

'''simple docstring'''

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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

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

def     __magic_name__       ( A							)			->		List[str]:
	snake_case							 =				download_model(pretrained_models[args.image_size]							)

	snake_case							 =				state_dict['x_embedder.proj.weight']
	snake_case							 =				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							):
		snake_case							 =				state_dict[
		    't_embedder.mlp.0.weight'
		]
		snake_case							 =				state_dict[
		    't_embedder.mlp.0.bias'
		]
		snake_case							 =				state_dict[
		    't_embedder.mlp.2.weight'
		]
		snake_case							 =				state_dict[
		    't_embedder.mlp.2.bias'
		]
		snake_case							 =				state_dict[
		    'y_embedder.embedding_table.weight'
		]

		snake_case							 =				state_dict[
		    F'''blocks.{depth}.adaLN_modulation.1.weight'''
		]
		snake_case							 =				state_dict[
		    F'''blocks.{depth}.adaLN_modulation.1.bias'''
		]

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

		snake_case							 =				q
		snake_case							 =				q_bias
		snake_case							 =				k
		snake_case							 =				k_bias
		snake_case							 =				v
		snake_case							 =				v_bias

		snake_case							 =				state_dict[
		    F'''blocks.{depth}.attn.proj.weight'''
		]
		snake_case							 =				state_dict[F'''blocks.{depth}.attn.proj.bias''']

		snake_case							 =				state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
		snake_case							 =				state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
		snake_case							 =				state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
		snake_case							 =				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'							)

	snake_case							 =				state_dict['final_layer.adaLN_modulation.1.weight']
	snake_case							 =				state_dict['final_layer.adaLN_modulation.1.bias']
	snake_case							 =				state_dict['final_layer.linear.weight']
	snake_case							 =				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
	snake_case							 =				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=A					,   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=A					,   )
	transformer.load_state_dict(A					,   strict=A							)

	snake_case							 =				DDIMScheduler(
	    num_train_timesteps=1_0_0_0					,   beta_schedule='linear'					,   prediction_type='epsilon'					,   clip_sample=A					,   )

	snake_case							 =				AutoencoderKL.from_pretrained(args.vae_model							)

	snake_case							 =				DiTPipeline(transformer=A					,   vae=A					,   scheduler=A							)

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


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

						lowerCAmelCase_						   =			parser.parse_args()
						main(args)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

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





