

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        lowerCamelCase_  (				UpperCAmelCase_				:  List[Any]						)    ->					List[str]:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]						)    ->					Tuple:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    download_model(pretrained_models[args.image_size]						)

	_UpperCamelCase :		List[Any]								=    state_dict['x_embedder.proj.weight']
	_UpperCamelCase :		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(2_8						):
		_UpperCamelCase :		Optional[Any]								=    state_dict[
		    't_embedder.mlp.0.weight'
		]
		_UpperCamelCase :		Union[str, Any]								=    state_dict[
		    't_embedder.mlp.0.bias'
		]
		_UpperCamelCase :		str								=    state_dict[
		    't_embedder.mlp.2.weight'
		]
		_UpperCamelCase :		str								=    state_dict[
		    't_embedder.mlp.2.bias'
		]
		_UpperCamelCase :		str								=    state_dict[
		    'y_embedder.embedding_table.weight'
		]

		_UpperCamelCase :		Optional[Any]								=    state_dict[
		    F'''blocks.{depth}.adaLN_modulation.1.weight'''
		]
		_UpperCamelCase :		Union[str, 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 :		List[str]								=    torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']				,							3				,							dim=0						)

		_UpperCamelCase :		List[str]								=    q
		_UpperCamelCase :		List[Any]								=    q_bias
		_UpperCamelCase :		int								=    k
		_UpperCamelCase :		Optional[Any]								=    k_bias
		_UpperCamelCase :		List[str]								=    v
		_UpperCamelCase :		str								=    v_bias

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

		_UpperCamelCase :		Tuple								=    state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
		_UpperCamelCase :		Dict								=    state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
		_UpperCamelCase :		List[str]								=    state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
		_UpperCamelCase :		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'						)

	_UpperCamelCase :		str								=    state_dict['final_layer.adaLN_modulation.1.weight']
	_UpperCamelCase :		Optional[Any]								=    state_dict['final_layer.adaLN_modulation.1.bias']
	_UpperCamelCase :		Union[str, Any]								=    state_dict['final_layer.linear.weight']
	_UpperCamelCase :		Union[str, 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 :		Optional[int]								=    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=UpperCAmelCase_				,							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=UpperCAmelCase_				,							)
	transformer.load_state_dict(UpperCAmelCase_				,							strict=UpperCAmelCase_						)

	_UpperCamelCase :		str								=    DDIMScheduler(
	    num_train_timesteps=1_0_0_0				,							beta_schedule='linear'				,							prediction_type='epsilon'				,							clip_sample=UpperCAmelCase_				,							)

	_UpperCamelCase :		List[Any]								=    AutoencoderKL.from_pretrained(args.vae_model						)

	_UpperCamelCase :		Tuple								=    DiTPipeline(transformer=UpperCAmelCase_				,							vae=UpperCAmelCase_				,							scheduler=UpperCAmelCase_						)

	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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__             =		{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)




