

'''simple docstring'''


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=False  )    ->    List[str]:
	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
		    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
		    ' instructions.'  )
		raise

	if not is_sharded:
		UpperCAmelCase_		:      List[Any]       	=					os.path.abspath(UpperCamelCase  )
		logger.info(f"""Loading PyTorch weights from {pt_path}"""  )

		UpperCAmelCase_		:      List[str]       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
		logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters."""  )

		UpperCAmelCase_		:      Any       	=					convert_pytorch_state_dict_to_flax(UpperCamelCase   ,UpperCamelCase  )
	else:
		# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
		UpperCAmelCase_		:      str       	=					convert_pytorch_sharded_state_dict_to_flax(UpperCamelCase   ,UpperCamelCase  )
	return flax_state_dict







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,)    ->    (Tuple[str], np.ndarray):

	def is_key_or_prefix_key_in_dict(UpperCamelCase  ) -> bool:
		return len(set(UpperCamelCase  ) & {key, (model_prefix,) + key}  ) > 0

	# layer norm
	UpperCAmelCase_		:      Tuple       	=					pt_tuple_key[:-1] + ('scale',)
	if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(UpperCamelCase  ):
		return renamed_pt_tuple_key, pt_tensor

	# batch norm layer mean
	UpperCAmelCase_		:      Dict       	=					pt_tuple_key[:-1] + ('mean',)
	if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(UpperCamelCase  ):
		return renamed_pt_tuple_key, pt_tensor

	# batch norm layer var
	UpperCAmelCase_		:      Union[str, Any]       	=					pt_tuple_key[:-1] + ('var',)
	if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(UpperCamelCase  ):
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	UpperCAmelCase_		:      List[str]       	=					pt_tuple_key[:-1] + ('embedding',)
	if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(UpperCamelCase  ):
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	UpperCAmelCase_		:      Union[str, Any]       	=					pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(UpperCamelCase  ):
		UpperCAmelCase_		:      List[str]       	=					pt_tensor.transpose(2   ,3   ,1   ,0  )
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	UpperCAmelCase_		:      Optional[int]       	=					pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(UpperCamelCase  ):
		UpperCAmelCase_		:      int       	=					pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	UpperCAmelCase_		:      List[str]       	=					pt_tuple_key[:-1] + ('weight',)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	UpperCAmelCase_		:      int       	=					pt_tuple_key[:-1] + ('bias',)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
	UpperCAmelCase_		:      List[Any]       	=					None
	if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
		UpperCAmelCase_		:      Optional[Any]       	=					pt_tuple_key[-2] + '_g'
	elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
		UpperCAmelCase_		:      str       	=					pt_tuple_key[-2] + '_v'
	if name is not None:
		UpperCAmelCase_		:      Optional[Any]       	=					pt_tuple_key[:-3] + (name,)
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	# convert pytorch tensor to numpy
	UpperCAmelCase_		:      str       	=					{k: v.numpy() for k, v in pt_state_dict.items()}

	UpperCAmelCase_		:      Tuple       	=					flax_model.base_model_prefix

	# use params dict if the model contains batch norm layers
	if "params" in flax_model.params:
		UpperCAmelCase_		:      str       	=					flax_model.params['params']
	else:
		UpperCAmelCase_		:      Union[str, Any]       	=					flax_model.params
	UpperCAmelCase_		:      int       	=					flatten_dict(UpperCamelCase  )

	# add batch_stats keys,values to dict
	if "batch_stats" in flax_model.params:
		UpperCAmelCase_		:      Tuple       	=					flatten_dict(flax_model.params['batch_stats']  )
		random_flax_state_dict.update(UpperCamelCase  )

	UpperCAmelCase_		:      Union[str, Any]       	=					{}

	UpperCAmelCase_		:      Dict       	=					(model_prefix not in flax_model_params) and (
	    model_prefix in {k.split('.'  )[0] for k in pt_state_dict.keys()}
	)
	UpperCAmelCase_		:      Any       	=					(model_prefix in flax_model_params) and (
	    model_prefix not in {k.split('.'  )[0] for k in pt_state_dict.keys()}
	)

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		UpperCAmelCase_		:      List[str]       	=					tuple(pt_key.split('.'  )  )

		# remove base model prefix if necessary
		UpperCAmelCase_		:      Optional[int]       	=					pt_tuple_key[0] == model_prefix
		if load_model_with_head_into_base_model and has_base_model_prefix:
			UpperCAmelCase_		:      Any       	=					pt_tuple_key[1:]

		# Correctly rename weight parameters
		UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					rename_key_and_reshape_tensor(
		    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

		# add model prefix if necessary
		UpperCAmelCase_		:      int       	=					(model_prefix,) + flax_key in random_flax_state_dict
		if load_base_model_into_model_with_head and require_base_model_prefix:
			UpperCAmelCase_		:      Optional[Any]       	=					(model_prefix,) + flax_key

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
				    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""  )

        # add batch stats if the model contains batchnorm layers
		if "batch_stats" in flax_model.params:
			if "mean" in flax_key[-1] or "var" in flax_key[-1]:
				UpperCAmelCase_		:      Any       	=					jnp.asarray(UpperCamelCase  )
				continue
			# remove num_batches_tracked key
			if "num_batches_tracked" in flax_key[-1]:
				flax_state_dict.pop(UpperCamelCase   ,UpperCamelCase  )
				continue

			# also add unexpected weight so that warning is thrown
			UpperCAmelCase_		:      str       	=					jnp.asarray(UpperCamelCase  )

		else:
			# also add unexpected weight so that warning is thrown
			UpperCAmelCase_		:      List[Any]       	=					jnp.asarray(UpperCamelCase  )

	return unflatten_dict(UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	import torch

	# Load the index
	UpperCAmelCase_		:      Union[str, Any]       	=					{}
	for shard_file in shard_filenames:
		# load using msgpack utils
		UpperCAmelCase_		:      Union[str, Any]       	=					torch.load(UpperCamelCase  )
		UpperCAmelCase_		:      List[str]       	=					{k: v.numpy() for k, v in pt_state_dict.items()}

		UpperCAmelCase_		:      List[Any]       	=					flax_model.base_model_prefix

		# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
		if "batch_stats" in flax_model.params:
			UpperCAmelCase_		:      Any       	=					flax_model.params['params']

			UpperCAmelCase_		:      List[str]       	=					flatten_dict(UpperCamelCase  )
			random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']  )  )
		else:
			UpperCAmelCase_		:      Optional[Any]       	=					flax_model.params
			UpperCAmelCase_		:      Dict       	=					flatten_dict(UpperCamelCase  )

		UpperCAmelCase_		:      Any       	=					(model_prefix not in flax_model_params) and (
		    model_prefix in {k.split('.'  )[0] for k in pt_state_dict.keys()}
		)
		UpperCAmelCase_		:      Optional[Any]       	=					(model_prefix in flax_model_params) and (
		    model_prefix not in {k.split('.'  )[0] for k in pt_state_dict.keys()}
		)
		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
			UpperCAmelCase_		:      Optional[Any]       	=					tuple(pt_key.split('.'  )  )

			# remove base model prefix if necessary
			UpperCAmelCase_		:      Union[str, Any]       	=					pt_tuple_key[0] == model_prefix
			if load_model_with_head_into_base_model and has_base_model_prefix:
				UpperCAmelCase_		:      List[str]       	=					pt_tuple_key[1:]

			# Correctly rename weight parameters
			UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					rename_key_and_reshape_tensor(
			    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
			# add model prefix if necessary
			UpperCAmelCase_		:      str       	=					(model_prefix,) + flax_key in random_flax_state_dict
			if load_base_model_into_model_with_head and require_base_model_prefix:
				UpperCAmelCase_		:      Optional[int]       	=					(model_prefix,) + flax_key

			if flax_key in random_flax_state_dict:
				if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
					raise ValueError(
					    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
					    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""  )

            # add batch stats if the model contains batchnorm layers
			if "batch_stats" in flax_model.params:
				if "mean" in flax_key[-1]:
					UpperCAmelCase_		:      str       	=					jnp.asarray(UpperCamelCase  )
					continue
				if "var" in flax_key[-1]:
					UpperCAmelCase_		:      Dict       	=					jnp.asarray(UpperCamelCase  )
					continue
				# remove num_batches_tracked key
				if "num_batches_tracked" in flax_key[-1]:
					flax_state_dict.pop(UpperCamelCase   ,UpperCamelCase  )
					continue

				# also add unexpected weight so that warning is thrown
				UpperCAmelCase_		:      Dict       	=					jnp.asarray(UpperCamelCase  )

			else:
				# also add unexpected weight so that warning is thrown
				UpperCAmelCase_		:      List[Any]       	=					jnp.asarray(UpperCamelCase  )
	return unflatten_dict(UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	UpperCAmelCase_		:      int       	=					os.path.abspath(UpperCamelCase  )
	logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""  )

	# import correct flax class
	UpperCAmelCase_		:      Union[str, Any]       	=					getattr(UpperCamelCase   ,'Flax' + model.__class__.__name__  )

	# load flax weight dict
	with open(UpperCamelCase   ,'rb'  ) as state_f:
		try:
			UpperCAmelCase_		:      int       	=					from_bytes(UpperCamelCase   ,state_f.read()  )
		except UnpicklingError:
			raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """  )

	return load_flax_weights_in_pytorch_model(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Any:

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
		    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
		    ' instructions.'  )
		raise

	# check if we have bf16 weights
	UpperCAmelCase_		:      List[str]       	=					flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase  : x.dtype == jnp.bfloataa   ,UpperCamelCase  )  ).values()
	if any(UpperCamelCase  ):
		# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
		# and bf16 is not fully supported in PT yet.
		logger.warning(
		    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
		    'before loading those in PyTorch model.'  )
		UpperCAmelCase_		:      Tuple       	=					jax.tree_util.tree_map(
		    lambda UpperCamelCase  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params   ,UpperCamelCase  )

	UpperCAmelCase_		:      Dict       	=					flatten_dict(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					pt_model.state_dict()

	UpperCAmelCase_		:      int       	=					(pt_model.base_model_prefix in flax_state) and (
	    pt_model.base_model_prefix not in {k.split('.'  )[0] for k in pt_model_dict.keys()}
	)
	UpperCAmelCase_		:      Optional[Any]       	=					(pt_model.base_model_prefix not in flax_state) and (
	    pt_model.base_model_prefix in {k.split('.'  )[0] for k in pt_model_dict.keys()}
	)

	# keep track of unexpected & missing keys
	UpperCAmelCase_		:      List[str]       	=					[]
	UpperCAmelCase_		:      Optional[int]       	=					set(pt_model_dict.keys()  )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		UpperCAmelCase_		:      Dict       	=					flax_key_tuple[0] == pt_model.base_model_prefix
		UpperCAmelCase_		:      Union[str, Any]       	=					'.'.join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

		# adapt flax_key to prepare for loading from/to base model only
		if load_model_with_head_into_base_model and has_base_model_prefix:
			UpperCAmelCase_		:      Union[str, Any]       	=					flax_key_tuple[1:]
		elif load_base_model_into_model_with_head and require_base_model_prefix:
			UpperCAmelCase_		:      Union[str, Any]       	=					(pt_model.base_model_prefix,) + flax_key_tuple

		# rename flax weights to PyTorch format
		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(UpperCamelCase  ) not in pt_model_dict:
			# conv layer
			UpperCAmelCase_		:      int       	=					flax_key_tuple[:-1] + ('weight',)
			UpperCAmelCase_		:      Optional[int]       	=					jnp.transpose(UpperCamelCase   ,(3, 2, 0, 1)  )
		elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase  ) not in pt_model_dict:
			# linear layer
			UpperCAmelCase_		:      List[str]       	=					flax_key_tuple[:-1] + ('weight',)
			UpperCAmelCase_		:      Dict       	=					flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
			UpperCAmelCase_		:      int       	=					flax_key_tuple[:-1] + ('weight',)

		# adding batch stats from flax batch norm to pt
		elif "mean" in flax_key_tuple[-1]:
			UpperCAmelCase_		:      int       	=					flax_key_tuple[:-1] + ('running_mean',)
		elif "var" in flax_key_tuple[-1]:
			UpperCAmelCase_		:      Optional[Any]       	=					flax_key_tuple[:-1] + ('running_var',)

		if "batch_stats" in flax_state:
			UpperCAmelCase_		:      str       	=					'.'.join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
		else:
			UpperCAmelCase_		:      Optional[Any]       	=					'.'.join(UpperCamelCase  )

		# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
		UpperCAmelCase_		:      Tuple       	=					{}
		# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
		for key in pt_model_dict:
			UpperCAmelCase_		:      List[Any]       	=					key.split('.'  )
			UpperCAmelCase_		:      Optional[int]       	=					None
			if key_components[-3::2] == ["parametrizations", "original0"]:
				UpperCAmelCase_		:      List[str]       	=					key_components[-2] + '_g'
			elif key_components[-3::2] == ["parametrizations", "original1"]:
				UpperCAmelCase_		:      Union[str, Any]       	=					key_components[-2] + '_v'
			if name is not None:
				UpperCAmelCase_		:      Optional[Any]       	=					key_components[:-3] + [name]
				UpperCAmelCase_		:      str       	=					'.'.join(UpperCamelCase  )
				UpperCAmelCase_		:      Optional[int]       	=					key

		if flax_key in special_pt_names:
			UpperCAmelCase_		:      List[Any]       	=					special_pt_names[flax_key]

		if flax_key in pt_model_dict:
			if flax_tensor.shape != pt_model_dict[flax_key].shape:
				raise ValueError(
				    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
				    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""  )
			else:
				# add weight to pytorch dict
				UpperCAmelCase_		:      Dict       	=					np.asarray(UpperCamelCase  ) if not isinstance(UpperCamelCase   ,np.ndarray  ) else flax_tensor
				UpperCAmelCase_		:      Any       	=					torch.from_numpy(UpperCamelCase  )
				# remove from missing keys
				missing_keys.remove(UpperCamelCase  )
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(UpperCamelCase  )

	pt_model.load_state_dict(UpperCamelCase  )

	# re-transform missing_keys to list
	UpperCAmelCase_		:      Tuple       	=					list(UpperCamelCase  )

	if len(UpperCamelCase  ) > 0:
		logger.warning(
		    'Some weights of the Flax model were not used when initializing the PyTorch model'
		    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
		    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
		    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
		    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
		    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
		    ' FlaxBertForSequenceClassification model).'  )
	else:
		logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""  )
	if len(UpperCamelCase  ) > 0:
		logger.warning(
		    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
		    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
		    ' use it for predictions and inference.'  )
	else:
		logger.warning(
		    f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
		    'If your task is similar to the task the model of the checkpoint was trained on, '
		    f"""you can already use {pt_model.__class__.__name__} for predictions without further training."""  )

	return pt_model









'''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)






