




"""simple docstring"""

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_lowerCamelCase	:      Tuple   =							'pcuenq/k-upscaler'

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   *, _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[str]						 =		{
	    # norm1
	    f"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[f"""{resnet_prefix}.main.0.mapper.weight"""],
	    f"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[f"""{resnet_prefix}.main.0.mapper.bias"""],
	    # conv1
	    f"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[f"""{resnet_prefix}.main.2.weight"""],
	    f"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[f"""{resnet_prefix}.main.2.bias"""],
	    # norm2
	    f"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[f"""{resnet_prefix}.main.4.mapper.weight"""],
	    f"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[f"""{resnet_prefix}.main.4.mapper.bias"""],
	    # conv2
	    f"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[f"""{resnet_prefix}.main.6.weight"""],
	    f"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[f"""{resnet_prefix}.main.6.bias"""],
	}

	if resnet.conv_shortcut is not None:
		rv.update(
		    {
		        f"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[f"""{resnet_prefix}.skip.weight"""],
		    }						)

	return rv

def 		lowercase_   (		_UpperCAmelCase	,   *, _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_ ,			A_ ,			A_	:					List[Any]						 =		checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3	,   dim=0						)
	A_ ,			A_ ,			A_	:					str						 =		checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3	,   dim=0						)
	A_	:					str						 =		{
	    # norm
	    f"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[f"""{attention_prefix}.norm_in.mapper.weight"""],
	    f"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[f"""{attention_prefix}.norm_in.mapper.bias"""],
	    # to_q
	    f"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1						).squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
	    # to_k
	    f"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1						).squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
	    # to_v
	    f"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1						).squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
	    # to_out
	    f"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[f"""{attention_prefix}.out_proj.weight"""]
	    .squeeze(-1						)
	    .squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[f"""{attention_prefix}.out_proj.bias"""],
	}

	return rv

def 		lowercase_   (		_UpperCAmelCase	,   *, _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_ ,			A_	:					List[str]						 =		checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2	,   dim=0						)
	A_ ,			A_	:					Optional[int]						 =		checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2	,   dim=0						)

	A_	:					Optional[int]						 =		{
	    # norm2 (ada groupnorm)
	    f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
	        f"""{attention_prefix}.norm_dec.mapper.weight"""
	    ],
	    f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
	        f"""{attention_prefix}.norm_dec.mapper.bias"""
	    ],
	    # layernorm on encoder_hidden_state
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
	        f"""{attention_prefix}.norm_enc.weight"""
	    ],
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
	        f"""{attention_prefix}.norm_enc.bias"""
	    ],
	    # to_q
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
	        f"""{attention_prefix}.q_proj.weight"""
	    ]
	    .squeeze(-1						)
	    .squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
	        f"""{attention_prefix}.q_proj.bias"""
	    ],
	    # to_k
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1						).squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
	    # to_v
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1						).squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
	    # to_out
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
	        f"""{attention_prefix}.out_proj.weight"""
	    ]
	    .squeeze(-1						)
	    .squeeze(-1						),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
	        f"""{attention_prefix}.out_proj.bias"""
	    ],
	}

	return rv

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					str						 =		'''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
	A_	:					Any						 =		f"""{block_prefix}.{block_idx}"""

	A_	:					Optional[int]						 =		{}

	if not hasattr(a_	,   '''attentions'''						):
		A_	:					int						 =		1  # resnet only
	elif not block.attentions[0].add_self_attention:
		A_	:					int						 =		2  # resnet -> cross-attention
	else:
		A_	:					Union[str, Any]						 =		3  # resnet -> self-attention -> cross-attention)

	for resnet_idx, resnet in enumerate(block.resnets						):
		# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
		A_	:					List[Any]						 =		f"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
		A_	:					List[str]						 =		n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
		A_	:					List[Any]						 =		f"""{block_prefix}.{idx}""" if block_type == '''up''' else f"""{block_prefix}.{idx}"""

		diffusers_checkpoint.update(
		    resnet_to_diffusers_checkpoint(
		        a_	,   a_	,   diffusers_resnet_prefix=a_	,   resnet_prefix=a_						)						)

	if hasattr(a_	,   '''attentions'''						):
		for attention_idx, attention in enumerate(block.attentions						):
			A_	:					str						 =		f"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
			A_	:					Dict						 =		n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
			A_	:					str						 =		f"""{block_prefix}.{idx}"""
			A_	:					Dict						 =		f"""{block_prefix}.{idx }"""
			A_	:					Tuple						 =		1 if not attention.add_self_attention else 2
			A_	:					str						 =		(
			    n * attention_idx + cross_attention_index
			    if block_type == '''up'''
			    else n * attention_idx + cross_attention_index + 1
			)
			A_	:					List[Any]						 =		f"""{block_prefix}.{idx }"""

			diffusers_checkpoint.update(
			    cross_attn_to_diffusers_checkpoint(
			        a_	,   diffusers_attention_prefix=a_	,   diffusers_attention_index=2	,   attention_prefix=a_	,   )						)

			if attention.add_self_attention is True:
				diffusers_checkpoint.update(
				    self_attn_to_diffusers_checkpoint(
				        a_	,   diffusers_attention_prefix=a_	,   attention_prefix=a_	,   )						)

	return diffusers_checkpoint

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Any						 =		{}

	# pre-processing
	diffusers_checkpoint.update(
	    {
	        '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
	        '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
	    }						)

	# timestep and class embedding
	diffusers_checkpoint.update(
	    {
	        '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1						),
	        '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
	        '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
	        '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
	        '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
	        '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
	    }						)

	# down_blocks
	for down_block_idx, down_block in enumerate(model.down_blocks						):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(a_	,   a_	,   a_	,   '''down'''						)						)

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks						):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(a_	,   a_	,   a_	,   '''up'''						)						)

	# post-processing
	diffusers_checkpoint.update(
	    {
	        '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
	        '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
	    }						)

	return diffusers_checkpoint

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Dict						 =		original_config['''input_channels'''] + original_config['''unet_cond_dim''']
	A_	:					Union[str, Any]						 =		original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

	A_	:					Optional[int]						 =		original_config['''channels''']

	assert (
	    len(set(original_config['''depths''']						)						) == 1
	), "UNet2DConditionModel currently do not support blocks with different number of layers"
	A_	:					Optional[Any]						 =		original_config['''depths'''][0]

	A_	:					Tuple						 =		original_config['''mapping_cond_dim''']
	A_	:					Dict						 =		original_config['''cross_cond_dim''']

	A_	:					str						 =		[]
	A_	:					List[Any]						 =		[]
	for s, c in zip(original_config['''self_attn_depths''']	,   original_config['''cross_attn_depths''']						):
		if s:
			A_	:					Tuple						 =		'''self'''
			A_	:					Optional[int]						 =		'''cross''' if c else None
		elif c:
			A_	:					Any						 =		'''cross'''
			A_	:					int						 =		None
		else:
			A_	:					Union[str, Any]						 =		None
			A_	:					Tuple						 =		None
		attna_types.append(a_						)
		attna_types.append(a_						)

	A_	:					str						 =		UNetaDConditionModel(
	    in_channels=a_	,   out_channels=a_	,   down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')	,   mid_block_type=a_	,   up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')	,   block_out_channels=a_	,   layers_per_block=a_	,   act_fn='''gelu'''	,   norm_num_groups=a_	,   cross_attention_dim=a_	,   attention_head_dim=64	,   time_cond_proj_dim=a_	,   resnet_time_scale_shift='''scale_shift'''	,   time_embedding_type='''fourier'''	,   timestep_post_act='''gelu'''	,   conv_in_kernel=1	,   conv_out_kernel=1	,   )

	return unet

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Any						 =		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''						)

	A_	:					Optional[int]						 =		huggingface_hub.hf_hub_download(a_	,   '''config_laion_text_cond_latent_upscaler_2.json'''						)
	A_	:					List[str]						 =		huggingface_hub.hf_hub_download(
	    a_	,   '''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''						)
	print(f"""loading original model configuration from {orig_config_path}"""						)
	print(f"""loading original model checkpoint from {orig_weights_path}"""						)

	print('''converting to diffusers unet'''						)
	A_	:					int						 =		K.config.load_config(open(a_						)						)['''model''']
	A_	:					List[str]						 =		unet_model_from_original_config(a_						)

	A_	:					Optional[int]						 =		torch.load(a_	,   map_location=a_						)['''model_ema''']
	A_	:					Optional[Any]						 =		unet_to_diffusers_checkpoint(a_	,   a_						)

	model.load_state_dict(a_	,   strict=a_						)
	model.save_pretrained(args.dump_path						)
	print(f"""saving converted unet model in {args.dump_path}"""						)


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

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
		_lowerCamelCase	:      List[str]   =							parser.parse_args()

		main(args)







"""simple docstring"""







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

          self.register_buffer("watermark_image"				,torch.zeros((62, 62, 4)		)		)
          _UpperCAmelCase 						=       None







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       images[0].height
          _UpperCAmelCase 						=       images[0].width

          _UpperCAmelCase 						=       sample_size or h

          _UpperCAmelCase 						=       min(h / sample_size				,w / sample_size		)
          _UpperCAmelCase						,				_UpperCAmelCase 						=       (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       1024**2, img_w * img_h
          _UpperCAmelCase 						=       (Sa / Sa) ** 0.5
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

          if self.watermark_image_as_pil is None:
             _UpperCAmelCase 						=       self.watermark_image.to(torch.uinta		).cpu().numpy()
             _UpperCAmelCase 						=       Image.fromarray(lowerCamelCase_				,mode="RGBA"		)
             _UpperCAmelCase 						=       watermark_image

          _UpperCAmelCase 						=       self.watermark_image_as_pil.resize(
              (wm_size, wm_size)				,PIL_INTERPOLATION["bicubic"]				,reducing_gap=lowerCamelCase_		)

          for pil_img in images:
             pil_img.paste(lowerCamelCase_				,box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,mask=wm_img.split()[-1]		)

          return images

