


'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCAmelCase   :Any								=    '''pcuenq/k-upscaler'''





def lowerCamelCase    (    lowerCAmelCase       :			Any	,	lowerCAmelCase       :			Dict	,	*, lowerCAmelCase       :			str	,	lowerCAmelCase       :			Tuple	):





	"""simple docstring"""







	__magic_name__ :					Tuple  			=  {
	    # 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 lowerCamelCase    (    lowerCAmelCase       :			str	,	*, lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Dict	):





	"""simple docstring"""







	__magic_name__ :					List[Any]  			=  checkpoint[f'{attention_prefix}.qkv_proj.weight'].chunk(3	,	dim=0	)
	__magic_name__ :					Any  			=  checkpoint[f'{attention_prefix}.qkv_proj.bias'].chunk(3	,	dim=0	)
	__magic_name__ :					List[Any]  			=  {
	    # 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 lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	*, lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			int	):





	"""simple docstring"""







	__magic_name__ :					Dict  			=  checkpoint[f'{attention_prefix}.kv_proj.weight'].chunk(2	,	dim=0	)
	__magic_name__ :					Any  			=  checkpoint[f'{attention_prefix}.kv_proj.bias'].chunk(2	,	dim=0	)

	__magic_name__ :					Any  			=  {
	    # 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 lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			Any	):





	"""simple docstring"""







	__magic_name__ :					List[str]  			=  'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
	__magic_name__ :					Union[str, Any]  			=  f'{block_prefix}.{block_idx}'

	__magic_name__ :					Dict  			=  {}

	if not hasattr(lowerCAmelCase	,	'attentions'	):
		__magic_name__ :					Optional[Any]  			=  1  # resnet only
	elif not block.attentions[0].add_self_attention:
		__magic_name__ :					Union[str, Any]  			=  2  # resnet -> cross-attention
	else:
		__magic_name__ :					Dict  			=  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}"
		__magic_name__ :					List[str]  			=  f'{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'
		__magic_name__ :					Any  			=  n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
		__magic_name__ :					Union[str, Any]  			=  f'{block_prefix}.{idx}' if block_type == 'up' else f'{block_prefix}.{idx}'

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

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

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

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

	return diffusers_checkpoint





def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			Any	):





	"""simple docstring"""







	__magic_name__ :					List[str]  			=  {}

	# 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(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	'down'	)	)

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks	):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	'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 lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	):





	"""simple docstring"""







	__magic_name__ :					List[str]  			=  original_config['input_channels'] + original_config['unet_cond_dim']
	__magic_name__ :					Dict  			=  original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

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

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

	__magic_name__ :					str  			=  original_config['mapping_cond_dim']
	__magic_name__ :					Tuple  			=  original_config['cross_cond_dim']

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

	__magic_name__ :					Dict  			=  UNetaDConditionModel(
	    in_channels=lowerCAmelCase	,	out_channels=lowerCAmelCase	,	down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')	,	mid_block_type=lowerCAmelCase	,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')	,	block_out_channels=lowerCAmelCase	,	layers_per_block=lowerCAmelCase	,	act_fn='gelu'	,	norm_num_groups=lowerCAmelCase	,	cross_attention_dim=lowerCAmelCase	,	attention_head_dim=64	,	time_cond_proj_dim=lowerCAmelCase	,	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 lowerCamelCase    (    lowerCAmelCase       :			List[str]	):





	"""simple docstring"""







	__magic_name__ :					str  			=  torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

	__magic_name__ :					Any  			=  huggingface_hub.hf_hub_download(lowerCAmelCase	,	'config_laion_text_cond_latent_upscaler_2.json'	)
	__magic_name__ :					List[str]  			=  huggingface_hub.hf_hub_download(
	    lowerCAmelCase	,	'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'	)
	__magic_name__ :					List[Any]  			=  K.config.load_config(open(lowerCAmelCase	)	)['model']
	__magic_name__ :					Optional[Any]  			=  unet_model_from_original_config(lowerCAmelCase	)

	__magic_name__ :					str  			=  torch.load(lowerCAmelCase	,	map_location=lowerCAmelCase	)['model_ema']
	__magic_name__ :					Dict  			=  unet_to_diffusers_checkpoint(lowerCAmelCase	,	lowerCAmelCase	)

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


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

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

							main(args)



'''simple docstring'''

import math





def lowerCamelCase    (    lowerCAmelCase       :			int	):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase	)
	if number < 1:
		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
		raise ValueError(lowerCAmelCase	)
	__magic_name__ :					Tuple  			=  [number]
	while number != 1:
		if number % 2 == 0:
			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
		else:
			__magic_name__ :					Union[str, Any]  			=  math.floor(
			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
		sequence.append(lowerCAmelCase	)
	return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()