



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A             ={5_1_2: "DiT-XL-2-512x512.pt", 2_5_6: "DiT-XL-2-256x256.pt"}

def 	a						(				_UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     f'pretrained_models/{model_name}'
		if not os.path.isfile(_UpperCAmelCase						):
				os.makedirs('''pretrained_models'''			,       exist_ok=_UpperCAmelCase						)
				__UpperCAmelCase   :					List[Any]													=     f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
				download_url(_UpperCAmelCase			,       '''pretrained_models'''						)
		__UpperCAmelCase   :					Union[str, Any]													=     torch.load(_UpperCAmelCase			,       map_location=lambda _UpperCAmelCase			,       _UpperCAmelCase						: storage						)
		return model

def 	a						(				_UpperCAmelCase							: Tuple						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[int]													=     download_model(pretrained_models[args.image_size]						)

		__UpperCAmelCase   :					Optional[int]													=     state_dict['''x_embedder.proj.weight''']
		__UpperCAmelCase   :					Tuple													=     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						):
				__UpperCAmelCase   :					List[str]													=     state_dict[
				    '''t_embedder.mlp.0.weight'''
				]
				__UpperCAmelCase   :					Any													=     state_dict[
				    '''t_embedder.mlp.0.bias'''
				]
				__UpperCAmelCase   :					Optional[int]													=     state_dict[
				    '''t_embedder.mlp.2.weight'''
				]
				__UpperCAmelCase   :					List[Any]													=     state_dict[
				    '''t_embedder.mlp.2.bias'''
				]
				__UpperCAmelCase   :					Union[str, Any]													=     state_dict[
				    '''y_embedder.embedding_table.weight'''
				]

				__UpperCAmelCase   :					List[str]													=     state_dict[
				    f'blocks.{depth}.adaLN_modulation.1.weight'
				]
				__UpperCAmelCase   :					Optional[Any]													=     state_dict[
				    f'blocks.{depth}.adaLN_modulation.1.bias'
				]

				__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					Dict													=     torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']			,       3			,       dim=0						)
				__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					str													=     torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']			,       3			,       dim=0						)

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

				__UpperCAmelCase   :					Dict													=     state_dict[
				    f'blocks.{depth}.attn.proj.weight'
				]
				__UpperCAmelCase   :					List[str]													=     state_dict[f'blocks.{depth}.attn.proj.bias']

				__UpperCAmelCase   :					Any													=     state_dict[f'blocks.{depth}.mlp.fc1.weight']
				__UpperCAmelCase   :					Any													=     state_dict[f'blocks.{depth}.mlp.fc1.bias']
				__UpperCAmelCase   :					List[str]													=     state_dict[f'blocks.{depth}.mlp.fc2.weight']
				__UpperCAmelCase   :					str													=     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'''						)

		__UpperCAmelCase   :					Optional[int]													=     state_dict['''final_layer.adaLN_modulation.1.weight''']
		__UpperCAmelCase   :					str													=     state_dict['''final_layer.adaLN_modulation.1.bias''']
		__UpperCAmelCase   :					str													=     state_dict['''final_layer.linear.weight''']
		__UpperCAmelCase   :					Optional[Any]													=     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
		__UpperCAmelCase   :					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=_UpperCAmelCase			,       num_attention_heads=16			,       activation_fn='''gelu-approximate'''			,       num_embeds_ada_norm=10_00			,       norm_type='''ada_norm_zero'''			,       norm_elementwise_affine=_UpperCAmelCase			,       )
		transformer.load_state_dict(_UpperCAmelCase			,       strict=_UpperCAmelCase						)

		__UpperCAmelCase   :					Dict													=     DDIMScheduler(
		    num_train_timesteps=10_00			,       beta_schedule='''linear'''			,       prediction_type='''epsilon'''			,       clip_sample=_UpperCAmelCase			,       )

		__UpperCAmelCase   :					Optional[Any]													=     AutoencoderKL.from_pretrained(args.vae_model						)

		__UpperCAmelCase   :					str													=     DiTPipeline(transformer=_UpperCAmelCase			,       vae=_UpperCAmelCase			,       scheduler=_UpperCAmelCase						)

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


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

				parser.add_argument(
				    "--image_size",
				    default=2_5_6,
				    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."
				)

				__A             =parser.parse_args()
				main(args)










import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			int	,	a_							:			str	,	a_							:			List[str]=sys.maxsize			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     '''bilinear'''
					__UpperCAmelCase   :					str													=     max_size
					__UpperCAmelCase   :					Dict													=     short_edge_length




			def __call__(       self							:			str	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     []
					for img in imgs:
							__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     img.shape[:2]
							# later: provide list and randomly choose index for resize
							__UpperCAmelCase   :					Optional[Any]													=     np.random.randint(self.short_edge_length[0]	,	self.short_edge_length[1] + 1			)
							if size == 0:
									return img
							__UpperCAmelCase   :					Optional[Any]													=     size * 1.0 / min(a_	,	a_			)
							if h < w:
									__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     size, scale * w
							else:
									__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     scale * h, size
							if max(a_	,	a_			) > self.max_size:
									__UpperCAmelCase   :					Union[str, Any]													=     self.max_size * 1.0 / max(a_	,	a_			)
									__UpperCAmelCase   :					int													=     newh * scale
									__UpperCAmelCase   :					Union[str, Any]													=     neww * scale
							__UpperCAmelCase   :					Any													=     int(neww + 0.5			)
							__UpperCAmelCase   :					Optional[int]													=     int(newh + 0.5			)

							if img.dtype == np.uinta:
									__UpperCAmelCase   :					Any													=     Image.fromarray(a_			)
									__UpperCAmelCase   :					Any													=     pil_image.resize((neww, newh)	,	PILImageResampling.BILINEAR			)
									__UpperCAmelCase   :					List[Any]													=     np.asarray(a_			)
							else:
									__UpperCAmelCase   :					Union[str, Any]													=     img.permute(2	,	0	,	1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
									__UpperCAmelCase   :					Optional[int]													=     nn.functional.interpolate(
									    a_	,	(newh, neww)	,	mode=self.interp_method	,	align_corners=a_			).squeeze(0			)
							img_augs.append(a_			)

					return img_augs

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,	cfg.INPUT.MAX_SIZE_TEST			)
					__UpperCAmelCase   :					List[str]													=     cfg.INPUT.FORMAT
					__UpperCAmelCase   :					int													=     cfg.SIZE_DIVISIBILITY
					__UpperCAmelCase   :					int													=     cfg.PAD_VALUE
					__UpperCAmelCase   :					Any													=     cfg.INPUT.MAX_SIZE_TEST
					__UpperCAmelCase   :					Tuple													=     cfg.MODEL.DEVICE
					__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					List[Any]													=     torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					Union[str, Any]													=     lambda a_			: (x - self.pixel_mean) / self.pixel_std




			def 			snake_case__					(       self							:			int	,	a_							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     tuple(max(a_			) for s in zip(*[img.shape for img in images]			)			)
					__UpperCAmelCase   :					Any													=     [im.shape[-2:] for im in images]
					__UpperCAmelCase   :					Union[str, Any]													=     [
					    nn.functional.pad(
					        a_	,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]	,	value=self.pad_value	,	)
					    for size, im in zip(a_	,	a_			)
					]

					return torch.stack(a_			), torch.tensor(a_			)




			def __call__(       self							:			List[str]	,	a_							:			int	,	a_							:			int=False			):
					'''simple docstring'''





					with torch.no_grad():
							if not isinstance(a_	,	a_			):
									__UpperCAmelCase   :					Optional[Any]													=     [images]
							if single_image:
									assert len(a_			) == 1
							for i in range(len(a_			)			):
									if isinstance(images[i]	,	torch.Tensor			):
											images.insert(a_	,	images.pop(a_			).to(self.device			).float()			)
									elif not isinstance(images[i]	,	torch.Tensor			):
											images.insert(
											    a_	,	torch.as_tensor(img_tensorize(images.pop(a_			)	,	input_format=self.input_format			)			)
											    .to(self.device			)
											    .float()	,	)
            # resize smallest edge
							__UpperCAmelCase   :					Optional[Any]													=     torch.tensor([im.shape[:2] for im in images]			)
							__UpperCAmelCase   :					Dict													=     self.aug(a_			)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							__UpperCAmelCase   :					Optional[int]													=     [self.normalizer(a_			) for x in images]
							# now pad them to do the following operations
							__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     self.pad(a_			)
							# Normalize

							if self.size_divisibility > 0:
									raise NotImplementedError()
							# pad
							__UpperCAmelCase   :					str													=     torch.true_divide(a_	,	a_			)
							if single_image:
									return images[0], sizes[0], scales_yx[0]
							else:
									return images, sizes, scales_yx

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple[int, int]						):







		'''simple docstring'''





		assert torch.isfinite(_UpperCAmelCase						).all(), "Box tensor contains infinite or NaN!"
		__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     box_size
		tensor[:, 0].clamp_(min=0			,       max=_UpperCAmelCase						)
		tensor[:, 1].clamp_(min=0			,       max=_UpperCAmelCase						)
		tensor[:, 2].clamp_(min=0			,       max=_UpperCAmelCase						)
		tensor[:, 3].clamp_(min=0			,       max=_UpperCAmelCase						)





