
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase     				=			'''pcuenq/k-upscaler'''






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							*, __SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   {
	    # 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_		(							__SCREAMING_SNAKE_CASE			,							*, __SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase  , lowercase  , lowercase					  =   checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3			,							dim=0							)
	lowercase  , lowercase  , lowercase					  =   checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3			,							dim=0							)
	lowercase					  =   {
	    # 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_		(							__SCREAMING_SNAKE_CASE			,							*, __SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase  , lowercase					  =   checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2			,							dim=0							)
	lowercase  , lowercase					  =   checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2			,							dim=0							)

	lowercase					  =   {
	    # 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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
	lowercase					  =   F'''{block_prefix}.{block_idx}'''

	lowercase					  =   {}

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

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

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

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

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

	return diffusers_checkpoint






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   {}

	# 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(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							'down'							)							)

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks							):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							'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_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   original_config['input_channels'] + original_config['unet_cond_dim']
	lowercase					  =   original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

	lowercase					  =   original_config['channels']

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

	lowercase					  =   original_config['mapping_cond_dim']
	lowercase					  =   original_config['cross_cond_dim']

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

	lowercase					  =   UNetaDConditionModel(
	    in_channels=__SCREAMING_SNAKE_CASE			,							out_channels=__SCREAMING_SNAKE_CASE			,							down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')			,							mid_block_type=__SCREAMING_SNAKE_CASE			,							up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')			,							block_out_channels=__SCREAMING_SNAKE_CASE			,							layers_per_block=__SCREAMING_SNAKE_CASE			,							act_fn='gelu'			,							norm_num_groups=__SCREAMING_SNAKE_CASE			,							cross_attention_dim=__SCREAMING_SNAKE_CASE			,							attention_head_dim=64			,							time_cond_proj_dim=__SCREAMING_SNAKE_CASE			,							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_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

	lowercase					  =   huggingface_hub.hf_hub_download(__SCREAMING_SNAKE_CASE			,							'config_laion_text_cond_latent_upscaler_2.json'							)
	lowercase					  =   huggingface_hub.hf_hub_download(
	    __SCREAMING_SNAKE_CASE			,							'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'							)
	lowercase					  =   K.config.load_config(open(__SCREAMING_SNAKE_CASE							)							)['model']
	lowercase					  =   unet_model_from_original_config(__SCREAMING_SNAKE_CASE							)

	lowercase					  =   torch.load(__SCREAMING_SNAKE_CASE			,							map_location=__SCREAMING_SNAKE_CASE							)['model_ema']
	lowercase					  =   unet_to_diffusers_checkpoint(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)

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


if __name__ == "__main__":
	UpperCAmelCase     				=			argparse.ArgumentParser()

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

	main(args)



from __future__ import annotations

from queue import Queue







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   {}





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i in self.vertices:
			print(snake_case		,   ' : '		,   ' -> '.join([str(snake_case	) for j in self.vertices[i]]	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if from_vertex in self.vertices:
			self.vertices[from_vertex].append(snake_case	)
		else:
			lowercase					  =   [to_vertex]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   set()

		# create a first in first out queue to store all the vertices for BFS
		lowercase					  =   Queue()

		# mark the source node as visited and enqueue it
		visited.add(snake_case	)
		queue.put(snake_case	)

		while not queue.empty():
			lowercase					  =   queue.get()

			# loop through all adjacent vertex and enqueue it if not yet visited
			for adjacent_vertex in self.vertices[vertex]:
				if adjacent_vertex not in visited:
					queue.put(snake_case	)
					visited.add(snake_case	)
		return visited


if __name__ == "__main__":
	from doctest import testmod

	testmod(verbose=True)

	UpperCAmelCase     				=			Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	# 0  :  1 -> 2
	# 1  :  2
	# 2  :  0 -> 3
	# 3  :  3

	assert sorted(g.bfs(2)) == [0, 1, 2, 3]

