






"""simple docstring"""



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A_     =				'''pcuenq/k-upscaler'''




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Tuple,    *, UpperCAmelCase__							:       Optional[int],    UpperCAmelCase__							:       List[str]   )		->List[str]:
						A__    :	Optional[int]			=	{
						    # 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		(				UpperCAmelCase__							:       Optional[int],    *, UpperCAmelCase__							:       int,    UpperCAmelCase__							:       List[Any]   )		->int:
						A__	,	A__	,	A__    :	Any			=	checkpoint[f'{attention_prefix}.qkv_proj.weight'].chunk(3,    dim=0   )
						A__	,	A__	,	A__    :	Tuple			=	checkpoint[f'{attention_prefix}.qkv_proj.bias'].chunk(3,    dim=0   )
						A__    :	Optional[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		(				UpperCAmelCase__							:       Any,    *, UpperCAmelCase__							:       Optional[Any],    UpperCAmelCase__							:       Optional[Any],    UpperCAmelCase__							:       List[str]   )		->str:
						A__	,	A__    :	Any			=	checkpoint[f'{attention_prefix}.kv_proj.weight'].chunk(2,    dim=0   )
						A__	,	A__    :	List[Any]			=	checkpoint[f'{attention_prefix}.kv_proj.bias'].chunk(2,    dim=0   )

						A__    :	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		(				UpperCAmelCase__							:       Optional[Any],    UpperCAmelCase__							:       List[Any],    UpperCAmelCase__							:       Optional[int],    UpperCAmelCase__							:       Any   )		->str:
						A__    :	Tuple			=	"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
						A__    :	Optional[int]			=	f'{block_prefix}.{block_idx}'

						A__    :	List[Any]			=	{}

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

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

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

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

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

						return diffusers_checkpoint




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Optional[Any]   )		->int:
						A__    :	int			=	{}

						# 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(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    """down"""   )   )

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks   ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    """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		(				UpperCAmelCase__							:       Dict   )		->int:
						A__    :	List[str]			=	original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
						A__    :	Tuple			=	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__    :	Union[str, Any]			=	original_config["""depths"""][0]

						A__    :	Tuple			=	original_config["""mapping_cond_dim"""]
						A__    :	Optional[Any]			=	original_config["""cross_cond_dim"""]

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

						A__    :	List[str]			=	UNetaDConditionModel(
						    in_channels=UpperCAmelCase__,    out_channels=UpperCAmelCase__,    down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D"""),    mid_block_type=UpperCAmelCase__,    up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D"""),    block_out_channels=UpperCAmelCase__,    layers_per_block=UpperCAmelCase__,    act_fn="""gelu""",    norm_num_groups=UpperCAmelCase__,    cross_attention_dim=UpperCAmelCase__,    attention_head_dim=6_4,    time_cond_proj_dim=UpperCAmelCase__,    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		(				UpperCAmelCase__							:       int   )		->Optional[Any]:
						A__    :	Dict			=	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

						A__    :	int			=	huggingface_hub.hf_hub_download(UpperCAmelCase__,    """config_laion_text_cond_latent_upscaler_2.json"""   )
						A__    :	Union[str, Any]			=	huggingface_hub.hf_hub_download(
						    UpperCAmelCase__,    """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__    :	Optional[int]			=	K.config.load_config(open(UpperCAmelCase__   )   )["""model"""]
						A__    :	Any			=	unet_model_from_original_config(UpperCAmelCase__   )

						A__    :	Optional[int]			=	torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   )["""model_ema"""]
						A__    :	Any			=	unet_to_diffusers_checkpoint(UpperCAmelCase__,    UpperCAmelCase__   )

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


if __name__ == "__main__":
						A_     =				argparse.ArgumentParser()

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						A_     =				parser.parse_args()

						main(args)














"""simple docstring"""



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       List[dict],    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       GradScaler = None,    )		->Any:
						# Model states
						for i, state in enumerate(UpperCAmelCase__   ):
												A__    :	Optional[int]			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	int			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Model weights saved in {output_model_file}'   )
						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	str			=	opt.state_dict()
												A__    :	Any			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Optional[int]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Optimizer state saved in {output_optimizer_file}'   )
						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	scheduler.state_dict()
												A__    :	Union[str, Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Tuple			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Scheduler state saved in {output_scheduler_file}'   )
						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	scaler.state_dict()
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Gradient scaler state saved in {output_scaler_file}'   )
						# Random number generator states
						A__    :	str			=	{}
						A__    :	str			=	f'{RNG_STATE_NAME}_{process_index}.pkl'
						A__    :	Optional[Any]			=	random.getstate()
						A__    :	str			=	np.random.get_state()
						A__    :	List[str]			=	torch.get_rng_state()
						if is_xpu_available():
												A__    :	Optional[Any]			=	torch.xpu.get_rng_state_all()
						else:
												A__    :	str			=	torch.cuda.get_rng_state_all()
						if is_tpu_available():
												A__    :	Tuple			=	xm.get_rng_state()
						A__    :	str			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
						torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
						logger.info(f'Random states saved in {output_states_file}'   )
						return output_dir




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Optional[Any]=None,    UpperCAmelCase__							:       str=None,    **UpperCAmelCase__							:       Optional[int],    )		->List[str]:
						if map_location not in [None, "cpu", "on_device"]:
												raise TypeError(
												    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""   )
						if map_location is None:
												A__    :	Union[str, Any]			=	"""cpu"""
						elif map_location == "on_device":
												A__    :	Any			=	PartialState().device
						# Model states
						for i, model in enumerate(UpperCAmelCase__   ):
												A__    :	Any			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												models[i].load_state_dict(torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   ),    **UpperCAmelCase__   )
						logger.info("""All model weights loaded successfully"""   )

						# Optimizer states
						for i, opt in enumerate(UpperCAmelCase__   ):
												A__    :	Union[str, Any]			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Dict			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												A__    :	List[Any]			=	torch.load(UpperCAmelCase__,    map_location=UpperCAmelCase__   )
												optimizers[i].load_state_dict(UpperCAmelCase__   )
						logger.info("""All optimizer states loaded successfully"""   )

						# Scheduler states
						for i, scheduler in enumerate(UpperCAmelCase__   ):
												A__    :	List[Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scheduler.load_state_dict(torch.load(UpperCAmelCase__   )   )
						logger.info("""All scheduler states loaded successfully"""   )

						# GradScaler state
						if scaler is not None:
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scaler.load_state_dict(torch.load(UpperCAmelCase__   )   )
												logger.info("""GradScaler state loaded successfully"""   )

						# Random states
						try:
												A__    :	Any			=	torch.load(os.path.join(UpperCAmelCase__,    f'{RNG_STATE_NAME}_{process_index}.pkl'   )   )
												random.setstate(states["""random_state"""]   )
												np.random.set_state(states["""numpy_random_seed"""]   )
												torch.set_rng_state(states["""torch_manual_seed"""]   )
												if is_xpu_available():
																		torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]   )
												else:
																		torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]   )
												if is_tpu_available():
																		xm.set_rng_state(states["""xm_seed"""]   )
												logger.info("""All random states loaded successfully"""   )
						except Exception:
												logger.info("""Could not load random states"""   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       int = 0   )		->Dict:
						A__    :	Dict			=	Path(UpperCAmelCase__   ) / f'custom_checkpoint_{index}.pkl'
						logger.info(f'Saving the state of {get_pretty_name(UpperCAmelCase__   )} to {save_location}'   )
						torch.save(obj.state_dict(),    UpperCAmelCase__   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       str,    UpperCAmelCase__							:       int = 0   )		->str:
						A__    :	Optional[int]			=	f'{path}/custom_checkpoint_{index}.pkl'
						logger.info(f'Loading the state of {get_pretty_name(UpperCAmelCase__   )} from {load_location}'   )
						obj.load_state_dict(torch.load(UpperCAmelCase__,    map_location="""cpu"""   )   )






