

'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a       :      Union[str, Any]  	=			'''pcuenq/k-upscaler'''





def 				lowercase (					__magic_name__			,   __magic_name__			,   *, __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							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 				lowercase (					__magic_name__			,   *, __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple        =       checkpoint[F"{attention_prefix}.qkv_proj.weight"].chunk(3			,   dim=0							)
					UpperCAmelCase		:							Dict        =       checkpoint[F"{attention_prefix}.qkv_proj.bias"].chunk(3			,   dim=0							)
					UpperCAmelCase		:							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 				lowercase (					__magic_name__			,   *, __magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       checkpoint[F"{attention_prefix}.kv_proj.weight"].chunk(2			,   dim=0							)
					UpperCAmelCase		:							str        =       checkpoint[F"{attention_prefix}.kv_proj.bias"].chunk(2			,   dim=0							)

					UpperCAmelCase		:							Optional[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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[str]        =       'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					UpperCAmelCase		:							int        =       F"{block_prefix}.{block_idx}"

					UpperCAmelCase		:							Optional[int]        =       {}

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

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

					if hasattr(__magic_name__			,   "attentions"							):
										for attention_idx, attention in enumerate(block.attentions							):
															UpperCAmelCase		:							str        =       F"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
															UpperCAmelCase		:							Tuple        =       n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
															UpperCAmelCase		:							Dict        =       F"{block_prefix}.{idx}"
															UpperCAmelCase		:							Tuple        =       F"{block_prefix}.{idx }"
															UpperCAmelCase		:							Union[str, Any]        =       1 if not attention.add_self_attention else 2
															UpperCAmelCase		:							str        =       (
															    n * attention_idx + cross_attention_index
															    if block_type == 'up'
															    else n * attention_idx + cross_attention_index + 1
															)
															UpperCAmelCase		:							Tuple        =       F"{block_prefix}.{idx }"

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

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

					return diffusers_checkpoint





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							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(__magic_name__			,   __magic_name__			,   __magic_name__			,   "down"							)							)

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



					'''simple docstring'''







					UpperCAmelCase		:							str        =       original_config['input_channels'] + original_config['unet_cond_dim']
					UpperCAmelCase		:							Tuple        =       original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					UpperCAmelCase		:							str        =       original_config['channels']

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

					UpperCAmelCase		:							str        =       original_config['mapping_cond_dim']
					UpperCAmelCase		:							Union[str, Any]        =       original_config['cross_cond_dim']

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

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



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       torch.device("cuda" if torch.cuda.is_available() else "cpu"							)

					UpperCAmelCase		:							List[Any]        =       huggingface_hub.hf_hub_download(__magic_name__			,   "config_laion_text_cond_latent_upscaler_2.json"							)
					UpperCAmelCase		:							Union[str, Any]        =       huggingface_hub.hf_hub_download(
					    __magic_name__			,   "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"							)
					UpperCAmelCase		:							Optional[Any]        =       K.config.load_config(open(__magic_name__							)							)['model']
					UpperCAmelCase		:							Tuple        =       unet_model_from_original_config(__magic_name__							)

					UpperCAmelCase		:							Dict        =       torch.load(__magic_name__			,   map_location=__magic_name__							)['model_ema']
					UpperCAmelCase		:							int        =       unet_to_diffusers_checkpoint(__magic_name__			,   __magic_name__							)

					model.load_state_dict(__magic_name__			,   strict=__magic_name__							)
					model.save_pretrained(args.dump_path							)
					print(F"saving converted unet model in {args.dump_path}"							)


if __name__ == "__main__":
						a       :      Optional[int]  	=			argparse.ArgumentParser()

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

						main(args)



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
