
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCamelCase__        =						"""pcuenq/k-upscaler"""
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				*, SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[Any]:
			lowerCAmelCase__ : 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__     (    SCREAMING_SNAKE_CASE_			,				*, SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						str:
			lowerCAmelCase__ : List[str]   =						checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3			,				dim=0	)
			lowerCAmelCase__ : List[Any]   =						checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3			,				dim=0	)
			lowerCAmelCase__ : 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__     (    SCREAMING_SNAKE_CASE_			,				*, SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Any:
			lowerCAmelCase__ : Union[str, Any]   =						checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2			,				dim=0	)
			lowerCAmelCase__ : Any   =						checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2			,				dim=0	)

			lowerCAmelCase__ : 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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Any:
			lowerCAmelCase__ : str   =						'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
			lowerCAmelCase__ : Tuple   =						F'''{block_prefix}.{block_idx}'''

			lowerCAmelCase__ : Union[str, Any]   =						{}

			if not hasattr(UpperCAmelCase_			,				'attentions'	):
						lowerCAmelCase__ : List[Any]   =						1  # resnet only
			elif not block.attentions[0].add_self_attention:
						lowerCAmelCase__ : str   =						2  # resnet -> cross-attention
			else:
						lowerCAmelCase__ : List[str]   =						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}"
						lowerCAmelCase__ : Optional[Any]   =						F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
						lowerCAmelCase__ : List[Any]   =						n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
						lowerCAmelCase__ : Optional[Any]   =						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	):
									lowerCAmelCase__ : str   =						F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
									lowerCAmelCase__ : Optional[int]   =						n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
									lowerCAmelCase__ : int   =						F'''{block_prefix}.{idx}'''
									lowerCAmelCase__ : Optional[int]   =						F'''{block_prefix}.{idx }'''
									lowerCAmelCase__ : int   =						1 if not attention.add_self_attention else 2
									lowerCAmelCase__ : Optional[Any]   =						(
									    n * attention_idx + cross_attention_index
									    if block_type == 'up'
									    else n * attention_idx + cross_attention_index + 1
									)
									lowerCAmelCase__ : List[Any]   =						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_	)							->						List[str]:
			lowerCAmelCase__ : Tuple   =						{}

			# 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_	)							->						List[Any]:
			lowerCAmelCase__ : List[Any]   =						original_config['input_channels'] + original_config['unet_cond_dim']
			lowerCAmelCase__ : Any   =						original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

			lowerCAmelCase__ : Tuple   =						original_config['channels']

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

			lowerCAmelCase__ : Tuple   =						original_config['mapping_cond_dim']
			lowerCAmelCase__ : Any   =						original_config['cross_cond_dim']

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

			lowerCAmelCase__ : List[Any]   =						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_	)							->						Any:
			lowerCAmelCase__ : Optional[Any]   =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

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

			lowerCAmelCase__ : List[Any]   =						torch.load(UpperCAmelCase_			,				map_location=UpperCAmelCase_	)['model_ema']
			lowerCAmelCase__ : Optional[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__":
					lowerCamelCase__        =						argparse.ArgumentParser()

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

					main(args)


'''simple docstring'''

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Optional[Any]		   =					pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      str		   =					pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3    ,			dim=0      )

 UpperCAmelCase			:      Dict		   =					pt_weights[F"""{prefix}.out_proj.weight"""]
 UpperCAmelCase			:      Tuple		   =					pt_weights[F"""{prefix}.out_proj.bias"""]

 UpperCAmelCase			:      Dict		   =					q_proj
 UpperCAmelCase			:      Optional[Any]		   =					q_proj_bias

 UpperCAmelCase			:      Union[str, Any]		   =					k_proj
 UpperCAmelCase			:      Optional[Any]		   =					k_proj_bias

 UpperCAmelCase			:      Dict		   =					v_proj
 UpperCAmelCase			:      Tuple		   =					v_proj_bias

 UpperCAmelCase			:      List[Any]		   =					out_proj_weights
 UpperCAmelCase			:      Any		   =					out_proj_bias




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 copy_linear(hf_mlp.fca    ,			UpperCAmelCase_    ,			F"""{prefix}.c_fc"""      )
 copy_linear(hf_mlp.fca    ,			UpperCAmelCase_    ,			F"""{prefix}.c_proj"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					pt_weights[F"""{prefix}.weight"""].data
 UpperCAmelCase			:      Dict		   =					pt_weights[F"""{prefix}.bias"""].data




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 # copy layer norms
 copy_linear(hf_layer.layer_norma    ,			UpperCAmelCase_    ,			F"""{prefix}.ln_1"""      )
 copy_linear(hf_layer.layer_norma    ,			UpperCAmelCase_    ,			F"""{prefix}.ln_2"""      )

 # copy MLP
 copy_mlp(hf_layer.mlp    ,			UpperCAmelCase_    ,			F"""{prefix}.mlp"""      )

 # copy attn
 copy_attn_layer(hf_layer.self_attn    ,			UpperCAmelCase_    ,			F"""{prefix}.attn"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 for layer_id, hf_layer in enumerate(UpperCAmelCase_      ):
  copy_layer(UpperCAmelCase_    ,			UpperCAmelCase_    ,			F"""{prefix}.{layer_id}"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 # copy projection
 UpperCAmelCase			:      Optional[int]		   =					pt_weights['text_projection'].data.T

 # copy text encoder
 for name, param in hf_model.text_model.named_parameters():
  UpperCAmelCase			:      int		   =					pt_weights[F"""bert.{name}"""].data




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 # copy projection
 UpperCAmelCase			:      Optional[int]		   =					pt_weights['visual.proj'].data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layrnorm    ,			UpperCAmelCase_    ,			'visual.ln_pre'      )
 copy_linear(hf_model.vision_model.post_layernorm    ,			UpperCAmelCase_    ,			'visual.ln_post'      )

 # copy embeddings
 UpperCAmelCase			:      List[str]		   =					pt_weights['visual.conv1.weight'].data
 UpperCAmelCase			:      Tuple		   =					pt_weights['visual.class_embedding'].data
 UpperCAmelCase			:      str		   =					pt_weights['visual.positional_embedding'].data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers    ,			UpperCAmelCase_    ,			'visual.transformer.resblocks'      )




@torch.no_grad()
def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_=None      ):
 assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
 UpperCAmelCase			:      str		   =					ChineseCLIPConfig.from_pretrained(UpperCAmelCase_      )

 UpperCAmelCase			:      List[Any]		   =					ChineseCLIPModel(UpperCAmelCase_      ).eval()

 UpperCAmelCase			:      Optional[Any]		   =					torch.load(UpperCAmelCase_    ,			map_location='cpu'      )['state_dict']
 UpperCAmelCase			:      Any		   =					{(name[7:] if name.startswith('module.'      ) else name): value for name, value in pt_weights.items()}

 copy_text_model_and_projection(UpperCAmelCase_    ,			UpperCAmelCase_      )
 copy_vision_model_and_projection(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      Optional[int]		   =					pt_weights['logit_scale'].data

 hf_model.save_pretrained(UpperCAmelCase_      )


if __name__ == "__main__":
 lowercase__            =     argparse.ArgumentParser()
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     help="Path to the output folder storing converted hf PyTorch model.",
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
 )
 parser.add_argument(
     "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
 )
 lowercase__            =     parser.parse_args()

 convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
 print("The conversion is finished!")




