






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A          = {512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       f"""pretrained_models/{model_name}"""
					if not os.path.isfile(_lowercase       ):
										os.makedirs('''pretrained_models'''    ,	exist_ok=_lowercase       )
										_A									=       f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
										download_url(_lowercase    ,	'''pretrained_models'''       )
					_A									=       torch.load(_lowercase    ,	map_location=lambda _lowercase    ,	_lowercase       : storage       )
					return model




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       download_model(pretrained_models[args.image_size]       )

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

										_A									=       state_dict[
										    f"""blocks.{depth}.adaLN_modulation.1.weight"""
										]
										_A									=       state_dict[
										    f"""blocks.{depth}.adaLN_modulation.1.bias"""
										]

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

										_A									=       q
										_A									=       q_bias
										_A									=       k
										_A									=       k_bias
										_A									=       v
										_A									=       v_bias

										_A									=       state_dict[
										    f"""blocks.{depth}.attn.proj.weight"""
										]
										_A									=       state_dict[f"""blocks.{depth}.attn.proj.bias"""]

										_A									=       state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
										_A									=       state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
										_A									=       state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
										_A									=       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'''       )

					_A									=       state_dict['''final_layer.adaLN_modulation.1.weight''']
					_A									=       state_dict['''final_layer.adaLN_modulation.1.bias''']
					_A									=       state_dict['''final_layer.linear.weight''']
					_A									=       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
					_A									=       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=_lowercase    ,	num_attention_heads=16    ,	activation_fn='''gelu-approximate'''    ,	num_embeds_ada_norm=10_00    ,	norm_type='''ada_norm_zero'''    ,	norm_elementwise_affine=_lowercase    ,	)
					transformer.load_state_dict(_lowercase    ,	strict=_lowercase       )

					_A									=       DDIMScheduler(
					    num_train_timesteps=10_00    ,	beta_schedule='''linear'''    ,	prediction_type='''epsilon'''    ,	clip_sample=_lowercase    ,	)

					_A									=       AutoencoderKL.from_pretrained(args.vae_model       )

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

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


if __name__ == "__main__":
				__A          = 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          = parser.parse_args()
				main(args)





"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	RePaintPipeline
	lowercase_        =	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	lowercase_        =	PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	lowercase_        =	IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			lowerCamelCase     :			Optional[Any]		=    UNetaDModel(
			    block_out_channels=(32, 64)		, layers_per_block=2		, sample_size=32		, in_channels=3		, out_channels=3		, down_block_types=('DownBlock2D', 'AttnDownBlock2D')		, up_block_types=('AttnUpBlock2D', 'UpBlock2D')		, )
			lowerCamelCase     :			Union[str, Any]		=    RePaintScheduler()
			lowerCamelCase     :			Dict		=    {'unet': unet, 'scheduler': scheduler}
			return components




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=0					)		->							Optional[int]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			str		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Tuple		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			lowerCamelCase     :			str		=    np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			lowerCamelCase     :			Tuple		=    torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[int]		=    (image > 0).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[Any]		=    {
			    'image': image,
			    'mask_image': mask,
			    'generator': generator,
			    'num_inference_steps': 5,
			    'eta': 0.0,
			    'jump_length': 2,
			    'jump_n_sample': 2,
			    'output_type': 'numpy',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Any		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			Union[str, Any]		=    self.get_dummy_components()
			lowerCamelCase     :			List[Any]		=    RePaintPipeline(**UpperCAmelCase_					)
			lowerCamelCase     :			int		=    sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    self.get_dummy_inputs(UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    sd_pipe(**UpperCAmelCase_					).images
			lowerCamelCase     :			List[Any]		=    image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase     :			Optional[Any]		=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return super().test_save_load_local()




	@unittest.skip('non-deterministic pipeline'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_inference_batch_single_identical()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			return super().test_save_load_optional_components()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class        _lowercase   (				unittest.TestCase		):




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256.png'					)
			lowerCamelCase     :			str		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'					)
			lowerCamelCase     :			Optional[Any]		=    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256_result.npy'					)

			lowerCamelCase     :			Any		=    'google/ddpm-ema-celebahq-256'
			lowerCamelCase     :			List[Any]		=    UNetaDModel.from_pretrained(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    RePaintPipeline(unet=UpperCAmelCase_		, scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)
			lowerCamelCase     :			str		=    repaint(
			    UpperCAmelCase_		, UpperCAmelCase_		, num_inference_steps=250		, eta=0.0		, jump_length=10		, jump_n_sample=10		, generator=UpperCAmelCase_		, output_type='np'		, )
			lowerCamelCase     :			Optional[int]		=    output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2






