

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase_       :				int		   =      '''pcuenq/k-upscaler'''
def        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      Optional[Any] ,*, a__						:      Union[str, Any] ,a__						:      Any					)				->							Union[str, Any]:
	__A   : 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        __SCREAMING_SNAKE_CASE    (      a__						:      Dict ,*, a__						:      List[str] ,a__						:      List[str]					)				->							List[Any]:
	__A						,							__A						,							__A   : List[str]								= checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3 ,dim=0					)
	__A						,							__A						,							__A   : List[str]								= checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3 ,dim=0					)
	__A   : Optional[int]								= {
	    # 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        __SCREAMING_SNAKE_CASE    (      a__						:      int ,*, a__						:      Tuple ,a__						:      Union[str, Any] ,a__						:      List[Any]					)				->							Dict:
	__A						,							__A   : Tuple								= checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2 ,dim=0					)
	__A						,							__A   : Optional[Any]								= checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2 ,dim=0					)

	__A   : 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        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      List[Any] ,a__						:      str ,a__						:      str					)				->							Union[str, Any]:
	__A   : Any								= """inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
	__A   : Any								= f"""{block_prefix}.{block_idx}"""

	__A   : Optional[Any]								= {}

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

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

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

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

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

	return diffusers_checkpoint
def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      Union[str, Any]					)				->							List[str]:
	__A   : 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(a__ ,a__ ,a__ ,"""down"""					)					)

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks					):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(a__ ,a__ ,a__ ,"""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        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any]					)				->							List[str]:
	__A   : Optional[int]								= original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
	__A   : Tuple								= original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

	__A   : Optional[Any]								= original_config["""channels"""]

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

	__A   : Union[str, Any]								= original_config["""mapping_cond_dim"""]
	__A   : Dict								= original_config["""cross_cond_dim"""]

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

	__A   : Union[str, Any]								= UNetaDConditionModel(
	    in_channels=a__ ,out_channels=a__ ,down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""") ,mid_block_type=a__ ,up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") ,block_out_channels=a__ ,layers_per_block=a__ ,act_fn="""gelu""" ,norm_num_groups=a__ ,cross_attention_dim=a__ ,attention_head_dim=64 ,time_cond_proj_dim=a__ ,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        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any]					)				->							str:
	__A   : List[Any]								= torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)

	__A   : int								= huggingface_hub.hf_hub_download(a__ ,"""config_laion_text_cond_latent_upscaler_2.json"""					)
	__A   : Optional[int]								= huggingface_hub.hf_hub_download(
	    a__ ,"""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(a__					)					)["""model"""]
	__A   : int								= unet_model_from_original_config(a__					)

	__A   : List[str]								= torch.load(a__ ,map_location=a__					)["""model_ema"""]
	__A   : Any								= unet_to_diffusers_checkpoint(a__ ,a__					)

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


if __name__ == "__main__":
			UpperCAmelCase_       :				int		   =      argparse.ArgumentParser()

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

			main(args)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_       :				Optional[Any]		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				List[str]		   =      {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      Optional[Any]    				=							'''nystromformer'''




	def __init__(    self	:    Optional[Any]				,			__A	:    Optional[Any]=3_0000				,			__A	:    Optional[int]=768				,			__A	:    List[str]=12				,			__A	:    str=12				,			__A	:    Tuple=3072				,			__A	:    List[Any]="gelu_new"				,			__A	:    Dict=0.1				,			__A	:    Optional[int]=0.1				,			__A	:    int=510				,			__A	:    Union[str, Any]=2				,			__A	:    Optional[int]=64				,			__A	:    str=64				,			__A	:    List[str]=65				,			__A	:    Tuple=False				,			__A	:    List[str]=0.0_2				,			__A	:    Tuple=1e-5				,			__A	:    List[str]=1				,			__A	:    int=0				,			__A	:    Tuple=2				,			**__A	:    Dict				,			):
		__A   : str								= vocab_size
		__A   : Union[str, Any]								= max_position_embeddings
		__A   : Optional[int]								= hidden_size
		__A   : Optional[int]								= num_hidden_layers
		__A   : List[Any]								= num_attention_heads
		__A   : Dict								= intermediate_size
		__A   : Union[str, Any]								= hidden_act
		__A   : int								= hidden_dropout_prob
		__A   : Tuple								= attention_probs_dropout_prob
		__A   : Optional[Any]								= initializer_range
		__A   : int								= type_vocab_size
		__A   : Optional[int]								= segment_means_seq_len
		__A   : List[str]								= num_landmarks
		__A   : Dict								= conv_kernel_size
		__A   : Union[str, Any]								= inv_coeff_init_option
		__A   : Union[str, Any]								= layer_norm_eps
		super().__init__(pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			**__A       )





