





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowerCAmelCase 	=      {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}

def      a    (   _lowercase :  Tuple							)  ->Any:
		UpperCamelCase__								=						F"""pretrained_models/{model_name}"""
		if not os.path.isfile(_lowercase							):
				os.makedirs("""pretrained_models"""			,    exist_ok=_lowercase							)
				UpperCamelCase__								=						F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
				download_url(_lowercase			,    """pretrained_models"""							)
		UpperCamelCase__								=						torch.load(_lowercase			,    map_location=lambda _lowercase			,    _lowercase							: storage							)
		return model

def      a    (   _lowercase :  int							)  ->int:
		UpperCamelCase__								=						download_model(pretrained_models[args.image_size]							)

		UpperCamelCase__								=						state_dict["""x_embedder.proj.weight"""]
		UpperCamelCase__								=						state_dict["""x_embedder.proj.bias"""]
		state_dict.pop("""x_embedder.proj.weight"""							)
		state_dict.pop("""x_embedder.proj.bias"""							)

		for depth in range(2_8							):
				UpperCamelCase__								=						state_dict[
				    """t_embedder.mlp.0.weight"""
				]
				UpperCamelCase__								=						state_dict[
				    """t_embedder.mlp.0.bias"""
				]
				UpperCamelCase__								=						state_dict[
				    """t_embedder.mlp.2.weight"""
				]
				UpperCamelCase__								=						state_dict[
				    """t_embedder.mlp.2.bias"""
				]
				UpperCamelCase__								=						state_dict[
				    """y_embedder.embedding_table.weight"""
				]

				UpperCamelCase__								=						state_dict[
				    F"""blocks.{depth}.adaLN_modulation.1.weight"""
				]
				UpperCamelCase__								=						state_dict[
				    F"""blocks.{depth}.adaLN_modulation.1.bias"""
				]

				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]			,    3			,    dim=0							)
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]			,    3			,    dim=0							)

				UpperCamelCase__								=						q
				UpperCamelCase__								=						q_bias
				UpperCamelCase__								=						k
				UpperCamelCase__								=						k_bias
				UpperCamelCase__								=						v
				UpperCamelCase__								=						v_bias

				UpperCamelCase__								=						state_dict[
				    F"""blocks.{depth}.attn.proj.weight"""
				]
				UpperCamelCase__								=						state_dict[F"""blocks.{depth}.attn.proj.bias"""]

				UpperCamelCase__								=						state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
				UpperCamelCase__								=						state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
				UpperCamelCase__								=						state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
				UpperCamelCase__								=						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__								=						state_dict["""final_layer.adaLN_modulation.1.weight"""]
		UpperCamelCase__								=						state_dict["""final_layer.adaLN_modulation.1.bias"""]
		UpperCamelCase__								=						state_dict["""final_layer.linear.weight"""]
		UpperCamelCase__								=						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__								=						TransformeraDModel(
		    sample_size=args.image_size // 8			,    num_layers=2_8			,    attention_head_dim=7_2			,    in_channels=4			,    out_channels=8			,    patch_size=2			,    attention_bias=_lowercase			,    num_attention_heads=1_6			,    activation_fn="""gelu-approximate"""			,    num_embeds_ada_norm=1_0_0_0			,    norm_type="""ada_norm_zero"""			,    norm_elementwise_affine=_lowercase			,    )
		transformer.load_state_dict(_lowercase			,    strict=_lowercase							)

		UpperCamelCase__								=						DDIMScheduler(
		    num_train_timesteps=1_0_0_0			,    beta_schedule="""linear"""			,    prediction_type="""epsilon"""			,    clip_sample=_lowercase			,    )

		UpperCamelCase__								=						AutoencoderKL.from_pretrained(args.vae_model							)

		UpperCamelCase__								=						DiTPipeline(transformer=_lowercase			,    vae=_lowercase			,    scheduler=_lowercase							)

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


if __name__ == "__main__":
				__lowerCAmelCase 	=      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."
				)

				__lowerCAmelCase 	=      parser.parse_args()
				main(args)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase 	=      {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase 	=      [
				    "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VisualBertForMultipleChoice",
				    "VisualBertForPreTraining",
				    "VisualBertForQuestionAnswering",
				    "VisualBertForRegionToPhraseAlignment",
				    "VisualBertForVisualReasoning",
				    "VisualBertLayer",
				    "VisualBertModel",
				    "VisualBertPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_visual_bert import (
								    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VisualBertForMultipleChoice,
								    VisualBertForPreTraining,
								    VisualBertForQuestionAnswering,
								    VisualBertForRegionToPhraseAlignment,
								    VisualBertForVisualReasoning,
								    VisualBertLayer,
								    VisualBertModel,
								    VisualBertPreTrainedModel,
								)


else:
				import sys

				__lowerCAmelCase 	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





