
"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Optional[int]=1       ):
	if n_shave_prefix_segments >= 0:
		return ".".join(path.split('.'       )[n_shave_prefix_segments:]       )
	else:
		return ".".join(path.split('.'       )[:n_shave_prefix_segments]       )






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Dict=0       ):
	A :    Tuple  				=				[]
	for old_item in old_list:
		A :    Tuple  				=				old_item
		A :    Optional[int]  				=				new_item.replace('block.'		, 'resnets.'       )
		A :    str  				=				new_item.replace('conv_shorcut'		, 'conv1'       )
		A :    Dict  				=				new_item.replace('in_shortcut'		, 'conv_shortcut'       )
		A :    List[str]  				=				new_item.replace('temb_proj'		, 'time_emb_proj'       )

		A :    Union[str, Any]  				=				shave_segments(snake_case__		, n_shave_prefix_segments=snake_case__       )

		mapping.append({'old': old_item, 'new': new_item}       )

	return mapping






def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       List[Any]=0		, snake_case__				:       Optional[Any]=False       ):
	A :    Tuple  				=				[]
	for old_item in old_list:
		A :    List[str]  				=				old_item

		# In `model.mid`, the layer is called `attn`.
		if not in_mid:
			A :    Optional[Any]  				=				new_item.replace('attn'		, 'attentions'       )
		A :    List[Any]  				=				new_item.replace('.k.'		, '.key.'       )
		A :    Tuple  				=				new_item.replace('.v.'		, '.value.'       )
		A :    Union[str, Any]  				=				new_item.replace('.q.'		, '.query.'       )

		A :    Any  				=				new_item.replace('proj_out'		, 'proj_attn'       )
		A :    Any  				=				new_item.replace('norm'		, 'group_norm'       )

		A :    Union[str, Any]  				=				shave_segments(snake_case__		, n_shave_prefix_segments=snake_case__       )
		mapping.append({'old': old_item, 'new': new_item}       )

	return mapping






def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       Any		, snake_case__				:       List[Any]		, snake_case__				:       List[str]=None		, snake_case__				:       Dict=None		, snake_case__				:       int=None       ):
	assert isinstance(snake_case__		, snake_case__       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

	if attention_paths_to_split is not None:
		if config is None:
			raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'       )

		for path, path_map in attention_paths_to_split.items():
			A :    Union[str, Any]  				=				old_checkpoint[path]
			A :    List[Any]  				=				old_tensor.shape[0] // 3

			A :    Optional[Any]  				=				(-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

			A :    int  				=				old_tensor.shape[0] // config.get('num_head_channels'		, 1       ) // 3

			A :    List[str]  				=				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
			A :    List[Any]  				=				old_tensor.split(channels // num_heads		, dim=1       )

			A :    Union[str, Any]  				=				query.reshape(snake_case__       ).squeeze()
			A :    Optional[int]  				=				key.reshape(snake_case__       ).squeeze()
			A :    Optional[Any]  				=				value.reshape(snake_case__       ).squeeze()

	for path in paths:
		A :    str  				=				path['new']

		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		A :    Union[str, Any]  				=				new_path.replace('down.'		, 'down_blocks.'       )
		A :    List[str]  				=				new_path.replace('up.'		, 'up_blocks.'       )

		if additional_replacements is not None:
			for replacement in additional_replacements:
				A :    Optional[Any]  				=				new_path.replace(replacement['old']		, replacement['new']       )

		if "attentions" in new_path:
			A :    List[Any]  				=				old_checkpoint[path['old']].squeeze()
		else:
			A :    List[str]  				=				old_checkpoint[path['old']]






def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       int       ):
	A :    Optional[Any]  				=				{}

	A :    Tuple  				=				checkpoint['temb.dense.0.weight']
	A :    Optional[int]  				=				checkpoint['temb.dense.0.bias']
	A :    List[Any]  				=				checkpoint['temb.dense.1.weight']
	A :    List[Any]  				=				checkpoint['temb.dense.1.bias']

	A :    Optional[int]  				=				checkpoint['norm_out.weight']
	A :    List[str]  				=				checkpoint['norm_out.bias']

	A :    Any  				=				checkpoint['conv_in.weight']
	A :    Any  				=				checkpoint['conv_in.bias']
	A :    Dict  				=				checkpoint['conv_out.weight']
	A :    List[Any]  				=				checkpoint['conv_out.bias']

	A :    List[str]  				=				len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'down' in layer}       )
	A :    Union[str, Any]  				=				{
	    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(snake_case__       )
	}

	A :    Union[str, Any]  				=				len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'up' in layer}       )
	A :    str  				=				{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(snake_case__       )}

	for i in range(snake_case__       ):
		A :    Optional[Any]  				=				(i - 1) // (config['layers_per_block'] + 1)

		if any('downsample' in layer for layer in down_blocks[i]       ):
			A :    List[str]  				=				checkpoint[
			    F'down.{i}.downsample.op.weight'
			]
			A :    str  				=				checkpoint[F'down.{i}.downsample.op.bias']
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

		if any('block' in layer for layer in down_blocks[i]       ):
			A :    Optional[Any]  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 2       ).split('.'       )[:2]       ) for layer in down_blocks[i] if 'block' in layer}       )
			A :    List[str]  				=				{
			    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
			    for layer_id in range(snake_case__       )
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block']       ):
					A :    List[str]  				=				renew_resnet_paths(blocks[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__       )

		if any('attn' in layer for layer in down_blocks[i]       ):
			A :    Union[str, Any]  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 2       ).split('.'       )[:2]       ) for layer in down_blocks[i] if 'attn' in layer}       )
			A :    Dict  				=				{
			    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
			    for layer_id in range(snake_case__       )
			}

			if num_attn > 0:
				for j in range(config['layers_per_block']       ):
					A :    Tuple  				=				renew_attention_paths(attns[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__		, config=snake_case__       )

	A :    List[Any]  				=				[key for key in checkpoint if 'mid.block_1' in key]
	A :    Tuple  				=				[key for key in checkpoint if 'mid.block_2' in key]
	A :    str  				=				[key for key in checkpoint if 'mid.attn_1' in key]

	# Mid new 2
	A :    Dict  				=				renew_resnet_paths(snake_case__       )
	assign_to_checkpoint(
	    snake_case__		, snake_case__		, snake_case__		, additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]		, )

	A :    int  				=				renew_resnet_paths(snake_case__       )
	assign_to_checkpoint(
	    snake_case__		, snake_case__		, snake_case__		, additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]		, )

	A :    str  				=				renew_attention_paths(snake_case__		, in_mid=snake_case__       )
	assign_to_checkpoint(
	    snake_case__		, snake_case__		, snake_case__		, additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]		, )

	for i in range(snake_case__       ):
		A :    Optional[Any]  				=				num_up_blocks - 1 - i

		if any('upsample' in layer for layer in up_blocks[i]       ):
			A :    Dict  				=				checkpoint[
			    F'up.{i}.upsample.conv.weight'
			]
			A :    int  				=				checkpoint[F'up.{i}.upsample.conv.bias']

		if any('block' in layer for layer in up_blocks[i]       ):
			A :    Any  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 2       ).split('.'       )[:2]       ) for layer in up_blocks[i] if 'block' in layer}       )
			A :    Optional[Any]  				=				{
			    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(snake_case__       )
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block'] + 1       ):
					A :    str  				=				{'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
					A :    Union[str, Any]  				=				renew_resnet_paths(blocks[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__		, additional_replacements=[replace_indices]       )

		if any('attn' in layer for layer in up_blocks[i]       ):
			A :    Any  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 2       ).split('.'       )[:2]       ) for layer in up_blocks[i] if 'attn' in layer}       )
			A :    Dict  				=				{
			    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(snake_case__       )
			}

			if num_attn > 0:
				for j in range(config['layers_per_block'] + 1       ):
					A :    str  				=				{'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
					A :    Dict  				=				renew_attention_paths(attns[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__		, additional_replacements=[replace_indices]       )

	A :    Tuple  				=				{k.replace('mid_new_2'		, 'mid_block'       ): v for k, v in new_checkpoint.items()}
	return new_checkpoint






def  _snake_case			(						snake_case__				:       Optional[int]		, snake_case__				:       Dict       ):
	A :    List[str]  				=				{}

	A :    List[Any]  				=				checkpoint['encoder.norm_out.weight']
	A :    Any  				=				checkpoint['encoder.norm_out.bias']

	A :    Optional[Any]  				=				checkpoint['encoder.conv_in.weight']
	A :    Optional[Any]  				=				checkpoint['encoder.conv_in.bias']
	A :    Tuple  				=				checkpoint['encoder.conv_out.weight']
	A :    Union[str, Any]  				=				checkpoint['encoder.conv_out.bias']

	A :    List[Any]  				=				checkpoint['decoder.norm_out.weight']
	A :    List[str]  				=				checkpoint['decoder.norm_out.bias']

	A :    str  				=				checkpoint['decoder.conv_in.weight']
	A :    Union[str, Any]  				=				checkpoint['decoder.conv_in.bias']
	A :    Dict  				=				checkpoint['decoder.conv_out.weight']
	A :    Optional[int]  				=				checkpoint['decoder.conv_out.bias']

	A :    List[Any]  				=				len({'.'.join(layer.split('.'       )[:3]       ) for layer in checkpoint if 'down' in layer}       )
	A :    Optional[int]  				=				{
	    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(snake_case__       )
	}

	A :    Dict  				=				len({'.'.join(layer.split('.'       )[:3]       ) for layer in checkpoint if 'up' in layer}       )
	A :    Optional[int]  				=				{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(snake_case__       )}

	for i in range(snake_case__       ):
		A :    str  				=				(i - 1) // (config['layers_per_block'] + 1)

		if any('downsample' in layer for layer in down_blocks[i]       ):
			A :    List[str]  				=				checkpoint[
			    F'encoder.down.{i}.downsample.conv.weight'
			]
			A :    Optional[int]  				=				checkpoint[
			    F'encoder.down.{i}.downsample.conv.bias'
			]

		if any('block' in layer for layer in down_blocks[i]       ):
			A :    Any  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 3       ).split('.'       )[:3]       ) for layer in down_blocks[i] if 'block' in layer}       )
			A :    Any  				=				{
			    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
			    for layer_id in range(snake_case__       )
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block']       ):
					A :    int  				=				renew_resnet_paths(blocks[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__       )

		if any('attn' in layer for layer in down_blocks[i]       ):
			A :    List[str]  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 3       ).split('.'       )[:3]       ) for layer in down_blocks[i] if 'attn' in layer}       )
			A :    Tuple  				=				{
			    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
			    for layer_id in range(snake_case__       )
			}

			if num_attn > 0:
				for j in range(config['layers_per_block']       ):
					A :    str  				=				renew_attention_paths(attns[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__		, config=snake_case__       )

	A :    Union[str, Any]  				=				[key for key in checkpoint if 'mid.block_1' in key]
	A :    str  				=				[key for key in checkpoint if 'mid.block_2' in key]
	A :    Optional[Any]  				=				[key for key in checkpoint if 'mid.attn_1' in key]

	# Mid new 2
	A :    Union[str, Any]  				=				renew_resnet_paths(snake_case__       )
	assign_to_checkpoint(
	    snake_case__		, snake_case__		, snake_case__		, additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]		, )

	A :    Tuple  				=				renew_resnet_paths(snake_case__       )
	assign_to_checkpoint(
	    snake_case__		, snake_case__		, snake_case__		, additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]		, )

	A :    List[Any]  				=				renew_attention_paths(snake_case__		, in_mid=snake_case__       )
	assign_to_checkpoint(
	    snake_case__		, snake_case__		, snake_case__		, additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]		, )

	for i in range(snake_case__       ):
		A :    Optional[int]  				=				num_up_blocks - 1 - i

		if any('upsample' in layer for layer in up_blocks[i]       ):
			A :    Dict  				=				checkpoint[
			    F'decoder.up.{i}.upsample.conv.weight'
			]
			A :    List[str]  				=				checkpoint[
			    F'decoder.up.{i}.upsample.conv.bias'
			]

		if any('block' in layer for layer in up_blocks[i]       ):
			A :    Optional[Any]  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 3       ).split('.'       )[:3]       ) for layer in up_blocks[i] if 'block' in layer}       )
			A :    int  				=				{
			    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(snake_case__       )
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block'] + 1       ):
					A :    List[str]  				=				{'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
					A :    Union[str, Any]  				=				renew_resnet_paths(blocks[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__		, additional_replacements=[replace_indices]       )

		if any('attn' in layer for layer in up_blocks[i]       ):
			A :    Optional[int]  				=				len(
			    {'.'.join(shave_segments(snake_case__		, 3       ).split('.'       )[:3]       ) for layer in up_blocks[i] if 'attn' in layer}       )
			A :    List[Any]  				=				{
			    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(snake_case__       )
			}

			if num_attn > 0:
				for j in range(config['layers_per_block'] + 1       ):
					A :    List[str]  				=				{'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
					A :    Optional[int]  				=				renew_attention_paths(attns[j]       )
					assign_to_checkpoint(snake_case__		, snake_case__		, snake_case__		, additional_replacements=[replace_indices]       )

	A :    Any  				=				{k.replace('mid_new_2'		, 'mid_block'       ): v for k, v in new_checkpoint.items()}
	A :    Optional[int]  				=				checkpoint['quant_conv.weight']
	A :    List[str]  				=				checkpoint['quant_conv.bias']
	if "quantize.embedding.weight" in checkpoint:
		A :    List[Any]  				=				checkpoint['quantize.embedding.weight']
	A :    int  				=				checkpoint['post_quant_conv.weight']
	A :    List[str]  				=				checkpoint['post_quant_conv.bias']

	return new_checkpoint


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

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

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

		_lowercase											=			parser.parse_args()
		_lowercase											=			torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				_lowercase											=			json.loads(f.read())

		# unet case
		_lowercase											=			{key.split('''.''')[0] for key in checkpoint.keys()}
		if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
				_lowercase											=			convert_vq_autoenc_checkpoint(checkpoint, config)
		else:
				_lowercase											=			convert_ddpm_checkpoint(checkpoint, config)

		if "ddpm" in config:
				del config["ddpm"]

		if config["_class_name"] == "VQModel":
				_lowercase											=			VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				_lowercase											=			AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				_lowercase											=			UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				_lowercase											=			DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				_lowercase											=			DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)

"""simple docstring"""


from __future__ import annotations






def  _snake_case			(						snake_case__				:       list[list[int]]		, snake_case__				:       list[list[int]]       ):
	assert len(snake_case__       ) == len(snake_case__       )

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	A  				=				[-1] * n  # who the donor has donated to
	A  				=				[-1] * n  # who the recipient has received from
	A  				=				[0] * n

	while unmatched_donors:
		A  				=				unmatched_donors[0]
		A  				=				donor_pref[donor]
		A  				=				donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		A  				=				recipient_pref[recipient]
		A  				=				rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record