



"""simple docstring"""

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A_	:						Tuple				=			{512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}
def    __snake_case       (  __A			:	str    )    ->					List[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       str 							=    F"""pretrained_models/{model_name}"""
				if not os.path.isfile(__A    ):
								os.makedirs('pretrained_models'       ,		exist_ok=__A    )
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
								download_url(__A       ,		'pretrained_models'    )
				SCREAMING_SNAKE_CASE	:       str 							=    torch.load(__A       ,		map_location=lambda __A       ,		__A    : storage    )
				return model
def    __snake_case       (  __A			:	int    )    ->					Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       int 							=    download_model(pretrained_models[args.image_size]    )

				SCREAMING_SNAKE_CASE	:       Any 							=    state_dict["""x_embedder.proj.weight"""]
				SCREAMING_SNAKE_CASE	:       Any 							=    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    ):
								SCREAMING_SNAKE_CASE	:       List[str] 							=    state_dict[
								    """t_embedder.mlp.0.weight"""
								]
								SCREAMING_SNAKE_CASE	:       Any 							=    state_dict[
								    """t_embedder.mlp.0.bias"""
								]
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    state_dict[
								    """t_embedder.mlp.2.weight"""
								]
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    state_dict[
								    """t_embedder.mlp.2.bias"""
								]
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    state_dict[
								    """y_embedder.embedding_table.weight"""
								]

								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    state_dict[
								    F"""blocks.{depth}.adaLN_modulation.1.weight"""
								]
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    state_dict[
								    F"""blocks.{depth}.adaLN_modulation.1.bias"""
								]

								SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]       ,		3       ,		dim=0    )
								SCREAMING_SNAKE_CASE	:       Any 							=    torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]       ,		3       ,		dim=0    )

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

								SCREAMING_SNAKE_CASE	:       Any 							=    state_dict[
								    F"""blocks.{depth}.attn.proj.weight"""
								]
								SCREAMING_SNAKE_CASE	:       str 							=    state_dict[F"""blocks.{depth}.attn.proj.bias"""]

								SCREAMING_SNAKE_CASE	:       List[Any] 							=    state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
								SCREAMING_SNAKE_CASE	:       str 							=    state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
								SCREAMING_SNAKE_CASE	:       int 							=    state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
								SCREAMING_SNAKE_CASE	:       Dict 							=    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'    )

				SCREAMING_SNAKE_CASE	:       int 							=    state_dict["""final_layer.adaLN_modulation.1.weight"""]
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    state_dict["""final_layer.adaLN_modulation.1.bias"""]
				SCREAMING_SNAKE_CASE	:       Dict 							=    state_dict["""final_layer.linear.weight"""]
				SCREAMING_SNAKE_CASE	:       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
				SCREAMING_SNAKE_CASE	:       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=__A       ,		num_attention_heads=16       ,		activation_fn='gelu-approximate'       ,		num_embeds_ada_norm=1000       ,		norm_type='ada_norm_zero'       ,		norm_elementwise_affine=__A       ,		)
				transformer.load_state_dict(__A       ,		strict=__A    )

				SCREAMING_SNAKE_CASE	:       Any 							=    DDIMScheduler(
				    num_train_timesteps=1000       ,		beta_schedule='linear'       ,		prediction_type='epsilon'       ,		clip_sample=__A       ,		)

				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    AutoencoderKL.from_pretrained(args.vae_model    )

				SCREAMING_SNAKE_CASE	:       Dict 							=    DiTPipeline(transformer=__A       ,		vae=__A       ,		scheduler=__A    )

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


if __name__ == "__main__":
							A_	:						int				=			argparse.ArgumentParser()

							parser.add_argument(
							    '--image_size',
							    default=256,
							    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_	:						Any				=			parser.parse_args()
							main(args)










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					def model(_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Any 							=    sample.shape
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
									SCREAMING_SNAKE_CASE	:       List[str] 							=    F.log_softmax(logits.double()    ,					dim=1						).float()
									return return_value

					return model





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Dict:





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





