





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase						:       int        =					'''pcuenq/k-upscaler'''







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						*, a ,						a  )				->				Optional[Any]:
     __A     :						List[Any]          =    {
         # 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 			_SCREAMING_SNAKE_CASE    (       a ,						*, a ,						a  )				->				int:
     __A     :						str          =    checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3 ,						dim=0  )
     __A     :						List[Any]          =    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 			_SCREAMING_SNAKE_CASE    (       a ,						*, a ,						a ,						a  )				->				int:
     __A     :						List[Any]          =    checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2 ,						dim=0  )
     __A     :						List[Any]          =    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 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[Any]:
     __A     :						Tuple          =    "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
     __A     :						Union[str, Any]          =    F"""{block_prefix}.{block_idx}"""

     __A     :						Any          =    {}

     if not hasattr(a ,						'attentions'  ):
          __A     :						Optional[int]          =    1  # resnet only
     elif not block.attentions[0].add_self_attention:
          __A     :						Union[str, Any]          =    2  # resnet -> cross-attention
     else:
          __A     :						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     :						str          =    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(
                  a ,						a ,						diffusers_resnet_prefix=a ,						resnet_prefix=a  )  )

     if hasattr(a ,						'attentions'  ):
          for attention_idx, attention in enumerate(block.attentions  ):
               __A     :						Any          =    F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
               __A     :						List[Any]          =    n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
               __A     :						str          =    F"""{block_prefix}.{idx}"""
               __A     :						str          =    F"""{block_prefix}.{idx }"""
               __A     :						List[Any]          =    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     :						Dict          =    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 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Dict:
     __A     :						Optional[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 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
     __A     :						List[Any]          =    original_config["input_channels"] + original_config["unet_cond_dim"]
     __A     :						Optional[int]          =    original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

     __A     :						Tuple          =    original_config["channels"]

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

     __A     :						Tuple          =    original_config["mapping_cond_dim"]
     __A     :						Tuple          =    original_config["cross_cond_dim"]

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

     __A     :						Optional[Any]          =    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 			_SCREAMING_SNAKE_CASE    (       a  )				->				Union[str, Any]:
     __A     :						int          =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

     __A     :						str          =    huggingface_hub.hf_hub_download(a ,						'config_laion_text_cond_latent_upscaler_2.json'  )
     __A     :						Any          =    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     :						List[str]          =    K.config.load_config(open(a  )  )["model"]
     __A     :						List[str]          =    unet_model_from_original_config(a  )

     __A     :						Union[str, Any]          =    torch.load(a ,						map_location=a  )["model_ema"]
     __A     :						List[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__":
    UpperCAmelCase						:       Optional[int]        =					argparse.ArgumentParser()

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

    main(args)











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase						:       str        =					logging.get_logger(__name__)

UpperCAmelCase						:       Optional[Any]        =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Union[str, Any]    =     '''swiftformer'''



				def __init__(      self					, _A=3					, _A=[3, 3, 6, 4]					, _A=[48, 56, 112, 220]					, _A=4					, _A=[True, True, True, True]					, _A="gelu"					, _A=3					, _A=2					, _A=1					, _A=0.0					, _A=True					, _A=1e-5					, _A=1e-5					, **_A					, ):
									super().__init__(**_A					)
									__A     :						List[Any]          =    num_channels
									__A     :						List[Any]          =    depths
									__A     :						int          =    embed_dims
									__A     :						Union[str, Any]          =    mlp_ratio
									__A     :						List[str]          =    downsamples
									__A     :						str          =    hidden_act
									__A     :						Any          =    down_patch_size
									__A     :						Any          =    down_stride
									__A     :						Any          =    down_pad
									__A     :						List[Any]          =    drop_path_rate
									__A     :						str          =    use_layer_scale
									__A     :						Dict          =    layer_scale_init_value
									__A     :						Union[str, Any]          =    batch_norm_eps





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     version.parse('''1.11'''       )



				@property
				def  UpperCAmelCase_		(      self					):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]					)





				@property
				def  UpperCAmelCase_		(      self					):
									return 1e-4




