
'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def A				(__lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:Any		):
						# load base model
						_lowerCAmelCase  						=			StableDiffusionPipeline.from_pretrained(__lowerCamelCase			,     torch_dtype=torch.floataa		)

						# load LoRA weight from .safetensors
						_lowerCAmelCase  						=			load_file(__lowerCamelCase		)

						_lowerCAmelCase  						=			[]

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

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

												_lowerCAmelCase  						=			[]
												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:
																		_lowerCAmelCase  						=			state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
																		_lowerCAmelCase  						=			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:
																		_lowerCAmelCase  						=			state_dict[pair_keys[0]].to(torch.floataa		)
																		_lowerCAmelCase  						=			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__":
						_lowercase    =		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.)""")

						_lowercase    =		parser.parse_args()

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

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

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


'''simple docstring'''



from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)




@flax.struct.dataclass
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				jnp.ndarray






@flax_register_to_config
class   UpperCAmelCase_					(		nn.Module	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				int									=       3_2
						_lowercase		:				int									=       4
						_lowercase		:				int									=       4
						_lowercase		:				Tuple[str]									=       (
						    "CrossAttnDownBlock2D",
						    "CrossAttnDownBlock2D",
						    "CrossAttnDownBlock2D",
						    "DownBlock2D",
						)
						_lowercase		:				Tuple[str]									=       ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
						_lowercase		:				Union[bool, Tuple[bool]]									=       False
						_lowercase		:				Tuple[int]									=       (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
						_lowercase		:				int									=       2
						_lowercase		:				Union[int, Tuple[int]]									=       8
						_lowercase		:				Optional[Union[int, Tuple[int]]]									=       None
						_lowercase		:				int									=       1_2_8_0
						_lowercase		:				float									=       0.0
						_lowercase		:				bool									=       False
						_lowercase		:				jnp.dtype									=       jnp.floataa
						_lowercase		:				bool									=       True
						_lowercase		:				int									=       0
						_lowercase		:				bool									=       False




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			(1, self.in_channels, self.sample_size, self.sample_size)
												_lowerCAmelCase  						=			jnp.zeros(_lowercase   ,       dtype=jnp.floataa				)
												_lowerCAmelCase  						=			jnp.ones((1,)   ,       dtype=jnp.intaa				)
												_lowerCAmelCase  						=			jnp.zeros((1, 1, self.cross_attention_dim)   ,       dtype=jnp.floataa				)

												_lowerCAmelCase						,   _lowerCAmelCase  						=			jax.random.split(_lowercase				)
												_lowerCAmelCase  						=			{"""params""": params_rng, """dropout""": dropout_rng}

												return self.init(_lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				)["params"]




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.block_out_channels
												_lowerCAmelCase  						=			block_out_channels[0] * 4

												if self.num_attention_heads is not None:
																		raise ValueError(
																		    """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""				)

												# If `num_attention_heads` is not defined (which is the case for most models)
												# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
												# The reason for this behavior is to correct for incorrectly named variables that were introduced
												# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
												# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
												# which is why we correct for the naming here.
												_lowerCAmelCase  						=			self.num_attention_heads or self.attention_head_dim

												# input
												_lowerCAmelCase  						=			nn.Conv(
												    block_out_channels[0]   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )

												# time
												_lowerCAmelCase  						=			FlaxTimesteps(
												    block_out_channels[0]   ,       flip_sin_to_cos=self.flip_sin_to_cos   ,       freq_shift=self.config.freq_shift				)
												_lowerCAmelCase  						=			FlaxTimestepEmbedding(_lowercase   ,       dtype=self.dtype				)

												_lowerCAmelCase  						=			self.only_cross_attention
												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			(only_cross_attention,) * len(self.down_block_types				)

												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			(num_attention_heads,) * len(self.down_block_types				)

												# down
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			block_out_channels[0]
												for i, down_block_type in enumerate(self.down_block_types				):
																		_lowerCAmelCase  						=			output_channel
																		_lowerCAmelCase  						=			block_out_channels[i]
																		_lowerCAmelCase  						=			i == len(_lowercase				) - 1

																		if down_block_type == "CrossAttnDownBlock2D":
																								_lowerCAmelCase  						=			FlaxCrossAttnDownBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       dropout=self.dropout   ,       num_layers=self.layers_per_block   ,       num_attention_heads=num_attention_heads[i]   ,       add_downsample=not is_final_block   ,       use_linear_projection=self.use_linear_projection   ,       only_cross_attention=only_cross_attention[i]   ,       use_memory_efficient_attention=self.use_memory_efficient_attention   ,       dtype=self.dtype   ,       )
																		else:
																								_lowerCAmelCase  						=			FlaxDownBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       dropout=self.dropout   ,       num_layers=self.layers_per_block   ,       add_downsample=not is_final_block   ,       dtype=self.dtype   ,       )

																		down_blocks.append(_lowercase				)
												_lowerCAmelCase  						=			down_blocks

												# mid
												_lowerCAmelCase  						=			FlaxUNetMidBlockaDCrossAttn(
												    in_channels=block_out_channels[-1]   ,       dropout=self.dropout   ,       num_attention_heads=num_attention_heads[-1]   ,       use_linear_projection=self.use_linear_projection   ,       use_memory_efficient_attention=self.use_memory_efficient_attention   ,       dtype=self.dtype   ,       )

												# up
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			list(reversed(_lowercase				)				)
												_lowerCAmelCase  						=			list(reversed(_lowercase				)				)
												_lowerCAmelCase  						=			list(reversed(_lowercase				)				)
												_lowerCAmelCase  						=			reversed_block_out_channels[0]
												for i, up_block_type in enumerate(self.up_block_types				):
																		_lowerCAmelCase  						=			output_channel
																		_lowerCAmelCase  						=			reversed_block_out_channels[i]
																		_lowerCAmelCase  						=			reversed_block_out_channels[min(i + 1   ,       len(_lowercase				) - 1				)]

																		_lowerCAmelCase  						=			i == len(_lowercase				) - 1

																		if up_block_type == "CrossAttnUpBlock2D":
																								_lowerCAmelCase  						=			FlaxCrossAttnUpBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       prev_output_channel=_lowercase   ,       num_layers=self.layers_per_block + 1   ,       num_attention_heads=reversed_num_attention_heads[i]   ,       add_upsample=not is_final_block   ,       dropout=self.dropout   ,       use_linear_projection=self.use_linear_projection   ,       only_cross_attention=only_cross_attention[i]   ,       use_memory_efficient_attention=self.use_memory_efficient_attention   ,       dtype=self.dtype   ,       )
																		else:
																								_lowerCAmelCase  						=			FlaxUpBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       prev_output_channel=_lowercase   ,       num_layers=self.layers_per_block + 1   ,       add_upsample=not is_final_block   ,       dropout=self.dropout   ,       dtype=self.dtype   ,       )

																		up_blocks.append(_lowercase				)
																		_lowerCAmelCase  						=			output_channel
												_lowerCAmelCase  						=			up_blocks

												# out
												_lowerCAmelCase  						=			nn.GroupNorm(num_groups=32   ,       epsilon=1e-5				)
												_lowerCAmelCase  						=			nn.Conv(
												    self.out_channels   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )




						def __call__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase=None   ,       _lowercase=None   ,       _lowercase = True   ,       _lowercase = False   ,       ):

												"""simple docstring"""



												if not isinstance(_lowercase   ,       jnp.ndarray				):
																		_lowerCAmelCase  						=			jnp.array([timesteps]   ,       dtype=jnp.intaa				)
												elif isinstance(_lowercase   ,       jnp.ndarray				) and len(timesteps.shape				) == 0:
																		_lowerCAmelCase  						=			timesteps.astype(dtype=jnp.floataa				)
																		_lowerCAmelCase  						=			jnp.expand_dims(_lowercase   ,       0				)

												_lowerCAmelCase  						=			self.time_proj(_lowercase				)
												_lowerCAmelCase  						=			self.time_embedding(_lowercase				)

												# 2. pre-process
												_lowerCAmelCase  						=			jnp.transpose(_lowercase   ,       (0, 2, 3, 1)				)
												_lowerCAmelCase  						=			self.conv_in(_lowercase				)

												# 3. down
												_lowerCAmelCase  						=			(sample,)
												for down_block in self.down_blocks:
																		if isinstance(_lowercase   ,       _lowercase				):
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			down_block(_lowercase   ,       _lowercase   ,       _lowercase   ,       deterministic=not train				)
																		else:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			down_block(_lowercase   ,       _lowercase   ,       deterministic=not train				)
																		down_block_res_samples += res_samples

												if down_block_additional_residuals is not None:
																		_lowerCAmelCase  						=			()

																		for down_block_res_sample, down_block_additional_residual in zip(
																		    _lowercase   ,       _lowercase				):
																								down_block_res_sample += down_block_additional_residual
																								new_down_block_res_samples += (down_block_res_sample,)

																		_lowerCAmelCase  						=			new_down_block_res_samples

												# 4. mid
												_lowerCAmelCase  						=			self.mid_block(_lowercase   ,       _lowercase   ,       _lowercase   ,       deterministic=not train				)

												if mid_block_additional_residual is not None:
																		sample += mid_block_additional_residual

												# 5. up
												for up_block in self.up_blocks:
																		_lowerCAmelCase  						=			down_block_res_samples[-(self.layers_per_block + 1) :]
																		_lowerCAmelCase  						=			down_block_res_samples[: -(self.layers_per_block + 1)]
																		if isinstance(_lowercase   ,       _lowercase				):
																								_lowerCAmelCase  						=			up_block(
																								    _lowercase   ,       temb=_lowercase   ,       encoder_hidden_states=_lowercase   ,       res_hidden_states_tuple=_lowercase   ,       deterministic=not train   ,       )
																		else:
																								_lowerCAmelCase  						=			up_block(_lowercase   ,       temb=_lowercase   ,       res_hidden_states_tuple=_lowercase   ,       deterministic=not train				)

        # 6. post-process
												_lowerCAmelCase  						=			self.conv_norm_out(_lowercase				)
												_lowerCAmelCase  						=			nn.silu(_lowercase				)
												_lowerCAmelCase  						=			self.conv_out(_lowercase				)
												_lowerCAmelCase  						=			jnp.transpose(_lowercase   ,       (0, 3, 1, 2)				)

												if not return_dict:
																		return (sample,)

												return FlaxUNetaDConditionOutput(sample=_lowercase				)
