

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a_         =      '''pcuenq/k-upscaler'''

def 	lowerCamelCase__	(   _a							,     _a							,     *, _a							,     _a):
	SCREAMING_SNAKE_CASE				: 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 	lowerCamelCase__	(   _a							,     *, _a							,     _a):
	SCREAMING_SNAKE_CASE				: Tuple			       =    checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3							,     dim=0)
	SCREAMING_SNAKE_CASE				: Dict			       =    checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3							,     dim=0)
	SCREAMING_SNAKE_CASE				: 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 	lowerCamelCase__	(   _a							,     *, _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: Dict			       =    checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2							,     dim=0)
	SCREAMING_SNAKE_CASE				: str			       =    checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2							,     dim=0)

	SCREAMING_SNAKE_CASE				: 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 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: List[str]			       =    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
	SCREAMING_SNAKE_CASE				: int			       =    f"{block_prefix}.{block_idx}"

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    {}

	if not hasattr(_a							,     "attentions"):
		SCREAMING_SNAKE_CASE				: Tuple			       =    1  # resnet only
	elif not block.attentions[0].add_self_attention:
		SCREAMING_SNAKE_CASE				: Any			       =    2  # resnet -> cross-attention
	else:
		SCREAMING_SNAKE_CASE				: 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}"
		SCREAMING_SNAKE_CASE				: List[str]			       =    f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
		SCREAMING_SNAKE_CASE				: Any			       =    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):
			SCREAMING_SNAKE_CASE				: str			       =    f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
			SCREAMING_SNAKE_CASE				: Tuple			       =    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
			SCREAMING_SNAKE_CASE				: Dict			       =    f"{block_prefix}.{idx}"
			SCREAMING_SNAKE_CASE				: Tuple			       =    f"{block_prefix}.{idx }"
			SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    1 if not attention.add_self_attention else 2
			SCREAMING_SNAKE_CASE				: str			       =    (
			    n * attention_idx + cross_attention_index
			    if block_type == 'up'
			    else n * attention_idx + cross_attention_index + 1
			)
			SCREAMING_SNAKE_CASE				: Tuple			       =    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 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: 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(_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 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: str			       =    original_config['input_channels'] + original_config['unet_cond_dim']
	SCREAMING_SNAKE_CASE				: Tuple			       =    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

	SCREAMING_SNAKE_CASE				: str			       =    original_config['channels']

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

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

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

	SCREAMING_SNAKE_CASE				: str			       =    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 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: Dict			       =    torch.device("cuda" if torch.cuda.is_available() else "cpu")

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

	SCREAMING_SNAKE_CASE				: Dict			       =    torch.load(_a							,     map_location=_a)['model_ema']
	SCREAMING_SNAKE_CASE				: int			       =    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__":
			a_         =      argparse.ArgumentParser()

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

			main(args)


from __future__ import annotations






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	List[Any] , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vertices
				SCREAMING_SNAKE_CASE				: Tuple			       =    [[0] * vertices for _ in range(a		)]
			def 							__UpperCamelCase	(     self							:	Dict , a							:	list[int]		)							->    None:

				"""simple docstring"""

				print("Vertex \t Distance from Source"		)
				for vertex in range(self.vertices		):
					print(a , "\t\t" , distances_from_source[vertex]		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	list[int] , a							:	list[bool]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    1e7
				SCREAMING_SNAKE_CASE				: int			       =    0

				# Search not nearest vertex not in the shortest path tree
				for vertex in range(self.vertices		):
					if distances_from_source[vertex] < minimum and visited[vertex] is False:
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    distances_from_source[vertex]
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    vertex
				return min_index






			def 							__UpperCamelCase	(     self							:	Any , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    [int(1e7		)] * self.vertices  # distances from the source
				SCREAMING_SNAKE_CASE				: Tuple			       =    0
				SCREAMING_SNAKE_CASE				: List[Any]			       =    [False] * self.vertices

				for _ in range(self.vertices		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.minimum_distance(a , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    True

					# Update dist value of the adjacent vertices
					# of the picked vertex only if the current
					# distance is greater than new distance and
					# the vertex in not in the shortest path tree
					for v in range(self.vertices		):
						if (
						    self.graph[u][v] > 0
						    and visited[v] is False
						    and distances[v] > distances[u] + self.graph[u][v]
						):
							SCREAMING_SNAKE_CASE				: int			       =    distances[u] + self.graph[u][v]

				self.print_solution(a		)


if __name__ == "__main__":
			a_         =      Graph(9)
			a_         =      [
			    [0, 4, 0, 0, 0, 0, 0, 8, 0],
			    [4, 0, 8, 0, 0, 0, 0, 11, 0],
			    [0, 8, 0, 7, 0, 4, 0, 0, 2],
			    [0, 0, 7, 0, 9, 14, 0, 0, 0],
			    [0, 0, 0, 9, 0, 10, 0, 0, 0],
			    [0, 0, 4, 14, 10, 0, 2, 0, 0],
			    [0, 0, 0, 0, 0, 2, 0, 1, 6],
			    [8, 11, 0, 0, 0, 0, 1, 0, 7],
			    [0, 0, 2, 0, 0, 0, 6, 7, 0],
			]
			graph.dijkstra(0)