


'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase				:Union[str, Any]        =      [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCAmelCase				:List[Any]        =      [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCAmelCase				:List[Any]        =      []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														lowerCAmelCase				:Tuple        =      F'down_blocks.{i}.resnets.{j}.'
														lowerCAmelCase				:Union[str, Any]        =      F'input_blocks.{3*i + j + 1}.0.'
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					lowerCAmelCase				:Optional[int]        =      F'down_blocks.{i}.attentions.{j}.'
																					lowerCAmelCase				:Optional[int]        =      F'input_blocks.{3*i + j + 1}.1.'
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														lowerCAmelCase				:List[str]        =      F'up_blocks.{i}.resnets.{j}.'
														lowerCAmelCase				:Optional[int]        =      F'output_blocks.{3*i + j}.0.'
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					lowerCAmelCase				:Tuple        =      F'up_blocks.{i}.attentions.{j}.'
																					lowerCAmelCase				:Dict        =      F'output_blocks.{3*i + j}.1.'
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														lowerCAmelCase				:Optional[Any]        =      F'down_blocks.{i}.downsamplers.0.conv.'
														lowerCAmelCase				:Dict        =      F'input_blocks.{3*(i+1)}.0.op.'
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														lowerCAmelCase				:Union[str, Any]        =      F'up_blocks.{i}.upsamplers.0.'
														lowerCAmelCase				:Optional[int]        =      F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase				:Any        =      '''mid_block.attentions.0.'''
lowerCAmelCase				:List[Any]        =      '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							lowerCAmelCase				:Optional[int]        =      F'mid_block.resnets.{j}.'
							lowerCAmelCase				:int        =      F'middle_block.{2*j}.'
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def lowerCamelCase      (      lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Optional[Any]					     =					{k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		__magic_name__	:   List[Any]					     =					sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				__magic_name__	:   List[str]					     =					v.replace(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   Optional[Any]					     =					v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			__magic_name__	:   Dict					     =					v.replace(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   List[Any]					     =					v
	__magic_name__	:   List[str]					     =					{v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase				:int        =      [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														lowerCAmelCase				:Tuple        =      F'encoder.down_blocks.{i}.resnets.{j}.'
														lowerCAmelCase				:str        =      F'encoder.down.{i}.block.{j}.'
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														lowerCAmelCase				:Dict        =      F'down_blocks.{i}.downsamplers.0.'
														lowerCAmelCase				:Optional[int]        =      F'down.{i}.downsample.'
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														lowerCAmelCase				:Tuple        =      F'up_blocks.{i}.upsamplers.0.'
														lowerCAmelCase				:Dict        =      F'up.{3-i}.upsample.'
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														lowerCAmelCase				:str        =      F'decoder.up_blocks.{i}.resnets.{j}.'
														lowerCAmelCase				:Union[str, Any]        =      F'decoder.up.{3-i}.block.{j}.'
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							lowerCAmelCase				:List[str]        =      F'mid_block.resnets.{i}.'
							lowerCAmelCase				:int        =      F'mid.block_{i+1}.'
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase				:Union[str, Any]        =      [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	return w.reshape(*w.shape		,		1		,		1		)





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					{k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			__magic_name__	:   int					     =					v.replace(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Dict					     =					v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				__magic_name__	:   Any					     =					v.replace(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   Any					     =					v
	__magic_name__	:   Any					     =					{v: vae_state_dict[k] for k, v in mapping.items()}
	__magic_name__	:   Dict					     =					['q', 'k', 'v', 'proj_out']
	for k, v in new_state_dict.items():
		for weight_name in weights_to_convert:
			if f'mid.attn_1.{weight_name}.weight' in k:
				print(f'Reshaping {k} for SD format'		)
				__magic_name__	:   int					     =					reshape_weight_for_sd(lowerCAmelCase		)
	return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase				:Tuple        =      [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCAmelCase				:int        =      {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase				:int        =      re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase				:str        =      {'''q''': 0, '''k''': 1, '''v''': 2}





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					{}
	__magic_name__	:   Tuple					     =					{}
	__magic_name__	:   List[Any]					     =					{}
	for k, v in text_enc_dict.items():
		if (
		    k.endswith('.self_attn.q_proj.weight'		)
		    or k.endswith('.self_attn.k_proj.weight'		)
		    or k.endswith('.self_attn.v_proj.weight'		)
		):
			__magic_name__	:   Any					     =					k[: -len('.q_proj.weight'		)]
			__magic_name__	:   Optional[int]					     =					k[-len('q_proj.weight'		)]
			if k_pre not in capture_qkv_weight:
				__magic_name__	:   int					     =					[None, None, None]
			__magic_name__	:   int					     =					v
			continue

		if (
		    k.endswith('.self_attn.q_proj.bias'		)
		    or k.endswith('.self_attn.k_proj.bias'		)
		    or k.endswith('.self_attn.v_proj.bias'		)
		):
			__magic_name__	:   List[str]					     =					k[: -len('.q_proj.bias'		)]
			__magic_name__	:   int					     =					k[-len('q_proj.bias'		)]
			if k_pre not in capture_qkv_bias:
				__magic_name__	:   int					     =					[None, None, None]
			__magic_name__	:   Optional[int]					     =					v
			continue

		__magic_name__	:   str					     =					textenc_pattern.sub(lambda lowerCAmelCase		: protected[re.escape(m.group(0		)		)]		,		lowerCAmelCase		)
		__magic_name__	:   Dict					     =					v

	for k_pre, tensors in capture_qkv_weight.items():
		if None in tensors:
			raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'		)
		__magic_name__	:   Dict					     =					textenc_pattern.sub(lambda lowerCAmelCase		: protected[re.escape(m.group(0		)		)]		,		lowerCAmelCase		)
		__magic_name__	:   Tuple					     =					torch.cat(lowerCAmelCase		)

	for k_pre, tensors in capture_qkv_bias.items():
		if None in tensors:
			raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'		)
		__magic_name__	:   Tuple					     =					textenc_pattern.sub(lambda lowerCAmelCase		: protected[re.escape(m.group(0		)		)]		,		lowerCAmelCase		)
		__magic_name__	:   Any					     =					torch.cat(lowerCAmelCase		)

	return new_state_dict





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	return text_enc_dict


if __name__ == "__main__":
							lowerCAmelCase				:Optional[Any]        =      argparse.ArgumentParser()

							parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
							parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
							parser.add_argument(
							    '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
							)

							lowerCAmelCase				:Optional[int]        =      parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							lowerCAmelCase				:List[Any]        =      osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
							lowerCAmelCase				:Union[str, Any]        =      osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
							lowerCAmelCase				:str        =      osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														lowerCAmelCase				:Optional[Any]        =      load_file(unet_path, device='''cpu''')
							else:
														lowerCAmelCase				:int        =      osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
														lowerCAmelCase				:Any        =      torch.load(unet_path, map_location='''cpu''')

							if osp.exists(vae_path):
														lowerCAmelCase				:Any        =      load_file(vae_path, device='''cpu''')
							else:
														lowerCAmelCase				:List[Any]        =      osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
														lowerCAmelCase				:str        =      torch.load(vae_path, map_location='''cpu''')

							if osp.exists(text_enc_path):
														lowerCAmelCase				:Union[str, Any]        =      load_file(text_enc_path, device='''cpu''')
							else:
														lowerCAmelCase				:List[Any]        =      osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
														lowerCAmelCase				:str        =      torch.load(text_enc_path, map_location='''cpu''')

							# Convert the UNet model
							lowerCAmelCase				:Dict        =      convert_unet_state_dict(unet_state_dict)
							lowerCAmelCase				:Optional[int]        =      {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							lowerCAmelCase				:int        =      convert_vae_state_dict(vae_state_dict)
							lowerCAmelCase				:Dict        =      {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							lowerCAmelCase				:Dict        =      '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														lowerCAmelCase				:Optional[int]        =      {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
														lowerCAmelCase				:List[str]        =      convert_text_enc_state_dict_vaa(text_enc_dict)
														lowerCAmelCase				:Dict        =      {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
							else:
														lowerCAmelCase				:List[Any]        =      convert_text_enc_state_dict(text_enc_dict)
														lowerCAmelCase				:Dict        =      {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							lowerCAmelCase				:Dict        =      {**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														lowerCAmelCase				:str        =      {k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														lowerCAmelCase				:Any        =      {'''state_dict''': state_dict}
														torch.save(state_dict, args.checkpoint_path)



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					UNetaDModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					self.dummy_uncond_unet
			__magic_name__	:   str					     =					KarrasVeScheduler()

			__magic_name__	:   List[Any]					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   int					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   str					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2







@slow
@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					'google/ncsnpp-celebahq-256'
			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   int					     =					KarrasVeScheduler()

			__magic_name__	:   str					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe(num_inference_steps=20  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   List[str]					     =					image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			__magic_name__	:   int					     =					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2