




'''simple docstring'''







import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_lowerCamelCase				:		str				      =    'pcuenq/k-upscaler'






def 						_lowercase						(      lowercase_		,				lowercase_		,				*, lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			{
		    # 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 						_lowercase						(      lowercase_		,				*, lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case			,       _snake_case      			=			checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3		,				dim=0    )
		_snake_case			,       _snake_case			,       _snake_case      			=			checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3		,				dim=0    )
		_snake_case      			=			{
		    # 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 						_lowercase						(      lowercase_		,				*, lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2		,				dim=0    )
		_snake_case			,       _snake_case      			=			checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2		,				dim=0    )

		_snake_case      			=			{
		    # 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 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			"inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
		_snake_case      			=			f'''{block_prefix}.{block_idx}'''

		_snake_case      			=			{}

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

				diffusers_checkpoint.update(
				    resnet_to_diffusers_checkpoint(
				        lowercase_		,				lowercase_		,				diffusers_resnet_prefix=lowercase_		,				resnet_prefix=lowercase_    )    )

		if hasattr(lowercase_		,				"attentions"    ):
				for attention_idx, attention in enumerate(block.attentions    ):
						_snake_case      			=			f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
						_snake_case      			=			n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
						_snake_case      			=			f'''{block_prefix}.{idx}'''
						_snake_case      			=			f'''{block_prefix}.{idx }'''
						_snake_case      			=			1 if not attention.add_self_attention else 2
						_snake_case      			=			(
						    n * attention_idx + cross_attention_index
						    if block_type == "up"
						    else n * attention_idx + cross_attention_index + 1
						)
						_snake_case      			=			f'''{block_prefix}.{idx }'''

						diffusers_checkpoint.update(
						    cross_attn_to_diffusers_checkpoint(
						        lowercase_		,				diffusers_attention_prefix=lowercase_		,				diffusers_attention_index=2		,				attention_prefix=lowercase_		,				)    )

						if attention.add_self_attention is True:
								diffusers_checkpoint.update(
								    self_attn_to_diffusers_checkpoint(
								        lowercase_		,				diffusers_attention_prefix=lowercase_		,				attention_prefix=lowercase_		,				)    )

		return diffusers_checkpoint






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			{}

		# 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(lowercase_		,				lowercase_		,				lowercase_		,				"down"    )    )

		# up_blocks
		for up_block_idx, up_block in enumerate(model.up_blocks    ):
				diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase_		,				lowercase_		,				lowercase_		,				"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 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			original_config["input_channels"] + original_config["unet_cond_dim"]
		_snake_case      			=			original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

		_snake_case      			=			original_config["channels"]

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

		_snake_case      			=			original_config["mapping_cond_dim"]
		_snake_case      			=			original_config["cross_cond_dim"]

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

		_snake_case      			=			UNetaDConditionModel(
		    in_channels=lowercase_		,				out_channels=lowercase_		,				down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")		,				mid_block_type=lowercase_		,				up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")		,				block_out_channels=lowercase_		,				layers_per_block=lowercase_		,				act_fn="gelu"		,				norm_num_groups=lowercase_		,				cross_attention_dim=lowercase_		,				attention_head_dim=64		,				time_cond_proj_dim=lowercase_		,				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 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			torch.device("cuda" if torch.cuda.is_available() else "cpu"    )

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

		_snake_case      			=			torch.load(lowercase_		,				map_location=lowercase_    )["model_ema"]
		_snake_case      			=			unet_to_diffusers_checkpoint(lowercase_		,				lowercase_    )

		model.load_state_dict(lowercase_		,				strict=lowercase_    )
		model.save_pretrained(args.dump_path    )
		print(f'''saving converted unet model in {args.dump_path}'''    )


if __name__ == "__main__":
						_lowerCamelCase				:		List[Any]				      =    argparse.ArgumentParser()

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

						main(args)











'''simple docstring'''







import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class 	lowerCamelCase_				(			enum.Enum				):




					'''simple docstring'''






					lowercase__       =					"""timm"""
					lowercase__       =					"""transformers"""






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if stage_names is None:
				raise ValueError("Stage_names must be set for transformers backbones"    )

		if out_features is not None:
				if not isinstance(lowercase_		,				(list,)    ):
						raise ValueError(f'''out_features must be a list {type(lowercase_    )}'''    )
				if any(feat not in stage_names for feat in out_features    ):
						raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''    )

		if out_indices is not None:
				if not isinstance(lowercase_		,				(list, tuple)    ):
						raise ValueError(f'''out_indices must be a list or tuple, got {type(lowercase_    )}'''    )
				if any(idx >= len(lowercase_    ) for idx in out_indices    ):
						raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"    )

		if out_features is not None and out_indices is not None:
				if len(lowercase_    ) != len(lowercase_    ):
						raise ValueError("out_features and out_indices should have the same length if both are set"    )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError("out_features and out_indices should correspond to the same stages if both are set"    )






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		if out_indices is None and out_features is None:
				_snake_case      			=			[len(lowercase_    ) - 1]
				_snake_case      			=			[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				_snake_case      			=			[stage_names.index(lowercase_    ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				_snake_case      			=			[stage_names[idx] for idx in out_indices]
		return out_features, out_indices






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		_snake_case			,       _snake_case      			=			_align_output_features_output_indices(
		    out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		return output_features, output_indices





class 	lowerCamelCase_				:




					'''simple docstring'''






					lowercase__       =					None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							if getattr(self,    "_backbone",    __UpperCamelCase					) is None:
									raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

							# These will diagree with the defaults for the transformers models e.g. for resnet50
							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
							_snake_case      			=			[stage["module"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			[stage["num_chs"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			self._backbone.feature_info.out_indices
							_snake_case      			=			self._backbone.feature_info.module_name()

							# We verify the out indices and out features are valid
							verify_out_features_out_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)
							_snake_case			,       _snake_case      			=			out_features, out_indices






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			getattr(__UpperCamelCase,    "stage_names"					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_features",    __UpperCamelCase					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_indices",    __UpperCamelCase					)

							_snake_case      			=			stage_names
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=__UpperCamelCase					)
							# Number of channels for each stage. This is set in the transformer backbone model init
							_snake_case      			=			None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			config

							_snake_case      			=			getattr(__UpperCamelCase,    "use_timm_backbone",    __UpperCamelCase					)
							_snake_case      			=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

							if self.backbone_type == BackboneType.TIMM:
									self._init_timm_backbone(__UpperCamelCase					)
							elif self.backbone_type == BackboneType.TRANSFORMERS:
									self._init_transformers_backbone(__UpperCamelCase					)
							else:
									raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# the current backbones will output the number of channels for each stage
							# even if that stage is not in the out_features list.
							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return [self.out_feature_channels[name] for name in self.out_features]






					def 							__SCREAMING_SNAKE_CASE      (       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							_snake_case      			=			dict(inspect.signature(self.forward					).parameters					)
							_snake_case      			=			{k: v for k, v in kwargs.items() if k in signature}
							return self(*__UpperCamelCase,    **__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    ):
							raise NotImplementedError("This method should be implemented by the derived class."					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





class 	lowerCamelCase_				:




					'''simple docstring'''






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





