



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase_  =   '''pcuenq/k-upscaler'''



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     *, SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__         = {
			    # 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       (   SCREAMING_SNAKE_CASE_  ,     *, SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__     ,				lowercase__     ,				lowercase__         = checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3  ,     dim=0     )
			lowercase__     ,				lowercase__     ,				lowercase__         = checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3  ,     dim=0     )
			lowercase__         = {
			    # 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       (   SCREAMING_SNAKE_CASE_  ,     *, SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__     ,				lowercase__         = checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2  ,     dim=0     )
			lowercase__     ,				lowercase__         = checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2  ,     dim=0     )

			lowercase__         = {
			    # 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       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__         = "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
			lowercase__         = f'''{block_prefix}.{block_idx}'''

			lowercase__         = {}

			if not hasattr(_UpperCAmelCase  ,     "attentions"     ):
						lowercase__         = 1  # resnet only
			elif not block.attentions[0].add_self_attention:
						lowercase__         = 2  # resnet -> cross-attention
			else:
						lowercase__         = 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}"
						lowercase__         = f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
						lowercase__         = n * resnet_idx if block_type == "up" else n * resnet_idx + 1
						lowercase__         = 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     ):
									lowercase__         = f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
									lowercase__         = n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
									lowercase__         = f'''{block_prefix}.{idx}'''
									lowercase__         = f'''{block_prefix}.{idx }'''
									lowercase__         = 1 if not attention.add_self_attention else 2
									lowercase__         = (
									    n * attention_idx + cross_attention_index
									    if block_type == "up"
									    else n * attention_idx + cross_attention_index + 1
									)
									lowercase__         = 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       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__         = {}

			# 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       (   SCREAMING_SNAKE_CASE_     ):
			lowercase__         = original_config["input_channels"] + original_config["unet_cond_dim"]
			lowercase__         = original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

			lowercase__         = original_config["channels"]

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

			lowercase__         = original_config["mapping_cond_dim"]
			lowercase__         = original_config["cross_cond_dim"]

			lowercase__         = []
			lowercase__         = []
			for s, c in zip(original_config["self_attn_depths"]  ,     original_config["cross_attn_depths"]     ):
						if s:
									lowercase__         = "self"
									lowercase__         = "cross" if c else None
						elif c:
									lowercase__         = "cross"
									lowercase__         = None
						else:
									lowercase__         = None
									lowercase__         = None
						attna_types.append(_UpperCAmelCase     )
						attna_types.append(_UpperCAmelCase     )

			lowercase__         = 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=64  ,     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       (   SCREAMING_SNAKE_CASE_     ):
			lowercase__         = torch.device("cuda" if torch.cuda.is_available() else "cpu"     )

			lowercase__         = huggingface_hub.hf_hub_download(_UpperCAmelCase  ,     "config_laion_text_cond_latent_upscaler_2.json"     )
			lowercase__         = 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"     )
			lowercase__         = K.config.load_config(open(_UpperCAmelCase     )     )["model"]
			lowercase__         = unet_model_from_original_config(_UpperCAmelCase     )

			lowercase__         = torch.load(_UpperCAmelCase  ,     map_location=_UpperCAmelCase     )["model_ema"]
			lowercase__         = 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__":
							lowercase_  =   argparse.ArgumentParser()

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

							main(args)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 					__A   (  a			):
				__A            = ["""pixel_values"""]


				def __init__(						self      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = 1 / 255      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase		       =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)

								lowerCamelCase		       =do_resize
								lowerCamelCase		       =size
								lowerCamelCase		       =resample
								lowerCamelCase		       =do_center_crop
								lowerCamelCase		       =crop_size
								lowerCamelCase		       =do_rescale
								lowerCamelCase		       =rescale_factor
								lowerCamelCase		       =do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase		       =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase		       =do_convert_rgb


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =get_resize_output_image_size(
								    UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								return center_crop(UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return rescale(UpperCAmelCase_      ,       scale=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return normalize(UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = ChannelDimension.FIRST      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase		       =size if size is not None else self.size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =resample if resample is not None else self.resample
								lowerCamelCase		       =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase		       =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								lowerCamelCase		       =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase		       =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase		       =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase		       =image_std if image_std is not None else self.image_std
								lowerCamelCase		       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase		       =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""							)

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase		       =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase		       =[to_numpy_array(UpperCAmelCase_							) for image in images]

								if do_resize:
												lowerCamelCase		       =[self.resize(image=UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_							) for image in images]

								if do_center_crop:
												lowerCamelCase		       =[self.center_crop(image=UpperCAmelCase_      ,       size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase		       =[self.rescale(image=UpperCAmelCase_      ,       scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase		       =[self.normalize(image=UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_							) for image in images]

								lowerCamelCase		       =[to_channel_dimension_format(UpperCAmelCase_      ,       UpperCAmelCase_							) for image in images]

								lowerCamelCase		       ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_      ,       tensor_type=UpperCAmelCase_							)





