	     =						UniSpeechSatForPreTraining(__magic_name__	)

		lowercase__     ,      lowercase__     ,      lowercase__	     =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]       ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
		lowercase__	     =						model[0].eval()

		recursively_load_weights(__magic_name__       ,       __magic_name__	)

		hf_wavavec.save_pretrained(__magic_name__	)


if __name__ == "__main__":
	A			:						List[str]        =  argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
	parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	parser.add_argument(
	    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	A			:						Optional[int]        =  parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

		# 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:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.text_encoder
				else:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.unet

				# find the target layer
				lowercase__	     =						layer_infos.pop(0	)
				while len(__magic_name__	) > -1:
						try:
								lowercase__	     =						curr_layer.__getattr__(__magic_name__	)
								if len(__magic_name__	) > 0:
										lowercase__	     =						layer_infos.pop(0	)
								elif len(__magic_name__	) == 0:
										break
						except Exception:
								if len(__magic_name__	) > 0:
										temp_name += "_" + layer_infos.pop(0	)
								else:
										lowercase__	     =						layer_infos.pop(0	)

				lowercase__	     =						[]
				if "lora_down" in key:
						pair_keys.append(key.replace("""lora_down"""       ,       """lora_up"""	)	)
						pair_keys.append(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						pair_keys.append(key.replace("""lora_up"""       ,       """lora_down"""	)	)

				# update weight
				if len(state_dict[pair_keys[0]].shape	) == 4:
						lowercase__	     =						state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	).unsqueeze(2	).unsqueeze(3	)
				else:
						lowercase__	     =						state_dict[pair_keys[0]].to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	)

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

		return pipeline


if __name__ == "__main__":
	A			:						int        =  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.)')

	A			:						str        =  parser.parse_args()

	A			:						Tuple        =  args.base_model_path
	A			:						List[str]        =  args.checkpoint_path
	A			:						Optional[int]        =  args.dump_path
	A			:						Optional[int]        =  args.lora_prefix_unet
	A			:						Any        =  args.lora_prefix_text_encoder
	A			:						Any        =  args.alpha

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

