

'''simple docstring'''


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}



class      lowercase						(  a_ ):
							_lowerCamelCase		:      Union[str, Any]=   "wavlm"


							def __init__(			self    ,			_snake_case=32    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=12    ,			_snake_case=3072    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case="group"    ,			_snake_case="gelu"    ,			_snake_case=(512, 512, 512, 512, 512, 512, 512)    ,			_snake_case=(5, 2, 2, 2, 2, 2, 2)    ,			_snake_case=(10, 3, 3, 3, 3, 2, 2)    ,			_snake_case=False    ,			_snake_case=128    ,			_snake_case=16    ,			_snake_case=320    ,			_snake_case=800    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=0.05    ,			_snake_case=10    ,			_snake_case=2    ,			_snake_case=0.0    ,			_snake_case=10    ,			_snake_case=320    ,			_snake_case=2    ,			_snake_case=0.1    ,			_snake_case=100    ,			_snake_case=256    ,			_snake_case=256    ,			_snake_case=0.1    ,			_snake_case="mean"    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=256    ,			_snake_case=(512, 512, 512, 512, 1500)    ,			_snake_case=(5, 3, 3, 1, 1)    ,			_snake_case=(1, 2, 3, 1, 1)    ,			_snake_case=512    ,			_snake_case=80    ,			_snake_case=0    ,			_snake_case=1    ,			_snake_case=2    ,			_snake_case=False    ,			_snake_case=3    ,			_snake_case=2    ,			_snake_case=3    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Dict:
								super().__init__(**_snake_case    ,			pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					hidden_size
								UpperCAmelCase_		:      int       	=					feat_extract_norm
								UpperCAmelCase_		:      int       	=					feat_extract_activation
								UpperCAmelCase_		:      Tuple       	=					list(_snake_case)
								UpperCAmelCase_		:      Any       	=					list(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					list(_snake_case)
								UpperCAmelCase_		:      Any       	=					conv_bias
								UpperCAmelCase_		:      Any       	=					num_buckets
								UpperCAmelCase_		:      Optional[int]       	=					max_bucket_distance
								UpperCAmelCase_		:      Any       	=					num_conv_pos_embeddings
								UpperCAmelCase_		:      List[str]       	=					num_conv_pos_embedding_groups
								UpperCAmelCase_		:      int       	=					len(self.conv_dim)
								UpperCAmelCase_		:      Dict       	=					num_hidden_layers
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      Optional[Any]       	=					num_attention_heads
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout
								UpperCAmelCase_		:      str       	=					attention_dropout
								UpperCAmelCase_		:      Dict       	=					activation_dropout
								UpperCAmelCase_		:      Tuple       	=					feat_proj_dropout
								UpperCAmelCase_		:      Any       	=					final_dropout
								UpperCAmelCase_		:      Tuple       	=					layerdrop
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Tuple       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					num_ctc_classes
								UpperCAmelCase_		:      int       	=					vocab_size
								UpperCAmelCase_		:      int       	=					do_stable_layer_norm
								UpperCAmelCase_		:      Any       	=					use_weighted_layer_sum
								UpperCAmelCase_		:      Any       	=					classifier_proj_size

								if (
								    (len(self.conv_stride) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel) != self.num_feat_extract_layers)
								    or (len(self.conv_dim) != self.num_feat_extract_layers)
								):
									raise ValueError(
									    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
									    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
									    F""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
									    F""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								UpperCAmelCase_		:      List[Any]       	=					apply_spec_augment
								UpperCAmelCase_		:      List[str]       	=					mask_time_prob
								UpperCAmelCase_		:      List[Any]       	=					mask_time_length
								UpperCAmelCase_		:      str       	=					mask_time_min_masks
								UpperCAmelCase_		:      Optional[int]       	=					mask_feature_prob
								UpperCAmelCase_		:      str       	=					mask_feature_length

								# parameters for pretraining with codevector quantized representations
								UpperCAmelCase_		:      Tuple       	=					num_codevectors_per_group
								UpperCAmelCase_		:      Optional[Any]       	=					num_codevector_groups
								UpperCAmelCase_		:      List[Any]       	=					contrastive_logits_temperature
								UpperCAmelCase_		:      Any       	=					num_negatives
								UpperCAmelCase_		:      int       	=					codevector_dim
								UpperCAmelCase_		:      str       	=					proj_codevector_dim
								UpperCAmelCase_		:      Any       	=					diversity_loss_weight

								# ctc loss
								UpperCAmelCase_		:      Any       	=					ctc_loss_reduction
								UpperCAmelCase_		:      List[str]       	=					ctc_zero_infinity

								# adapter
								UpperCAmelCase_		:      Optional[int]       	=					add_adapter
								UpperCAmelCase_		:      Optional[int]       	=					adapter_kernel_size
								UpperCAmelCase_		:      List[str]       	=					adapter_stride
								UpperCAmelCase_		:      Any       	=					num_adapter_layers
								UpperCAmelCase_		:      str       	=					output_hidden_size or hidden_size

								# SequenceClassification-specific parameter. Feel free to ignore for other classes.
								UpperCAmelCase_		:      Tuple       	=					classifier_proj_size

								# XVector-specific parameters. Feel free to ignore for other classes.
								UpperCAmelCase_		:      List[Any]       	=					list(_snake_case)
								UpperCAmelCase_		:      str       	=					list(_snake_case)
								UpperCAmelCase_		:      Any       	=					list(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					xvector_output_dim





							@property
							def 						_snake_case							(			self)							->				Union[str, Any]:
								return functools.reduce(operator.mul    ,			self.conv_stride    ,			1)









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

	# load LoRA weight from .safetensors
	UpperCAmelCase_		:      Union[str, Any]       	=					load_file(UpperCamelCase  )

	UpperCAmelCase_		:      Any       	=					[]

	# 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:
			UpperCAmelCase_		:      Optional[Any]       	=					key.split('.'  )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      str       	=					pipeline.text_encoder
		else:
			UpperCAmelCase_		:      List[str]       	=					key.split('.'  )[0].split(LORA_PREFIX_UNET + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      List[str]       	=					pipeline.unet

		# find the target layer
		UpperCAmelCase_		:      Dict       	=					layer_infos.pop(0  )
		while len(UpperCamelCase  ) > -1:
			try:
				UpperCAmelCase_		:      List[str]       	=					curr_layer.__getattr__(UpperCamelCase  )
				if len(UpperCamelCase  ) > 0:
					UpperCAmelCase_		:      Tuple       	=					layer_infos.pop(0  )
				elif len(UpperCamelCase  ) == 0:
					break
			except Exception:
				if len(UpperCamelCase  ) > 0:
					temp_name += "_" + layer_infos.pop(0  )
				else:
					UpperCAmelCase_		:      List[Any]       	=					layer_infos.pop(0  )

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

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

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

	return pipeline


if __name__ == "__main__":
						lowerCAmelCase__										= 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__										= parser.parse_args()

						lowerCAmelCase__										= args.base_model_path
						lowerCAmelCase__										= args.checkpoint_path
						lowerCAmelCase__										= args.dump_path
						lowerCAmelCase__										= args.lora_prefix_unet
						lowerCAmelCase__										= args.lora_prefix_text_encoder
						lowerCAmelCase__										= args.alpha

						lowerCAmelCase__										= convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						lowerCAmelCase__										= pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






