
"""simple docstring"""


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_lowercase											=			{5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}






def  _snake_case			(						snake_case__				:       List[Any]       ):
	A :    Optional[Any]  				=				F'pretrained_models/{model_name}'
	if not os.path.isfile(_lowerCamelCase       ):
		os.makedirs('pretrained_models'		, exist_ok=_lowerCamelCase       )
		A :    List[str]  				=				F'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
		download_url(_lowerCamelCase		, 'pretrained_models'       )
	A :    Optional[int]  				=				torch.load(_lowerCamelCase		, map_location=lambda snake_case__		, snake_case__       : storage       )
	return model






def  _snake_case			(						snake_case__				:       int       ):
	A :    str  				=				download_model(pretrained_models[args.image_size]       )

	A :    Optional[Any]  				=				state_dict['x_embedder.proj.weight']
	A :    Optional[int]  				=				state_dict['x_embedder.proj.bias']
	state_dict.pop('x_embedder.proj.weight'       )
	state_dict.pop('x_embedder.proj.bias'       )

	for depth in range(28       ):
		A :    int  				=				state_dict[
		    't_embedder.mlp.0.weight'
		]
		A :    Any  				=				state_dict[
		    't_embedder.mlp.0.bias'
		]
		A :    str  				=				state_dict[
		    't_embedder.mlp.2.weight'
		]
		A :    str  				=				state_dict[
		    't_embedder.mlp.2.bias'
		]
		A :    str  				=				state_dict[
		    'y_embedder.embedding_table.weight'
		]

		A :    List[str]  				=				state_dict[
		    F'blocks.{depth}.adaLN_modulation.1.weight'
		]
		A :    Union[str, Any]  				=				state_dict[
		    F'blocks.{depth}.adaLN_modulation.1.bias'
		]

		A				, A				, A :    int  				=				torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.weight']		, 3		, dim=0       )
		A				, A				, A :    int  				=				torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.bias']		, 3		, dim=0       )

		A :    Tuple  				=				q
		A :    Union[str, Any]  				=				q_bias
		A :    str  				=				k
		A :    Optional[Any]  				=				k_bias
		A :    Union[str, Any]  				=				v
		A :    Any  				=				v_bias

		A :    List[Any]  				=				state_dict[
		    F'blocks.{depth}.attn.proj.weight'
		]
		A :    Any  				=				state_dict[F'blocks.{depth}.attn.proj.bias']

		A :    int  				=				state_dict[F'blocks.{depth}.mlp.fc1.weight']
		A :    Union[str, Any]  				=				state_dict[F'blocks.{depth}.mlp.fc1.bias']
		A :    Optional[Any]  				=				state_dict[F'blocks.{depth}.mlp.fc2.weight']
		A :    List[Any]  				=				state_dict[F'blocks.{depth}.mlp.fc2.bias']

		state_dict.pop(F'blocks.{depth}.attn.qkv.weight'       )
		state_dict.pop(F'blocks.{depth}.attn.qkv.bias'       )
		state_dict.pop(F'blocks.{depth}.attn.proj.weight'       )
		state_dict.pop(F'blocks.{depth}.attn.proj.bias'       )
		state_dict.pop(F'blocks.{depth}.mlp.fc1.weight'       )
		state_dict.pop(F'blocks.{depth}.mlp.fc1.bias'       )
		state_dict.pop(F'blocks.{depth}.mlp.fc2.weight'       )
		state_dict.pop(F'blocks.{depth}.mlp.fc2.bias'       )
		state_dict.pop(F'blocks.{depth}.adaLN_modulation.1.weight'       )
		state_dict.pop(F'blocks.{depth}.adaLN_modulation.1.bias'       )

	state_dict.pop('t_embedder.mlp.0.weight'       )
	state_dict.pop('t_embedder.mlp.0.bias'       )
	state_dict.pop('t_embedder.mlp.2.weight'       )
	state_dict.pop('t_embedder.mlp.2.bias'       )
	state_dict.pop('y_embedder.embedding_table.weight'       )

	A :    int  				=				state_dict['final_layer.adaLN_modulation.1.weight']
	A :    Optional[Any]  				=				state_dict['final_layer.adaLN_modulation.1.bias']
	A :    int  				=				state_dict['final_layer.linear.weight']
	A :    Dict  				=				state_dict['final_layer.linear.bias']

	state_dict.pop('final_layer.linear.weight'       )
	state_dict.pop('final_layer.linear.bias'       )
	state_dict.pop('final_layer.adaLN_modulation.1.weight'       )
	state_dict.pop('final_layer.adaLN_modulation.1.bias'       )

	# DiT XL/2
	A :    List[str]  				=				TransformeraDModel(
	    sample_size=args.image_size // 8		, num_layers=28		, attention_head_dim=72		, in_channels=4		, out_channels=8		, patch_size=2		, attention_bias=_lowerCamelCase		, num_attention_heads=16		, activation_fn='gelu-approximate'		, num_embeds_ada_norm=1000		, norm_type='ada_norm_zero'		, norm_elementwise_affine=_lowerCamelCase		, )
	transformer.load_state_dict(_lowerCamelCase		, strict=_lowerCamelCase       )

	A :    Union[str, Any]  				=				DDIMScheduler(
	    num_train_timesteps=1000		, beta_schedule='linear'		, prediction_type='epsilon'		, clip_sample=_lowerCamelCase		, )

	A :    List[str]  				=				AutoencoderKL.from_pretrained(args.vae_model       )

	A :    Any  				=				DiTPipeline(transformer=_lowerCamelCase		, vae=_lowerCamelCase		, scheduler=_lowerCamelCase       )

	if args.save:
		pipeline.save_pretrained(args.checkpoint_path       )


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

		parser.add_argument(
		    '''--image_size''',
		    default=2_56,
		    type=int,
		    required=False,
		    help='''Image size of pretrained model, either 256 or 512.''',
		)
		parser.add_argument(
		    '''--vae_model''',
		    default='''stabilityai/sd-vae-ft-ema''',
		    type=str,
		    required=False,
		    help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
		)
		parser.add_argument(
		    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
		)
		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
		)

		_lowercase											=			parser.parse_args()
		main(args)

"""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