
"""simple docstring"""
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase		:						Any =					'pcuenq/k-upscaler'


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				*, lowerCamelCase_	,				lowerCamelCase_ )     ->			List[str]:


     '''simple docstring'''
     __a:						Union[str, 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     lowerCamelCase_    (	lowerCamelCase_	,				*, lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[Any]:


     '''simple docstring'''
     __a:						Optional[int]		    =					checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3	,				dim=0 )
     __a:						Optional[int]		    =					checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3	,				dim=0 )
     __a:						List[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     lowerCamelCase_    (	lowerCamelCase_	,				*, lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


     '''simple docstring'''
     __a:						Optional[Any]		    =					checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2	,				dim=0 )
     __a:						str		    =					checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2	,				dim=0 )

     __a:						List[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_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Tuple:


     '''simple docstring'''
     __a:						Any		    =					"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
     __a:						List[Any]		    =					f"""{block_prefix}.{block_idx}"""

     __a:						Optional[int]		    =					{}

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

     if hasattr(lowerCamelCase_	,				"""attentions""" ):
          for attention_idx, attention in enumerate(block.attentions ):
               __a:						List[Any]		    =					f"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
               __a:						Union[str, Any]		    =					n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
               __a:						List[Any]		    =					f"""{block_prefix}.{idx}"""
               __a:						Optional[Any]		    =					f"""{block_prefix}.{idx }"""
               __a:						Optional[Any]		    =					1 if not attention.add_self_attention else 2
               __a:						List[str]		    =					(
                   n * attention_idx + cross_attention_index
                   if block_type == """up"""
                   else n * attention_idx + cross_attention_index + 1
               )
               __a:						Optional[Any]		    =					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_	,				lowerCamelCase_ )     ->			Dict:


     '''simple docstring'''
     __a:						List[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(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_ )     ->			List[str]:


     '''simple docstring'''
     __a:						Union[str, Any]		    =					original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
     __a:						int		    =					original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

     __a:						str		    =					original_config["""channels"""]

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

     __a:						Optional[int]		    =					original_config["""mapping_cond_dim"""]
     __a:						Tuple		    =					original_config["""cross_cond_dim"""]

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

     __a:						Optional[int]		    =					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_ )     ->			Dict:


     '''simple docstring'''
     __a:						Optional[Any]		    =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

     __a:						List[Any]		    =					huggingface_hub.hf_hub_download(lowerCamelCase_	,				"""config_laion_text_cond_latent_upscaler_2.json""" )
     __a:						int		    =					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""" )
     __a:						Dict		    =					K.config.load_config(open(lowerCamelCase_ ) )["""model"""]
     __a:						List[str]		    =					unet_model_from_original_config(lowerCamelCase_ )

     __a:						Union[str, Any]		    =					torch.load(lowerCamelCase_	,				map_location=lowerCamelCase_ )["""model_ema"""]
     __a:						str		    =					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__":
   lowercase		:						str =					argparse.ArgumentParser()

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

   main(args)


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
