


'''simple docstring'''
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__SCREAMING_SNAKE_CASE				:							str		       =		'''pcuenq/k-upscaler'''







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     *, UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		{
							    # 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 			a_							(  UpperCamelCase_	,     *, UpperCamelCase_	,     UpperCamelCase_   ):
							A_						,				A_						,				A_ 						=		checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3	,     dim=0   )
							A_						,				A_						,				A_ 						=		checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3	,     dim=0   )
							A_ 						=		{
							    # 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 			a_							(  UpperCamelCase_	,     *, UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_						,				A_ 						=		checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2	,     dim=0   )
							A_						,				A_ 						=		checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2	,     dim=0   )

							A_ 						=		{
							    # 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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		"inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
							A_ 						=		f"{block_prefix}.{block_idx}"

							A_ 						=		{}

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

							# 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 			a_							(  UpperCamelCase_   ):
							A_ 						=		original_config["input_channels"] + original_config["unet_cond_dim"]
							A_ 						=		original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

							A_ 						=		original_config["channels"]

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

							A_ 						=		original_config["mapping_cond_dim"]
							A_ 						=		original_config["cross_cond_dim"]

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

							A_ 						=		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 			a_							(  UpperCamelCase_   ):
							A_ 						=		torch.device("cuda" if torch.cuda.is_available() else "cpu"   )

							A_ 						=		huggingface_hub.hf_hub_download(UpperCamelCase_	,     "config_laion_text_cond_latent_upscaler_2.json"   )
							A_ 						=		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_ 						=		K.config.load_config(open(UpperCamelCase_   )   )["model"]
							A_ 						=		unet_model_from_original_config(UpperCamelCase_   )

							A_ 						=		torch.load(UpperCamelCase_	,     map_location=UpperCamelCase_   )["model_ema"]
							A_ 						=		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__":
							__SCREAMING_SNAKE_CASE				:							int		       =		argparse.ArgumentParser()

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

							main(args)









'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													if text is None and images is None:
																				raise ValueError("You have to specify either text or images. Both cannot be none."  )

													if text is not None:
																				A_ 						=		self.tokenizer(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if text is not None and images is not None:
																				A_ 						=		image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





