

"""simple docstring"""






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


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

	_UpperCAmelCase      :							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  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  List[str]	)			->     Union[str, Any]:
	_UpperCAmelCase      :							Tuple       =   "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
	_UpperCAmelCase      :							Union[str, Any]       =   f'''{block_prefix}.{block_idx}'''

	_UpperCAmelCase      :							Any       =   {}

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

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

	if hasattr(_lowerCAmelCase,					"""attentions"""	):
		for attention_idx, attention in enumerate(block.attentions	):
			_UpperCAmelCase      :							Any       =   f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
			_UpperCAmelCase      :							List[Any]       =   n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
			_UpperCAmelCase      :							str       =   f'''{block_prefix}.{idx}'''
			_UpperCAmelCase      :							str       =   f'''{block_prefix}.{idx }'''
			_UpperCAmelCase      :							List[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      :							Dict       =   f'''{block_prefix}.{idx }'''

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

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

	return diffusers_checkpoint
def  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  List[Any]	)			->     Optional[Any]:
	_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(_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					"""down"""	)	)

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks	):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					"""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  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any]	)			->     List[Any]:
	_UpperCAmelCase      :							List[Any]       =   original_config["input_channels"] + original_config["unet_cond_dim"]
	_UpperCAmelCase      :							Optional[int]       =   original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

	_UpperCAmelCase      :							Tuple       =   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      :							Tuple       =   original_config["mapping_cond_dim"]
	_UpperCAmelCase      :							Tuple       =   original_config["cross_cond_dim"]

	_UpperCAmelCase      :							List[str]       =   []
	_UpperCAmelCase      :							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      :							Optional[Any]       =   "cross"
			_UpperCAmelCase      :							Union[str, Any]       =   None
		else:
			_UpperCAmelCase      :							Union[str, Any]       =   None
			_UpperCAmelCase      :							Optional[int]       =   None
		attna_types.append(_lowerCAmelCase	)
		attna_types.append(_lowerCAmelCase	)

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

	_UpperCAmelCase      :							str       =   huggingface_hub.hf_hub_download(_lowerCAmelCase,					"""config_laion_text_cond_latent_upscaler_2.json"""	)
	_UpperCAmelCase      :							Any       =   huggingface_hub.hf_hub_download(
	    _lowerCAmelCase,					"""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      :							List[str]       =   K.config.load_config(open(_lowerCAmelCase	)	)["model"]
	_UpperCAmelCase      :							List[str]       =   unet_model_from_original_config(_lowerCAmelCase	)

	_UpperCAmelCase      :							Union[str, Any]       =   torch.load(_lowerCAmelCase,					map_location=_lowerCAmelCase	)["model_ema"]
	_UpperCAmelCase      :							List[Any]       =   unet_to_diffusers_checkpoint(_lowerCAmelCase,					_lowerCAmelCase	)

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


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

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

						main(args)








"""simple docstring"""






from math import factorial
def  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  List[str]	)			->     Dict:
	return factorial(_lowerCAmelCase	) / (factorial(_lowerCAmelCase	) * factorial(n - r	))
def  UpperCamelCase   (     )			->     Tuple:
	_UpperCAmelCase      :							Optional[int]       =   0

	for i in range(1,					101	):
		for j in range(1,					i + 1	):
			if combinations(_lowerCAmelCase,					_lowerCAmelCase	) > 1E6:
				total += 1
	return total


if __name__ == "__main__":
						print(solution())





